THE IMPACT OF INPO ON PLANT PERFORMANCE AND
OPERATIONS
by
Humera Khan
S.B. Nuclear Engineering (1995)
S.B. Art and Design (1996)
Massachusetts Institute of Technology
Submitted to the Department of Nuclear Engineering
& Technology and Policy Program
in partial fulfillment of the requirements for the degrees of
Master of Science in Nuclear Engineering
and
Master of Science in Technology and Policy
at the
MASSACHUSETS INS'TIE OF TECHNOLoGY
1998
0 Humera.Jhan. All rights reserved. The author hereby grants to MIT
permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part
Author ..........
Department of Nuclear Engineering
and
Technology and Policy Program
May 18, 1998
Certified by.......
Kent F. Hansen
Professor
Thesis Supervisor
Certified by..............
..e"-,L
Nelson Repenning
Assistant Professor
Thesis Supervisor
Accepted by...........
Lawrence Lidsky
ents
nt Committee on Graau'at
Accepted by..... .w
..
Richard D. Tabors
Technology and Policy Program
S~rny
THE IMPACT OF INPO ON PLANT PERFORMANCE AND
OPERATIONS
by
Humera Khan
Submitted to the Department of Nuclear Engineering
& Technology and Policy Program
in partial fulfillment of the requirements for the degrees of
Master of Science in Nuclear Engineering and
Master of Science in Technology and Policy
ABSTRACT
The goal of this research was to understand the effect that a voluntary regulatory
body, the Institute of Nuclear Power Operations (INPO), has on the nuclear
industry. Understanding this information exchange sector is a step towards
understanding the dynamics of reform in a high risk industry. This was done
through creating a model of the information flow through the industry and
analyzing the results. This model shows how INPO interacts with the nuclear
industry, and how they run their own organization. Once this model had been
created, it was linked to an existing model of a nuclear power utility. This updated
model gave a more accurate depiction of the learning effect that INPO has on
the utility's performance and operations. The program used to create these
computer simulation models was VENSIM, a systems dynamics utility by
Ventana Systems.
Thesis Supervisor: Kent Hansen
Title: Professor, Department of Nuclear Engineering
Thesis Supervisor : Nelson Repenning
Title: Assistant Professor, Sloan School of Management
Thesis Supervisor: Richard Tabors
Title: Senior Lecturer, Technology and Policy Program
TABLE OF CONTENTS
Introduction ..........................................................................................
8
Historical Background of the Nuclear Industry ....................................................... 12
2.1 Introduction ...........................................................................................................
2.2 Performance and Public Response .......................................
12
........... 12
2.2.1 Regulations and Regulators Before Three Mile Island .........................
13
2.2.2 Regulations and Regulators After Three Mile Island...........................
14
The Institute of Nuclear Power Operations ......................................
3.1 Introduction ......................................................................
....... 16
.............................. 16
3.2 Conception and Need ............................................................
........................ 16
3.3 Information Collection.................................................................................
18
3.3.1 Event Notification ..........................................................................
18
3.3.2 SEE-IN (Significant Event Evaluation-Information Network) ..........
19
3.3.3 Inspections .............................................................................................. 22
3.4 Impact on the Industry ...........................................................
....................... 23
3.4.1 Past ................................................................................................................
23
3.4.2 Present .......................................................................................................... 25
3.4.3 Future ......................................................................
............................... 25
Introduction to System Dynamics ....................................
............
27
4.1 Introduction ........................................................................................................... 27
4.2 Systems Thinking ...............................................................................................
4.3 Systems Dynamics Tools .................................................
4.3.1 Reinforcing Loops .....................................
......
........
27
......... 29
................. 29
4.3.2 Balancing Loops ............................................................
........................ 31
4.3.3 Combining Loops .........................................................
........................ 33
4.3.4 Simulations ................................................................................................... 34
4.4 Policy Implications ..........................................................................................
34
Introduction to the Plant Operation Model .......................................................... 36
5.1 Introduction .......................................................................
.............................. 36
5.2 Nuclear Plant Operation Model (NPPMS) ......................................................
5.2.1 Defect Generation and Correction ......................................
36
....... 37
5.2.2 Engineers, Operators and Maintenance Crew.......................................... 38
5.3 INPO Information Flow Model (INPO sector) ......................................
39
5.4 Link to Plant Operation Model...............................
45
.......
.
.......
5.4.1 Defect Detection....................................................................................
46
5.4.2 Defect Correction .................................................................................. 47
Simulations, Analysis and Conclusions .......................................
6.1 Introduction .......................................................................
49
.............................. 49
6.2 Simulation Results: Combined Model .......................................
........49
6.2.1 B ase Run .......................................................................................................
49
6.2.2 Merits of INPO...........................................................................................
58
6.2.3 Maintenance Downsize ....................................................................
59
6.3 C onclusions .......................................................................
............................... 68
Appendix A ................................... ......................................................................... 70
Appen dix B ...................................................................................
Bibliography
.........................................
.......................... 80
85
LIST OF FIGURES
Page
Number
FIG 3-1 SCHEMATIC OF INFORMATION FLOW THROUGH INPO...........
.......
21
FIG 4-1: STOCK AND FLOW REPRESENTATION......................................................
29
FIG 4-2: GRAPH OF REINFORCING BEHAVIOR ...................................................... 30
FIG 4-3: REINFORCING LOOP...............................................................................
30
FIG 4-4: GRAPH OF BALANCING BEHAVIOR............................................. 32
FIG 4-5: BALANCING Loo......................................................................................... 32
FIG 4-6: S-SHAPED CURVE ............................................................
33
................................ 34
FIG 4-7: COMBINING LOOPS..........................................
FIG 5-1 DEFECT DISCOVERY AND CORRECTION..............................
FIG 5-2 WORK BACKLOG...................................................
........ 40
.............
41
FIG 5-3 FLOW THROUGH INPO ........................................................................... 41
FIG 5-4 INSPECTION EFFECTIVENESS ........................................
........
43
FIG 5-5 EFFECT OF SENS GENERATED ON INSPECTION EFFECTIVENESS .................. 43
FIG 5-6 EFFECT OF SOERs GENERATED ON INSPECTION EFFECTIVENESS ............ 44
FIG 5-7 EFFECT OF INSPECTION EFFECTIVENESS ON UTILrTY..............................
44
FIG 5-8 EFFECT OF INPO (DEFECT DISCOVERY) ................................................
46
FIG 5-9 EFFECT OF INPO (ENGINEERING) ......................................
.........
47
FIG 5-10 EFFECT ON ENGINEERING (PLANNING) .................................................
48
FIG 6-1: DEFECT GENERATION RATE (OTHER) .............................................
50
FIG 6-2: P3 STAFF .......................................................................................
51
FIG 6-3: TP STAFF .....................................................
.................................. 52
FIG 6-4: SUPPORT STAFF.........................................................
52
FIG 6-5: DEFECT DISCOVERY RATE (OTHER)......................................................
53
FIG 6-6: PLANNING STAFF................................................................................. 54
FIG 6-7: TOTAL DEFECTS .......................................................
........................... 55
FIG 6-8: RATE OF EQUIPMENT BREAKDOWN ......................................................
56
FIG6-9: P3 BACKLOG ...............................................................
57
FIG6-10: EXPECTED CAPACITY LOSS ................................................................ 58
FIG 6-11 DEFECT GENERATION RATE ...........................................
FIG 6-12 EQUIPMENT BREAKDOWN RATE .........................................
5
.........
60
........
61
FIG 6-13 TOTAL DEFECTS ..................................................................
62
FIG 6-14 DEFECT DISCOVERY RATE.................................................
63
.. 64
......... ......................
FIG 6-15 WORK BACKLOG ......................
65
FIG6-16 P3 PLANNING STAFF.......................................
..................
FIG 6-17 P3 ACTION STAFF....................................
.................... 66
FIG 6-18 TOOL POUCH ACTION STAFF ...........................
........................
66
FIG 6-19 SUPPORT ACTION STAFF .....................................................
67
FIG 6-20 EXPECTED CAPACITY LOSS ........................................
68
6
ACKNOWLEDGMENTS
The author wishes to thank Professors Hansen and Repenning, and Dr.
Tabors for their patience and help in seeing me through with this thesis.
I would not have been able to do it with anyone less understanding and
supporting than them. Their trust in me has provided me with the
chance to graduate on time, which had indeed seemed very bleak on
more than one occasion. I am also very grateful to Professor Lidsky for
backing me up and having faith in me.
Clare, Linda and Gail were wonderful in keeping me going, and in
making sure that I actually graduate. Their reminders about all the
deadlines that I missed made sure that I did not stop working till the last
day, last time...
The love and support of my mother and sister-in-law made life a lot
more cheerful than it otherwise would have been. Hugs and french
vanilla saved the day.
Some other people who truly deserve thanks include my pal Nadia. A
heartfelt 15MB thank you for all the help, support and blinkful sessions.
They were a sanity check in an otherwise chaotic world. Alhumdulillah I
am done!
Chapter I
INTRODUCTION
A mere 15 years ago the US nuclear utilities were known to be among
the worst performers in the world [Hansen et al], but now they are
considered among the best. This remarkable improvement is not due to
a change in technology, but rather due to a change in the management
and information sharing within the nuclear industry. One hypothesis for
this success can possibly be attributed to the creation of the Institute of
Nuclear Power Operations (INPO) and the changes in the regulations
that took place in the 1980s by the Nuclear Regulatory Commission
(NRC). The goal of this thesis is to evaluate the actual impact of INPO
on plant performance and operations. The focus is on understanding the
transmission and usage of information, and its effect on nuclear power
plant operations.
INPO is a voluntary association set up by the nuclear industry itself after
the Three Mile Island disaster. The goal of this organization is to ensure
that nuclear power plants meet safety regulations as well as promote
excellence in power plant operations. This is achieved through
inspections, reviews, analyses, and the widespread dissemination of
information to the nuclear industry which was previously unavailable.
INPO inspections take place every 12 to 18 months; the assessments are
shared with the plant management and corporate management The
standards to which the power plants are held are based on the best
practices observed worldwide. These evaluations are akin to peer reviews
and are given a lot of weight by the utility management In reality no
utility would ignore the recommendations of INPO, and this compliance
is expected by the NRC. It is widely accepted that INPO is "the most
effective and influential information exchange organization in the
industry..." [Simon]. There is a limited understanding however about why
this is the case, and how it actually happens.
INPO collects information from its various members and analyzes it.
The processed information is used to compile reports, and formulate
recommendations which are then sent back to the members. These
reports provide an opportunity for the utilities to learn from previous
mistakes. This central processing role that INPO plays provides a
valuable service for the nuclear industry, but comes at a price. The price
paid by the utilities is the allocation of scarce resources towards
providing information to INPO, and implementing the changes that
INPO recommends. This study will attempt to understand the dynamics
that come into play. The implications of the effect that a voluntary
organization can have on reform is applicable to all high-hazard
industries, and not just to the nuclear industry.
A few years ago a model was built to simulate the operations [Eubanks],
the information flow [Simon], and the finances of a nuclear power plant
[Turek]. After collaboration with a utility owner, the operational sector
of the model was redone to better reflect reality. The goal of this
research is to improve the previously modeled information sector, and to
link it to the existing operations model of a nuclear utility. The data
needed to create this information sector is available through both INPO
and the utility itself, with which there is an established working
relationship. The software used was Vensim (DSS) by Ventana Systems.
INPO is an organization that is discreet about the mechanics of their
organization, and about the evaluation processes that they carry out on
utilities. This confidentiality is to ensure that the nuclear utilities will be
forthright in admitting their mistakes. INPO members feel that they can
provide detailed information about their operations without worrying
about the consequences were the information to be made public; this
accurate information provides INPO with an opportunity to write more
comprehensive and constructive reports.
The INPO-side data came from contacts within INPO who were willing
to provide information about its structure and the resources that it has to
offer, without giving utility details. The plant side data came from a real
utility. Once again their name and location are confidential. They
provided proprietary information about how their plant is run which
they do not want to become public knowledge.
The current model of utility operations is a system dynamics
management simulator, called the Nuclear Power Plant Management
Simulator (NPPMS). There are various scenarios that can be run (e.g. fire
drills, forced outages, changing productivity etc.), and there are various
parameters that one can look at to judge the effects of changing
management policies (e.g. outage duration, maintenance backlog,
hiring/firing rates, changing personnel ratios etc. ). There are some 500
variables in the program and the behavior of any one of them can be
observed over time. The variable of interest depends on the user of the
simulation.
The objective of this thesis is to understand the impact that INPO has
on the operations of nuclear power plants. This was fulfilled by adding
two elements to the NPPMS:
*
Modeling the way INPO collects information and produces reports
useful to utilities.
*
Modeling how the existence of INPO affects utility operations. This
includes the cost to a utility to cooperate and the benefits gained by
cooperation.
Understanding this information exchange sector is a step towards
understanding the dynamics of reform in a high risk industry.
Chapter 2
HISTORICAL BACKGROUND OF THE NUCLEAR INDUSTRY
2.1 Introduction
The discovery of radiation in the nineteenth century heralded the birth
of a new era. Over the next few decades the structure of the atom and
it's nucleus were determined. Fission was discovered in the 1930s and
was followed by the subsequent development of nuclear weapons. By
the 1960s the first commercial nuclear reactors capable of producing
electricity were available. Today, the nuclear power industry provides
22% of all US electrical needs
2.2 Performance and Public Response
Initially the nuclear industry was very popular, but over the years the
nuclear industry has seen a dramatic reduction in its popularity. The field
was originally popularized in the 1950s by congressional support. The
public was assured that this new technology was "inexpensive,
inexhaustible, and safe" [Murray, page 172]. Convinced that this was
indeed the wave of the future, the public supported the newly founded
industry.
This initially favorable start was quickly eroded by the 1960s. Along with
changing values, and a growing distrust of the government due in no
small part to the Vietnam War, and the Watergate cover-up, people
started questioning the technology that the Government had espoused.
The economics of generating electricity by this technology were no
longer favorable. To add to the dilemma, scientists themselves had
12
differing opinions about nuclear power. There were questions about
safety, unknown hazards, radiation, and radioactivity itself These
concerns were fueled by public interest groups and soon the "not-inmy-backyard" syndrome was apparent.
By the middle of the 70s, no new reactors were being ordered. There
were growing concerns about waste disposal as well, which was an issue
that had not been given serious thought when the industry originally
developed. This, along with the association of nuclear electric power
with nuclear weapons, had a very negative impact on public perception
and acceptance of nuclear power. The event that finally nailed the coffin
shut however was the incident at Three Mile Island.
In 1979 the fateful accident occurred near Harrisburg PA. Fortunately
no one was actually hurt in this incident, and the increase in radiation
exposure to the inhabitants and workers was less than that of having one
medical x-ray done. The impact on regulation and industry however was
profound.
2.2.1 Regulations and Regulators Before Three Mile Island
High-risk industries and regulation move hand in hand. Once the nuclear
industry was created, there was an immediate need to regulate it.
The Atomic Energy Act of 1946 was the initial regulation that was
passed by Congress. One of the outcomes of this act was the creation of
the United States Atomic Energy Commission (AEC). This act was
updated 8 years later to create the Atomic Energy Act of 1954 which
dealt with the regulation of civilian applications of nuclear technology.
In 1974 the Atomic Energy Commission
split into two separate
agencies: the Energy Research and Development Administration, and
the Nuclear Regulatory Commission (NRC). This occurred because of
the natural conflict that arose when the agency promoting the nuclear
industry, the AEC, was also the one regulating it
The NRC was made directly responsible for regulating the nuclear
industry. It is run by the federal government and is responsible for the
licensing and regulation pertaining to nuclear power plants. The NRC
changed the licensing requirements for nuclear plants and required that a
safety analysis report, and an environmental report be submitted before a
plant could be licensed to operate. The NRC is also the agency in charge
of creating the regulations, and conducting plant inspections and reviews
on a regular basis. One last function of the NRC is to carry out in-house
research on nuclear power technology.
2.2.2 Regulations and Regulators After Three Mile Island
1979 proved to be the fateful year when a near meltdown occurred at
Three Mile Island. The event jarred the industry and its regulators into
action as it became increasingly apparent that the measures taken
previously were not adequate to ensure a safe management and
operation of a nuclear power plant.
President Carter appointed the Kemeny Commission not only to analyze
the technical deficiencies that led to the accident, but also to evaluate the
regulatory system and to make recommendations on the safety related
aspects of the nuclear industry [Taylor, page 182]. The Commission
made recommendations about the reorganization of the NRC. They felt
that the regulations to date had not been adequate, and that
improvements were in order.
This resulted in a lot of change in the regulatory structure, and the
regulations
within
the nuclear
industry. Previously
the
NRC
requirements had focused on the technical aspects of reactors but there
was a realization that the operational aspect could not be ignored.
Corrective actions were demanded by NRC from all utilities.
Chapter 3
THE INSTITUTE OF NUCLEAR POWER OPERATIONS
..............
3.1 Introduction
The Institute of Nuclear Power Operations was created after the
occurrence of Three Mile Island in 1979 to promote safety and high
standards within the nuclear industry. The nuclear industry realized the
need for an independent organization that could point out its own
weaknesses. Such an organization would help the utilities improve, and
thus help limit the regulations that the NRC felt it should impose on all
utilities to raise their level of performance.
3.2 Conception and Need
Over the many years of existence of the nuclear industry, there has only
been one disaster within America, and that was the event at Three Mile
Island (TMI) in 1979. This incident had a profound effect on the nuclear
industry. Previously the industry had not acted as a cohesive body, but as
separate entities that had limited interaction with one another. The result
of TMI was that NRC regulations increased in number and the public
attitude towards nuclear power was less than favorable. The utilities
discovered that an incident at any power plant would have an adverse
effect on all of them. Finger pointing did little good and it was not
enough of a reassurance for the NRC or the public to say that it was
them, not us.
The event at TMI, and the subsequent Kemeny investigation into it, lead
to the realization that the industry needed to bind together and police
itself. The standards of performance had to be raised and implemented
throughout the industry, and knowledge had to be disseminated. It was
only after the accident that utilities realized that a similar event had
happened at another power plant in which the operators had managed to
stop from becoming a disaster. If the operators at TMI had known of
this occurrence they would have been able to prevent their situation
from happening. The Institute of Nuclear Power Operations was created
as a self-assessing group to monitor utilities, and to set standards for the
industry to improve itself in the various sectors:
*
Operator training
*
Emergency planning
*
Dissemination of industry information
*
Use of probabilistic safety assessment and analysis of
more probable events.
INPO is responsible for evaluating events and practices within the US
nuclear industry and disseminating recommendations. In addition INPO
conducts periodic assessments of each utility in the United States,
including operations, maintenance, engineering training, radiation
protection, chemistry, and corporate support; the results of these
inspections factor into the insurance ratings of the utility.
INPO also provides highly specialized training programs for utility
personnel, including plant managers. The NRC endorsed the INPO
training accreditation program in 1985. This program is for reactor
engineers and operators to ensure that they are up to date on the
technology that they are working with
17
3.3 Information Collection
Information is collected by INPO in two ways. First, the 110 or so
members of INPO send reports about defects and events at their site, or
if they are vendors, about the sites where their equipment is being used.
Some information also comes in through the Nuclear Regulatory
Commission. This information is made available to INPO members
only, to use when necessary.
The other source of information is through on-site inspections. This
information however is accessible only to the plant itself and is made
available to the Nuclear Regulatory Commission on-site only.
3.3.1 Event Notification
Each of the member utilities of INPO is assigned to an INPO staff
member. Each INPO staff member is responsible for 4-5 plants. The
reports of defects and events come from the various sources, and are
entered into databases, classified, and analyzed as necessary (fig 3.1).
INPO maintains various databases. One stores all the known possible
failure modes for each piece of equipment, for each type of plant in the
United States of America. There is also a database that tracks all the
defects and events that have occurred at a given power plant. There is
also a record that tracks how each of the plants is performing compared
to the others. The worst performers in different categories are tagged
for future evaluations.
Plant operators can access these databases online and learn from the
stored information. They are also able to post questions on an electronic
bulletin board, and they get answers from the rest of the industry. This
helps reduce the time spent analyzing the errors as one can learn from
the experience of others.
3.3.2 SEE-IN (Significant Event Evaluation-Information Network)
When events are reported to INPO, they undergo an initial screening by
the assigned INPO staff member. If they are deemed insignificant by the
designated INPO staff official, they are entered into the databases after a
confirmation screening by a second LNPO staff member. If, however,
they are considered significant, more work needs to be done. The event
report is subsequently screened by the INPO Event Screening
Committee to confirm if the event was truly significant or insignificant
If it is confirmed as being significant, all utilities are immediately
informed of the incident These notifications are called Significant Event
Notifications (SEN), and are broadcast over the computer network that
links all the INPO members. The SENs alert each utility to be vigilant, as
relevant (fig 3.1).
Once a SEN has been issued, further analysis is carried out to decide
whether action needs to be taken on it or not for a particular event
situation. If, after the action screening, it is decided that no action needs
to be taken, then the event is merely recorded and saved in the database.
If, however, the committee decides that action need to be taken, a
Significant Operating Experience Report (SOER) is generated detailing
the steps that need to be implemented.
The assigned INPO staff member will then undertake a detailed
investigation of the event, with the cooperation of the utility. The results
obtained by the investigation are analyzed and screened by INPO before
they are released as a SOER to all INPO members. These reports
contain specific recommendations for utilities to follow and are expected
to be adhered to by all reactors that the particular type of event pertained
to. Indeed adherence to the SOERs is a factor that affects the evaluation
status of the utility through inspections.
Utilities
NRC
I
Reactor
suppliers
Addition to
database
Fig 3-1 Schematic of Information Flow through INPO
21
3.3.3 Inspections
INPO carries out onsite inspections of all utilities every 12-18 months.
They are carried out by INPO staff members as well as by peer
evaluators. These peer evaluators are employees (engineers, operators,
controllers and maintenance workers), that each utility "loans" to INPO
for a period of time each year so that they can assist INPO in
inspections, as well as learn from the experience itself. Teams of twenty
people, including INPO staff members and peer evaluators visit the
power plant for 2 weeks. These teams spend at least 2 weeks beforehand
preparing for the inspection. They check all the databases to see what
sort of problems the utility seemed to be having in the past, and if its
name was flagged because of exceptionally poor or exceptionally good
performance. This provides each of the inspectors a better sense of what
is going on at the plant, and they can be more constructive evaluators of
the plant performance.
At the power plant itself, this team observes the everyday workings of all
the crews. They take notes and evaluate how well the plant is being
managed, operated and maintained. There is a variable number of people
from the plant who are actually involved during this inspection. These
inspectors then try and weed out problem areas and also try and help the
plant come up with solutions. Sometimes the plant will itself ask for help
in a particular area it identifies and the inspectors spend more time on
that system to better understand its failings.
At the end of the 2 week long inspection the INPO team leaves a copy
of all its raw notes for the utility and takes a copy back to the
headquarters in Atlanta, Georgia. There they analyze the data and come
up with recommendations and evaluations of the plant, in comparison
22
with the rest of the industry. These are used to compile a report. This
report is checked by various screening committees as well as by the
utility staff before it is finally considered complete. The final product is
then sent back to the plant managers and also to the CEOs to make sure
they know how they are performing compared to everyone else.
3.4 Impact on the Industry
The role of the Institute of Nuclear Power Operations has changed
considerably over the course of its existence. Starting off as a voluntary
organization, it has now taken on the role of a powerful force in the
shaping of the nuclear industry.
3.4.1 Past
In the early days there was considerably less appreciation and helpfulness
exhibited by the industry for INPO. In theory it was a good idea but not
all of the utilities actually took it seriously. It was a long uphill battle to
establish credibility and also to be accepted within the industry. Even
though INPO was organized and run by the industry itself there was still
a feeling that it was an outside organization; and the last thing that the
utilities wanted was yet another force standing and watching over their
shoulders.
Since INPO had no legal clout over the utilities there was no reason for
any of the INPO members to actually follow the recommendations that
were made. There was a wide range of adherence to INPO
recommendations. The inspections were viewed as a hassle that was
drawing precious manpower resources from areas where they were
needed more, and there was no inclination to report ones own faults.
Since the utilities would not pay much attention to this new and weak
organization, it became weaker and weaker still. The change occurred
because of the case of the Peach Bottom Reactor in which INPO played
a significant, and openly active role. This reactor was very poorly
managed and had a history of marginal safety. Eventually the NRC had it
shut down in 1987. INPO had been pointing out troubles in its
operation for some 4 years prior to the shutdown, but to its dismay, its
recommendations were being ignored. There was no improvement in its
safety standards or in its management The INPO director wrote many
letters but they fell on deaf ears. When Peach Bottom was about to
restart INPO sent many criticisms about its failings, and its unwillingness
to take any action to all parties concerned, including the NRC. These
criticisms were taken seriously however and the restarting of the reactor
was delayed. After being off-line for 25 months, Peach Bottom finally
came back up after many of INPO' s recommendations were virtually
enforced on it by the NRC. (For a more detailed description see Rees).
The acceptance of INPO's recommendations by the NRC sent a very
strong message to the rest of the utilities. It was clear that INPO had
much say in the decisions of the NRC and it would be wise for them to
go along with the regulatory body. The Peach Bottom event started a
process whereby the recommendations and the inspection reports were
taken seriously. When this happened the utilities started to realize the
value that INPO could add to each of their operations.
At the outset of the creation of INPO, the comparisons between the
utilities made them realize how poorly each was doing; this led to an
attitude where some of the worst performers were eager to ignore the
24
INPO evaluations as a thorn in their side. They felt that since the NRC
was not requiring them to do more, there was no need for them to
oblige INPO.
It was only after there was pressure from the upper
management of the utilities, that forced the plant staff and personnel to
start following the recommendations, that a sense of competition grew
between them. Thus each utility was eager to work at being the best
[Rees] This led to an increased need for INPO and this the positive
impact kept reinforcing itself.
3.4.2 Present
Today INPO plays a very important role in the regulation of the
industry. The involvement of the management with the actual operation
of the plant that has come about as a result of INPO has been
substantial. The need for INPO has grown and the industry has grown
to depend on this organization. There is a willingness to cooperate with
the organization and power plants directly approach INPO asking for
help. This is a marked change from before. The confidentiality that
INPO has always maintained has also helped foster this atmosphere of
trust, and the Institute is no longer considered an outsider.
The nuclear industry has moved from following the bare minimum
requirements of the NRC to following the best practices that are
espoused by INPO. These practices are taken from the highest
performers within the industry and have helped the US industry move to
being top in the international ranks as well.
3.4.3 Future
The role of INPO is likely to get stronger as time goes by. The question
at hand is how much more can the Institute of Nuclear Power
Operations help the industry? There is a finite amount of improvement
that can take place given the fact that the industry is slowly dying. No
new reactors are being built and the present ones are nearing the end of
their lifetimes. How much longer can INPO last, and how much can it
prolong the life of the current industry? These are questions that are
going to be determined by the political scenario rather than by the need
or use of the actual technology itself.
Chapter 4
INTRODUCTION TO SYSTEM DYNAMICS
4.1 Introduction
The impact of INPO on the performance of nuclear power plants is an
important one as it can help explain the way a high risk industry can be
reformed from within. The tool chosen to analyse this problem was
system dynamics because of its flexibility and ease of application in
modeling complex systems. Systems thinking provides an opportunity
that few other techniques can provide.
The Fifth Discplneby Peter Senge was the book that brought the field of
system dynamics into the limelight in 1990. The field however is much
older; system dynamics formally came into being in the late 1950s. It was
developed by Jay Forrester as a means of describing dynamic feedback
systems. Analysis of complex systems has been done for decades but
system dynamics provided a tool to easily incorporate feedback
phenomenon. This created a means to better reflect the real world in
modeling and simulations.
4.2 Systems Thinking
Systems thinking is about understanding complex systems. This
understanding can provide a means to effectively manipulate them.
There are various characteristics of complex systems that make them
hard to understand with just superficial analysis. Sterman mentions the
following [Sterman]:
*
Coupled Systems
Various factors are interconnected and changing one influences the
others. Thus there is no way that only one change can be implemented
without having effects ripple through the whole integrated system.
*
Dynamic Systems
Changes occur within the system on a variety of time scales. The
problems that are to be addressed are not static and using conventional
techniques does not allow a thorough analysis of the system.
*
Policy Resistance
Obvious solutions to the problems at hand are not always successful.
Indeed they can possibly be counterproductive and actually cause the
issue to deteriorate even further.
*
Counterintuitive Results
One of the major problems that is encountered in complex system is
the separation of the cause and the effect in both spatial and temporal
dimensions. It is difficult to understand the weaknesses and strengths
one holds, and the leverage that one yields when one is looking at the
symptoms without knowing the cause.
*
Tradeoffs
Last but not the least, the characteristics of complex systems that is
observed is that the long term trends vary significantly from the short
term results that one sees.
4.3 Systems Dynamics Tools
The system dynamics representation relies on stocks (levels), and flows
(rates) to model non-linear systems. Stocks are represented as boxed
variables that can be thought of as "tubs" of a certain variable. Flows are
the inputs or outputs that change these levels.
-" ---Level
Inflow
Outflow
Fig 4-1: Stock and Flow Representation
One way of creating system dynamics models is to start of by creating
causal loops. These diagrams are used to describe relationships of
causality. Variables can have either direct or inverse causal effects on one
another. In terms of calculus, this can be expressed as the derivative
being either positive or negative. Positive causality is represented as
arrows with positive signs at the arrowheads, and inverse causality as
arrows with negative signs at the arrowheads. These relationships are
developed assuming that all other factors are the same, and each causality
is evaluated independently.
4.3.1 Reinforcing Loops
Reinforcing loops are created through all positive links, or an even
number of negative links. A reinforcing loop has the effect that if a
variable is perturbed it will cause the next link in the chain to increase or
decrease in the same manner. This change propagates through the
system and eventually results in the original variable being further
increased or decreased in the same direction. This would be akin to
spiraling, either up or down, depending on which direction the loop was
activated. Graphically the results would be represented as exponential
growth or decay.
Reinforcing Behavior
C
Time
Fig 4-2: Graph of Reinforcing Behavior
Walking through the loop given below will illustrate a reinforcing loop.
+
incident
reports to
INPO
+
INPO database
usefulness of
INPO
+
R
+
Usefulness
of database
Usage of database
+
Fig 4-3: Reinforcing Loop
We will start with the variable incident reports to INPO. As the numbers
of incidents that are reported to INPO increase, the size of the database
that stores the information increases too. As the database gains
completeness, it becomes more useful to the users in terms of providing
information. This results in a greater motivation to access the database,
and eventually an increased usage. As more and more utilities use this
database, the perception of INPO' s usefulness increases. Since the
plants and associated bodies deem this a worthwhile entity, they are
more willing to voluntarily provide information to INPO. Thus the
events come full circle and there is an increase in the number of
incidents reported to the organization, all things being equal.
4.3.2 Balancing Loops
Balancing loops provide a dampening effect on perturbations that ripple
through a system. They provide a stabilizing/goal seeking behavior.
Their effect is graphically expressed as an exponential decay. When the
system is perturbed, either positively or negatively, some link in the chain
of events will respond in a way contrary to the initial direction of change.
Thus an increase in one variable will cause another to decrease and this
will be the effect carried through. In other words, the system moves to
compensate for the changes and thus reach a stable equilibrium.
Time
Fig 4-4: Graph of Balancing Behavior
Walking through the balancing loop given below will provide an
introductory example.
incident
reports to
INPO
+
INPO database
incidents
Usage of database
Usefulness
of database
Fig 4-5: Balancing Loop
The effect of increasing the number of incidents that are reported to
INPO, as explained earlier, is that the size of the INPO database
increases. As the database becomes more extensive, it's usefulness
increases and more utilities will actually use it. The effect of this learning
however is that fewer incidents will take place. The utilities will have
learned to avoid errors, and to fix defects efficiently before they cause an
incident to take place. If there are fewer events taking place, the number
of events reported to INPO will naturally decrease, even if the actual
percentage of incidents reported stays the same.
4.3.3 Combining Loops
Loops are combined to create models. The way the modeling is done is
by combining the various effects, both reinforcing and balancing. In each
system there is an interplay between the individual loops. Some loops are
stronger than others and their effects will dominate the response of the
system. It is just as likely that the long term response will be different
from the short term response. In some cases some loops gain strength
only when other loops are activated, or a loop can actually act as a timedelay switch to start or stop another. There are a number of ways that
the combined loops can actually interact and it is through modeling that
the concepts are crystallized.
S-Shaped Curve
*Tn
Time
Fig 4-6: S-shaped curve
The example below shows how the loops previously mentioned are combined
inthe system.
incident
reports to
INPO
+
perceived
usefulness of
INPO
5
\+
incidents
INPO
Usage of database
INPO database
Usefulness
cof database
Fig 4-7: Combining Loops
4.3.4 Simulations
Once causal loops have been done, the next step is actually creating
stock and flow diagrams that are converted to numerical relations. These
equations are the basis of each link in the model. Once a robust model
has been created, it is then tested by running various known scenarios,
and doing sensitivity analysis. The system is calibrated and initialized in
equilibrium.
At this stage various "'what-if" simulations can be run. These can
provide insight into the way a system might behave in the future, and can
also be used to find policy impacts.
4.4 Policy Implications
Leverage points are gleaned through sensitivity testing, and allow the
users to see what possible outcomes can be expected. One issue of
importance is that system dynamics does not tell the future; it can show
34
possible trends given the current framework, but not the actual values
that will occur. One might be tempted to call this a shortcoming of the
field and usage in this manner is a common mistake made by new users;
however this is not the intended use of the tool, and is an unrealistic
expectation. The only way one can expect "real world" values would be
to model every last detail. This is an impossible exercise because it would
be akin to recreating the world. The ease of system dynamics, and indeed
all modeling techniques, is the fact that they create boundaries to define
the problem within, and thus help simplify the issues. Some of the policy
benefits can be derived based on the nature of the characteristics of
complex systems.
Policies can be modeled to see the effect that they will have on coupled
systems. The far reaching effects through the system can be observed
which would not be otherwise possible. The result of implementing
regulations on systems that change with time can be safely tested.
This is especially important because of counterintuitive results that might
be obtained. Systems thinking provides an opportunity to work through
the various scenarios to create robust policy. This can prevent failures in
the future. It is possible to weigh the long term benefits vs. the short
term benefits before actually creating policy.
One very useful aspect of using systems thinking to test policy before
implementation is to check for policy resistance. There is an opportunity
to learn of future obstacles that might come up, and solutions can be
shaped in advance.
Chapter 5
INTRODUCTION TO THE PLANT OPERATION MODEL
5.1 Introduction
This chapter describes the actual models that have been used to provide
results for the analysis. There is an existing system dynamics model that
simulates the operation of a nuclear power plant (NPPMS) that was
developed by Sangman Kwak; a new sector was created that simulates
the flow of information that is processed by the INPO organization
(INPO sector). After the INPO sector was independently developed, it
was integrated with NPPMS to give a better understanding of the system
(NPPMS-INPO).
5.2 Nuclear Plant Operation Model (NPPMS)
This system dynamics model was created to represent the operations of a
nuclear power plant [Kwak]. The various relationships that were
developed to model the maintenance, scheduling, staff allocation and
performance were developed while working in close collaboration with a
nuclear utility.
The model has been calibrated to the data they provided, and provides
an accurate picture of the way the plant operates both in steady state and
in the case of perturbations to the system. The nomenclature used is
specific unto this plant as well.
5.2.1 Defect Generation and Correction
Defects are to be expected in any and all systems that exist. In electrical
power plants where there are thousands of pieces of equipment that are
closely coupled, it is not uncommon to have defects which may
eventually lead to breakdowns. Defects come about for a number of
natural reasons (operational wear and tear, poor workmanship, stress
caused by defects in other equipment), and do not necessarily imply that
the piece of equipment in question has failed.
Defects, with or without breakdowns, affect the overall performance and
productivity of the system. If enough defects and breakdowns occur,
there comes a point where safety is compromised in a power plant. This
is because the safety of the system is assumed to be related to the
number of defects.
Defects are identified through a number of different programs. There are
mandatory
inspections
(Surveillance
Test
Procedures
-
STP),
Preventative Maintenance programs (PM), and voluntary observation
practices implemented in the utility. The majority of the defects are,
however, identified not through the STP and the PM programs, but
through observation by maintenance crews walking around the plant, or
as they are fixing other defects. Defects are also observed by operators.
Once identified they are reported back to engineering and enter a
backlog of work that needs to be done. The engineering staff analyzes
the errors, and sets up a schedule for repair. On average it takes a month
for a defect to be corrected. Defect detection is not one hundred percent
effective.
Some of the errors that previously went unnoticed are discovered when a
defective piece of equipment breaks down. These failures are also fed
into the work backlog that is taken care of over time.
There are some sorts of defects that are considered high priority and are
fixed as soon as they are found. When one of these occurs, all necessary
crews are assigned to working on it until it is completed. These defects
represent potential failures in equipment that if not fixed immediately
could compromise the safety of the plant, or its continued operations.
Defect correction is undertaken by crews assigned to the task. The
backlog is worked off at a fairly steady rate during normal plant
operations. During an outage there is an increased rate of completing
high priority and maintenance work. Outages provide a valuable
opportunity to fix equipment that would normally have to be taken offline to be repaired, and thus cannot be modified while the plant is
operating.
5.2.2 Engineers, Operators and Maintenance Crew
There are three main types of non-managerial staff at a nuclear power
plant: engineers, maintenance crews, and operators. Each has a specific
role that they play in making sure that the system is functional.
The operators are responsible for the actual control and operation of the
reactor at any given time. They are trained to handle not just the daily
running of the plant, but also to handle various types of operational
accidents. These people monitor control panels that show at any given
time what systems are functioning when the power plant is in operation.
The engineers are responsible for planning and scheduling the work.
They write the procedures for defect correction and know how each
individual piece of equipment works. They also modify existing
procedures and provide support while the maintenance crew does its
work.
The maintenance crew are responsible for finding and correcting defects.
They are assigned to defect correction based on the urgency of the
problem. The highest priority work (PI) gets done immediately as it
signals a potentially critical situation; all staff needed to correct the defect
are assigned to it The next level of priority is P2. All necessary
maintenance workers are assigned to P2 work until it is completed as P2
problems affect the continued operation of the plant and cannot be
delayed either. P3 work has a lower priority as it is the routine corrective
work that needs to be done. Maintenance workers are assigned on a need
basis, and there is usually a backlog. The lowest priority work are Tool
Pouch work and Support Work. Maintenance workers are assigned to
Tool Pouch and Support duty only when they can be spared from doing
other corrective work.
5.3 INPO Information Flow Model (INPO sector)
The model of the information flow through INPO and its feedback into
the industry is represented through different stages (documentation in
appendix A). Simplified versions of the defect generation, defect
identification, defect correction, and worker allocation at the utility were
created.
The maintenance sector of a plant has been represented in a simplified
manner. Defects are generated as a sum of a standard defect generation
rate and a breakdown rate. These are calculated as a fraction of all the
equipment in the plant over a period of time (fig 5-1). There are about
100,000 separately identified pieces of equipment in the plant and defects
are created during normal operation at the rate of approximately 90
defects/week (about 0.1% of plant equipment develops defects in any
given week). The breakdown rate is about 30 pieces of equipment per
week. A fraction of the undiscovered defects are actually identified by
the various crews.
Fraction
Breakdowns
<Plant
Equipmat>
Avg time for
breakdowns to
Breakdown rate
O dedt
C
=
DD
et =
DDe4
fixin
De
genrateion
<Normal Defect
Generation Rate>
<Avgtimeto
find defeds>
<Avg timeto
fix defects>
Fig 5-1 Defect Discovery and Correction
The discovered defects, and the broken down equipment enter a backlog
of maintenance work that first needs to be scheduled and then carried
out (fig 5-2). This process takes a month.
rate>
<Defect ID
rate>
Worker
generation
<Avg time to \
fix defects> <Maintenance
Crew>
Fig 5-2 Work Backlog
The first element of the data collected by the INPO organization that is
represented is the breakdowns detected at all nuclear power plants. This
input is a simple aggregation over 107 plants that are members of INPO
(fig 5-3). There is an assumption that events occur only when equipment
breaks; and that only a very small fraction of all breakdowns result in
events. There are about 4500 defects/year that are reported by the
whole industry.
Fraction
Breakdowns
Causing Events
Number of Utili
Events
<Breakdown
rate>
SEN fraction
Events undr
Investigatio
vestigati
SEN rate
SOER
fraction
SOR
lgeneation
gdelay
Fig 5-3 Flow through INPO
Once this information has been fed into the system, INPO analyzes each
of the events that are reported. A fraction (3%) of these are deemed
significant and these are then reported back to the utilities (SEN). The
SEN rate is about 135 notifications per year. After a delay of 3 months
representing the length of time it takes to conduct investigations and
research the events, reports are generated that are also broadcast to the
whole community (SOER). The SOER generation is about 3 per year.
The SENs and SOERs generated by INPO are assumed to affect the
utility defect discovery and the defect correction rates. The Significant
Event Notices and the Significant Operating Experience Report provide
a means for learning. The utilities learn not just from their own defects
but also from others. This learning effect is represented as an
effectiveness factor (fig 5-4, fig 5-5, fig 5-6) that reduces the average time
to find the defects because the crew knows where to look, and also
reduces the time to fix the defects. This is because the planning
engineers are more efficient at making up work plans to deal with the
defect (fig 5-7). The values of the effect of the number of SOERs and
SENs generated on Inspection Effectiveness are assumed, and are
variables. The nominal value of the Inspection Effectiveness factor is 1,
which implies that the industry is operating by itself, without any input
from INPO.
Inspection effectiveness = max(l, (Effect of SEN rate(SEN rate)
+Effect of SOER rate(SOER generation rate)))
Effect of SEN r
<SEN rate>
Inspection
effectivness
/"
Effect of SOER rate
<SOER
generation
rate>
Fig 5-4 Inspection Effectiveness
Effect of SENs on Inspection Effectiveness
00.8
1.2
o 0.8
", 0.6
0.4
0.2
0
1.00
2.00
3.00
4.00
5.00
6.00
7.00
SENs generated per week
Fig 5-5 Effect of SENs generated on Inspection Effectiveness
8.00
Effect of SOERs on Inspection Effectiveness
0
01
0.2
3
0.4
0.5
SOE
guer.dpws j
0.6
0.7
0.8
0.9
1
week
Fig 5-6 Effect of SOERs generated on Inspection
Effectiveness
Normal Planning Crew
Time to find defects
Avg time to
find defects
<nspection\'k
effectine
Avg Planning Crew
<Inspection
effectivness>
Crew
Planning Time
Avg Planning Time
on
Avg time to
effectvnesWfix defects
Action Time
Fig 5-7 Effect of Inspection effectiveness on Utility
Maintenance
Crew
Over time the actual defect identification rate and the defect fixing rate
decrease. The backlog of maintenance work also diminishes. As the
number of defects is reduced there are fewer breakdowns and thus
events.
The inspection effectiveness also plays a part in the allocation of workers
to maintenance work. There is a total of 79 crews and when the backlog
of P3 work increases, workers are allocated to planning and scheduling
duty rather than to fixing defects. This is because the delay in planning
and scheduling each defect is the cause of the increase in the backlog.
This delay is mainly due to the time it takes to order the replacement
equipment (planning time is about 3 weeks) rather than due to the time it
actually takes to fix the defect (action time is about 1 week).
5.4 Link to Plant Operation Model
After the flow of information through INPO was modeled and tested
(INPO sector) it was linked up to the existing model of the operations of
a nuclear power plant (documentation in appendix B). This new
combined model is called NPPMS-INPO. The values and relations used
were the same as described in the previous section. Some of the variable
names were changed but their functions remained the same. The
simplified defect detection, generation and correction sectors, as well as
maintenance planning and scheduling sectors from the INPO sector
model did not have to be used as they already existed in detailed form in
the NPPMS model. What needed to be incorporated was the SEN and
SOER generation, and the effects they had within the utility.
Various adaptations were done to see the effects that the information
from INPO had on the following elements:
*
SENS and SOERS on defect identification through improved
insights and training of operators and maintenance staff.
*
SENS and SOERS on defect fixing through learning effects on
maintenance staff and engineering staff.
5.4.1 Defect Detection
The majority of the defects detected in the plant come about through
various crews walking around the plant, or when they are fixing some
other piece of equipment As the defects are discovered they are fed into
the planning and scheduling department.
The effect of getting reports and recommendations from INPO can be
seen on the observation effectiveness of both the utility operators and
the maintenance crews (fig 5-8). The observation effectiveness factor
was the variable created, and is equivalent to the variable called
Inspection Effectiveness in the INPO sector model.
Obseatimi effietiv ess fator
effective observation rate
effective observatinate
>defect discovery ratee
deftt disouy rate x
Defects Unbroken Identified
Undiscovered Defects x
Deficiency Reports
Generate rate p2 x
Generate rate p3 x
Generate rate tool pouch x
Fig 5-8 Effect of INPO (defect discovery)
The SENs provide a focus to the observations (fig 5-9). The crew has
some idea which equipment is sensitive to particular types of failure
modes and they are more sensitive to potential problems. The INPO
input provides an accelerated pace of experience accumulation by
learning through the problems of other utilities with comparative reactor
types (fig 5-9). The variable INPO effect factor is identical to the
Inspection Effectiveness factor from the INPO model alone.
e ftinfo Pody ftor x
O vafin effectieessx
infoprody fidorx ->
engineer jP
comsantM
ier pradutivitynfminl
x
nivityaniml
c
ediveh rvatin ate
dvit
x
productivity nginees x
Fig 5-9 Effect of INPO (engineering)
5.4.2 Defect Correction
The impact of INPO on the factors that actually contribute to both the
rate of defect correction and the quality is profound. As the flow of
information from INPO trickles into the plant, there is a person assigned
to evaluate it. This person then decides its relevance, and which sector it
needs to be assigned to so that learning can take place. This learning
process requires time which is at the time expense of actually carrying
out the normally assigned duties. The learning effects the productivity
and also the effectiveness of the planning and scheduling by the
engineers. This can be seen through the increased quality of the
maintenance work that is carried out (fig 5-10). When a defect is
reported, it is first analyzed, and then, after considerable planning, the
work orders are actually issued. Having the INPO defects database, as
well the SOERS influences the actual time spent planning the defect
correction, and the quality of the work orders that are eventually issued.
There is less time required to analyze the defect, create the work orders,
and to plan modifications.
pui-iityini
[mttiplelci]Ky
cityni]a
Fig 5-10 Effect on Engineering (planning)
In this manner both speed and effectiveness are enhanced. This effect
ripples through the system and can be seen in the quality of the work
performed by the maintenance crew. The work backlog can be handled
faster and more efficiently. The other impact can be seen in the support
that is provided to the maintenance crew as they perform their
assignments. This factor too is affected by the quality and productivity of
the engineering work that is done.
Chapter 6
SIMULATIONS, ANALYSIS AND CONCLUSIONS
6.1 Introduction
The INPO sector model, the NPPMS model and the NPPMS-INPO
model were run under various different scenarios to see the trends
exhibited. In the first case, the INPO model was run to observe the
effects of learning on the industry. Once it had been established that a
learning effect can be seen, as verified by industry, sensitivity analyses
were run on the model to see where the leverage points might lie.
The next case that was run was the base case of the original model
combined with the INPO sector. The results of this NPPMS-INPO run
were compared to base case runs of the original model. These provided a
basis for understanding how the system works, what effect the learning
sector has, and to see how long it takes for the system to settle down in a
new equilibrium. Once this had been established, and the results
analyzed, another scenario was simulated where the maintenance
workforce was reduced after the learning effects from having the INPO
sector had stabilized. This was compared with making the same changes
in the original model to see the differences. Once this analysis was done,
policy recommendations were made.
6.2 Simulation Results: Combined Model
6.2.1 Base Run
The base case scenario was run after adding the INPO sector (INPObase), but without making any changes to other parameters. The results
were then compared to the older version of the
NPPMS model
(NPPMS-base) to see the differences.
The information flow impacted the plant performance in all sectors.
Initially there was an increase in the work that had to be done but that
decreased over time.
The number of defects that were generated and identified increased for
the first two years but after that there was a gradual reduction and they
started to follow the NPPMS-base case (figs 6-1).
Guph fcr dbfect gmere re x
200
150
100
50
0
0
26
52
78
104 130 156
T (%e&k)
dect generte rate x[JfR]: NIWVtFase
defed a te
rate 40121]: N((
182
208 234 260
OfecWis
ek
Dfec
kPv-O----cW
kekc
Fig 6-1: Defect generation rate (Other)
50
The defect generation rates increased because as the number of INPO
reports increased, the engineering work needed for maintenance support
increased; this caused productivity to initially fall as they tried to learn
and keep up with the increased work. There is an increase in the actual
work that needs to be done too because more of the defects were
identified; and this increase became more than they could handle initially.
To handle the backlog all the support staff and the tool pouch crew were
assigned to do P3 backlog work (fig 6-9) instead of carrying out their
respective work. Once the backlog was worked off the crew was allowed
to return to its normal duties.
Graphfor action staff P3 x
40
30
20
-
10
0
0
26
52
78
104 130 156
Time (Week)
182
action staffP3 x[UNBROKEN] :NPPMS-base
action staff P3 x[UNBROKEN] :NPPMS-INPO --------
Fig 6-2: P3 staff
208
234
260
Persons
Persons
Graph for action staff TP x
10
IA
7.5
5
~~|-
25
0
0
52
26
78
10C
14 130
156
Time (Wee]k)
action staff TP xUNBROKEN1 : NPPlS-base
action staff TP x[UNBROKEN] : NPPIS-INPO
182
208
234
260
Crew
Crew
-..
Fig 6-3: TP staff
Graph for action staff x
26
52
78
104
130
156
Time (Week)
action staffxrSPRT1 : NPPMS-base
action staff x[SPRT] : NPPMS- INPO
Fig 6-4: Support Staff
182
208
234
260
Crew
Crew
The defect generation rate also depends on how big the work backlog is
(fig 6-9), and how many total defects (fig 6-7) there are. As these factors
grew because the observation teams got more adept at finding the
defects (fig 6-4, 6-5, 6-6), they caused an increase in the defect generation
too. The defect generation rate for the mandatory inspection programs
(the preventative maintenance program, the surveillance test procedures),
and the normal generation rate grew by about the same magnitude.
We can also see the improvement in the defect identification through
observations, PM and STP, which results in fewer defects leading to
breakdowns as they are identified before they can do so.
Graph for defect discovery rate x
400
300
200
100
0
0
26
52
78
104
130 156
Time (Week)
182
208
defect discovery rate x[OTHER] :NPPMS-base
defect discovery rate x[OTHER] : NPPMS-INPO ----------------...........
Fig 6-5: Defect Discovery rate (Other)
234
260
Defects/Week
Defects/Week
We can see the increased number of crews assigned to working off the
P3 work backlog until the levels start to stabilize at the NPPM-run levels.
These crews are diverted from the Tool Pouch work that they would
normally be assigned to. As the P3 work backlog slowly gets worked off,
the crew go back to their Tool Pouch work. Another effect seen was the
increased number of planners that were needed to help work off the
backlog
Graph for planning staff
\7
26
-
-- K -
52
78
104
130
156
Time (Week)
182
208
234
planning staffP3 : NPPMS-base
planning staffP3 : NPPMS-INPO
Fig 6-6: Planning staff
The total defects
(fig 6-7) in the plant also increased but sharply
dropped after 1.5 years (almost halved in a period of six months). At this
54
260
Persons
Persons
is point the bulk of the backlog that has been created by the improved
observations around the utility is worked off and the plant can return to
its earlier manpower allocation rates.
G iridd
s.
1M
7
40
0
25
78
01 M 1% M1 28 2N 2D)
Toa(W1<)
aiddits :1W
___
ITBds
Fig 6-7: Total defects
The rate of equipment breakdown (fig 6-8) and the number of
undiscovered defects also decreased but in an exponential fashion and
appeared to flatten out after a time. This can be seen because the
defective equipment was spotted before it broke down, and there were
fewer undiscovered defects.
Graphfor rate of equipnmet break down
40
32.5
25
17.5
10
0
26
52
78
104
130
156
182
208
234
260
Tine (Week)
rate of equipnaet break dow: NPP S-base
rate of equilment break down: NPP~INP O------....
EqupmetWeek
Equipment/Week
Fig 6-8: Rate of Equipment Breakdown
Two other variables tracked showed the following behavior: There is an
increase in work backlog that lasts for 2.75 years after which it follows
the base case, and eventually starts to decrease after 4 years (fig 6-9).
Graph for backlog P3
2000
1,500
1,000
500
0
0
26
52
78
104 130
156
Tnm (Week)
baddog P3: NPPMS-base
baddklog P3: NPPMS-INPO
182
208
--
234
Defects
Defects
Fig 6-9: P3 backlog
There was an increase in the capacity factor, and a reduction by a factor
of 2 in the expected capacity loss of the plant. This is important because
it means the plant performance overall has improved and is more
efficient and profitable (fig 6-10).
260
Graph for expected capacity loss
4
3
-
2
--
\
-
1
0
0
26
52
78
104
130 156
Time (Week)
182
208
expected capacity loss: NPPMS-base
expected capacity loss: NPPMS-INPO ..............................................
234
260
Percent
Percent
Fig 6-10: Expected Capacity Loss
6.2.2 Merits of INPO
It can be seen through this simulation that there is a marked
improvement in the long term performance of the plant by including the
INPO sector. The learning opportunity that is provided by INPO
recommendations is a burden at first because it takes workers away from
their daily tasks. Over time however the utility is compensated by a drop
in the total defects at the utility, and an increase in the performance and
safety. There are fewer forced outages and this directly translates into an
improved economic performance as the plant is able to produce
electricity with fewer glitches.
6.2.3 Maintenance Downsize
A new scenario was run to see the effect that a reduction in the
maintenance crew would have on the plant. Utilities might be tempted to
reduce their crew to save money to be able to reap a larger profit without
thinking of the long term effects of such an action. In the short term it is
obvious that it would save money but the long term effects are not so
clear, especially in the case of a emergencies or outages.
This scenario was run by reducing the number of maintenance workers
by 10%, after the new model with the INPO sector had reached a new
equilibrium. This change was made at week 260. The results from this
run were compared by making a similar change in the original model at
week 260.
In the run with the original model, without the INPO sector, a reduction
in the maintenance staff does not adversely effect the plant performance
until the first outage is over. During the outage all staff is diverted to
dealing with the outage, and no one is fixing P3 defects. During this time
the backlog grows by so much that once the plant returns to normal
operations, the reduced work force cannot work it off. All the support
and toolpouch workers are also diverted to maintenance duties yet they
still cannot recover with the reduced staff. Very soon the backlog grows
out of hand and increases exponentially. This results in a dramatic
decrease in the plant capacity and increases the likelihood of further
forced outages.
Similar results can be seen in the model with the INPO sector. By week
260, the system has reached an equilibrium where the defect generation
rate is slightly lower than the base rate. After the reduction in the
maintenance staff, the defect generation starts to increase after the first
outage after week 260 (fig 6-11). The second outage worsens the
situation. The defect generation rate continues to grow and the system
does not return to its initial equilibrium levels. By week 520 the defect
generation rate though starting to return to the initial equilibrium does
not get there. The defect generation rate is initially lower than the base
rate but eventually it catches up and reaches a new equilibrium when it
follows the base case by week 520.
Graph for defect generate rate x
200
150
F\r....= P.
: .%
. _
._.
100
50
0
0
52
104
156
208 260 312
Te (Week)
364
defect eneate rate xfOTHER1 : rmintdown260
defect gerate rate xOHER] indown260 ...............
416
468
Defects/Week
DefectsWeek
Fig 6-11 Defect generation rate
There is an increase in the rate of equipment breakdown that can be seen
after the first, and especially the second outage (fig 6-12). The rate of
equipment breakdown follows the trend of the base rate but does not
reach the same values. This is due to both the increase in the defect
60
520
generation rate, as well as the increased number of defects in the backlog
that need to be worked off. As the defects pile up, there are more and
more breakdowns that occur before the defects are actually dealt with.
The difference in the breakdown rates between the two simulations
stays at the same level as the difference that existed at the equilibrium
before the maintenance reduction took place.
Craphfcr rate of equipmut heak dow
40
30
20
10
0
0
52
104
156
208 260 312
Tne (ek)
364
rate cfeqipmt brreak down: naidowr260
rate cfein
break don •irVodo0n
..............
416
468
520
ic~lp~t~e
Fig 6-12 Equipment breakdown rate
As the rate of defects generated, and the rate of breakdowns increased,
without a proportional increase in the rate of repair, the total defects
increased. The increase after the first outage is only slight but after the
second outage (week 312) there is a strong linear increase in the total
defects. The model with INPO follows the base case results with a
61
constant difference. This increase in total defects (fig 6-13) has an effect
on the capacity of the plant. As more and more equipment breaks down,
it causes other equipment to fail too. This eventually results in an
increase in the probability of having a forced outage (fig 6-20).
Graphfor total defects
40,000
30,000
20,000
10,000
_
-
0
0
52
104
156
208
260
312
364
416
468
520
Trime (Week)
total defects: maihtdown260
total defects: inpodown260 ...........................................
Defects
Defects
Fig 6-13 Total defects
The defect discovery rate in plant does not show an appreciable
difference at the very beginning (fig 6-14). This is because even though it
is easier to find defects because there are more of them around, there are
fewer people around to actually do so. A large percentage of defects are
discovered through observation, and in the maintenance reduction
scenario, all the tool pouch workers, and support staff, who do much of
the observations, are assigned to fixing scheduled P3 defects. An increase
62
is notable later on however. This happens because the number of defects
grows so much that even the reduced staff discovers more defects.
aph for defect discwey rate x
400
300
200-
0
---
---
100--:-4------
1
0
52
104
156
208 260 312
T~in(Week)
364
416
468
520
defect discovey rate xfOIHER: mintdovwn260
Dfect/Week
de ds
y rdte Nx[OHER] hoda
D ..............
aects/Week
Fig 6-14 Defect Discovery rate
The P3 work backlog shows a slight increase after the first outage but
the workers are unable to work it off before the second outage occurs.
This further increases the backlog and the number of P3 repairs that
need to be done increase very quickly. The system is not able to recover
in 5 years, and is still growing.
The backlog with the INPO sector shows a reduced number of P3
defects that need to be fixed but the rate of increase is the same as in the
case of the original model (fig 6-15). The plant which has informational
63
learning is able to control its work backlog for a little longer than one
which does not have one, but cannot sustain it in the long run.
Graph for backlog P3
20,000
15,000
10,000
5,000
--
L,-I-
0
0
52
104
156
208 260 312
Timn (Week)
364
416
backlog P3 : maintdown260
backlog P3 :inpodown260 ............................................
468
520
Defects
Defects
Fig 6-15 Work backlog
There is a growing need for planners for P3 work as the backlog grows
(fig 6-16). This is because defects cannot be fixed if they are not planned
and scheduled first. The learning caused by INPO shortens the planning
time but it cannot change the actual procurement time of the
replacement equipment The improvement is thus restricted by factors
that cannot be changed by the operational crew. This delay in defect
fixing and planning results in additional breakdowns taking place before
the backlog can be worked off. As the backlog keeps growing there are
not enough planning staff to deal with all the work that has been created
and staff is diverted away from action work to take care of the planning.
15
-
--
10
0
52
104
156
plaring staffP3: nirtdowvn2
planr1igsta3 :ir'30-
208 260 312
Tin (Week)
-
364
416
......................................
468
520
Pers
Prsns
Fig 6-16 P3 Planning Staff
The diversion of the action staff away from their regular duties further
compounds the problem. The reduction in maintenance staff results in
there not being enough P3 workers to work off the backlog (fig 6-17).
All the tool-pouch crew (fig 6-18) and support staff are assigned to P3
work (fig 6-19) after the first outage occurs. The size of the backlog does
not allow them to return to their normal duties even in the long run as
there are more pressing events to be dealt with.
Graph for action staff x
0
52
104
156
208 260 312
Time (Week)
action staffx[P31 : maintdown260
action staffxP31 : inpodown260
364
416
--------.---.---
468
--
520
Crew
Crew
Fig 6-17 P3 Action Staff
Graph for action staff x
10
7.5
5
2.5
0
0
52
104
156
208 260 312
Time (Week)
364
416
action staffx TP: maintdown260
action staffxTP: inpodow1260
Fig 6-18 Tool Pouch action staff
66
468
520
Crew
Crew
Graph for action staffx
10
7.5
5
25
0
0
52
104
156
208 260 312
Tne (W;k)
actin staffx[SPK : naintdow260
action staffx[SPRTJ: inpodo~n260
364
416
468
520
Crew
---------------
------------
Crew
Fig 6-19 Support Action Staff
This huge increase in the total defects (fig 6-13) in the plant, which can
also be stated as a reduction in the amount of equipment that is
functional, not surprisingly results in a loss in the expected capacity of
the plant. This means that forced outage rate increases, and the plant
performance goes down (fig 6-20).
Graph for expected capacity loss
60
45
30
15
--
0
-
0
52
104
-
-
156
-
-
-
- -
208 260 312
Time (Week)
364
416
eaected capacity loss: naintdown260
expected capacity loss: odown260 ................................
468
520
Percent
Percent
Fig 6-20 Expected Capacity Loss
6.3 Conclusions
Running these various simulations indicates that having an agency like
INPO is a great asset to the nuclear industry. The information sharing
and analysis role that INPO plays results in improvements in
performance in the long run. There is increased work in the short run
but it more than compensates for the improvement later.
The
improvement in the safety of the plant can be seen from the reduction
in forced outages, and a decreased defect generation rate. The staff
members are more up to date with the equipment that they are working
with, and are generally more competent. They are given the opportunity
to learn from the mistakes of the past to help prevent them from
occurring in the future.
What can also be seen is that reducing the size of the maintenance
program is detrimental to the plant. The system quickly goes out of
control and cannot recover even in the long run. The reduction in the
maintenance crew caused the number of defects, that are not dealt with
in a timely fashion, to increase to such proportions in the long term that
the capacity factor of the nuclear utility fell to zero. The plant was unable
to run after 3 years.
This is an indication that it is very important to have adequate numbers
of maintenance workers. It is economical in the short run to reduce the
maintenance crew but in the long run it could result in a total shutdown
of
the nuclear utility. This is
because the plant performance is
compromised very extensively by defects that are generated faster than
they can be fixed. INPO dramatically helps plant operation as can be
seen from the base case run, however it cannot make up for bad
management policies.
Information sharing, and learning through experience should be an
integral part of every high risk industry. Organizations such as INPO,
while packing no legal clout, help foster an atmosphere where safety, and
operational excellence is paramount Run by the industry themselves, it
makes utilities want to improve themselves. This attitude can
undoubtedly encourage reform in similarly risk-prone industries.
APPENDIX A
<Breakdown
Defect
generation
rate
<Normal Defect
Generation Rate>
rate
/
<Avg time to
fix defects>
<Avg time to
find defects>
<Breakdown
rate>
<Defect ID
rate>
Work
backlog Backlo
generation
Worker
productivity
Backlog
fixing rate
<Avg time to
fix defects>
Fraction
Breakdowns
Causing Events
SEN fraction
Number of Utili
SOER
fraction
Events und
Event SEN
Events
<Breakdown
rate>
<Maintenance
Crew>
Investigaticn SOEF
rate
SOER
generation
delay
Effect of SEN rate
Planning Time
Inspection
<SEN rate>
effectivness
<Inspection
Avg Planning Time
Avg time to
effectivness>
A te
fix defects
Effect of SOER rate
<SOER
Action Time
generation
rate>
Normal Planning Crew
Time to find defects
<find
Avg time to
defects
<Inspection
<Inspection \ Avg Planning Crew
API
effectivness>
<Inspectiones
Maintenance
effectivness>
Crew
Avg time to find defects=
ime to find defects/Inspection effectivness
-Week
-This is the average length of time it takes to find defects through
inspection. It is modified by the Inspection Effectiveness to see the
effect of INPO.
Avg Planning Crew= max(Normal Planning Crew, Normal Planning
Crew*Inspection effectivness)
-person/utility
-This is the actual number of crews that are assigned to planning rather
than to maintenance duty, taking into account the extra work that is
created because of INPO.
Avg Planning Time=min(Planning Time, Planning Time/Inspection
effectivness)
-Week
Crew
-This is the average time it takes to plan and schedule each defect that is
reported.
Defect ID rate= (Defects Undiscovered/(Avg time to find defects))
-defects/(Week*utility)
-This is the rate of defect identification.
Planning Time=1.5
-Week
-This is the time it would take to plan each defect if there was no extra
work created by the INPO reports coming in.
Avg time to fix defects=Avg Planning Time+Action Time
-Week
-This is the actual time it takes to fix defects. Assume that it takes 1
month to turn over the backlog (2.5 weeks for action and procurement,
about 1.5 week for planning)
Normal Planning Crew=8
-person/utility
-This is the number of crews that would be assigned to planning and
scheduling without any effect from INPO.
Time to find defects=0.5
-Week
-This is the time it would take to find defects without the effect from
INPO.
Normal Defect Generation Rate =standard undiscovered
73
-defects/(utility*Week)
-This is the normal rate at which defects are generated in a plant
Breakdown rate = (Plant Equipment*Fraction Breakdowns)/Avg time
for breakdowns to occur
-defects/(Week*utility)
-This is the fraction of the total equipment in the plant that results in a
breakdown.
Defect generation rate =Normal Defect Generation Rate+Breakdown
rate
-defects /(Week*utility)
- This is the sum of the normal defects caused by operation as well as
the breakdowns that might occur for different reasons
Inspection effectiveness =max(1,(Effect of SEN rate(SEN rate) +Effect
of SOER rate(SOER generation rate)))
-dimensionless
-This is the dimensionless multiplier that represents the effect of INPO.
Effect of SOER rate( [(0,0) (10,2)],(0,0),(1,0.2),(2,0.4),(3,0.6),(4,0.8),(5,1),
(6,1.1), (7,1.2),(8,1.3),(9,1.4),(10,1.5))
-dimensionless
- This is the effect of the number of SOERs that are generated on the
Inspection Effectiveness.
Defect fixing rate= (Defects Discovered/Avg time to fix defects)
-defects/(Week*utility)
-This is the rate at which the discovered defects are fixed.
Effect of SEN rate(
[(0,0)(10,2)],(0,0),(0.41958,0.199288),(1.14219,0.405694),(2.00466,0.57651
2),(2.86713,0.718861),(3.91608,0.875445),(5,1.05),(6,1.15),(7,1.25),(8,1.35)
,(9,1.45),(10,1.5))
-dimensionless
-This is the effect of the number of SENs generated by INPO on the
Inspection Effectiveness.
Action Time=2.5
-Week
-This is the time it takes to procure the equipment to fix defects.
Maintenance Crew=Crew-Avg Planning Crew
-person/utility
S-This is the number of crews that are assigned to do maintenance work.
Crew=79
-person/utility
-This is the total number of crews that are available to the maintenance
sector. Some are assigned to planning and scheduling of defects, while
others are assigned to actually fixing the defects.
Worker productivity=7
-defects/person
-This is the number of defects that each worker is able to correct.
Backlog fixing rate= max(Work Backlog/Avg time to fix defects,
Worker productivity*Maintenance Crew/Avg time to fix defects)
-defects/(Week*utility)
-This is the rate at which the defects are fixed that are in the backlog.
Avg time for breakdowns to occur=275
-Week
-This is the average lifetime of each piece of equipment
standard undiscovered= Plant Equipment*Fraction defects
-defects/(utility*Week)
-This is the fraction of equipment in a plant that is defective because of
normal wear and tear.
backlog generation =Breakdown rate+Defect ID rate
-defects/(Week*utility)
-This is the rate of creating a work backlog in a plant They enter the
queue of defects that need to be fixed.
Events =Breakdown rate*Fraction Breakdowns Causing Events*Number
of Utilities
-events/Week
-This is the fraction of breakdowns that result in events happening,
aggregated over the nuclear industry.
Number of Utilities= 107
-utility
-This is the total number of plants that are members of INPO
Fraction defects=0.001
-defects/(equipment*Week)
-Assume that 0.1% of all plant equipment develops defects through
normal operation.
Plant Equipment=88500
~equipment/utility
-Number of separately identified pieces of equipment in each utility
Defects Undiscovered= INTEG (Defect generation rate-Defect ID rate,
Defect generation rate*Avg time to find defects)
-defects/utility
-This is the number of defects in the plant that are not discovered yet.
Defects Discovered= INTEG (+Defect ID rate-Defect fixing
rate,Defect ID rate*Avg time to fix defects)
-defects/utility
-This is the number of undiscovered defects in the plant that are
discovered.
Events under Investigation= INTEG (SEN rate-SOER generation rate,
SEN rate*SOER generation delay)
-Notifications
-This is the number of significant events that are reported to INPO that
are investigated further.
Fraction Breakdowns=0.1
-defects/equipment
-assume 10% of equipment breaks down over a period of 275 weeks
Fraction Breakdowns Causing Events=0.05
-events/defects
'assume that 5%of all breakdowns cause events
SEN fraction=0.03
-Notifications/events
- that 3%of all events cause SENs to be generated
SEN rate=Events*SEN fraction
-Notifications/Week
-This is the rate at which SENs are generated
SOER fraction=0.30
-dimensionless
--assume that 30% of all SENS cause SOERS to be written
SOER generation delay= 12
-Week
-assume that it takes 3 months for a SOER investigation to be
completed and for it to be written up.
SOER generation rate=SEN rate*SOER fraction/SOER generation
delay
-Notifications/Week
-This is the rate at which SOERs are generated and distributed to the
INPO members.
Work Backlog= INTEG (+backlog generation-Backlog fixing rate,
backlog generation*Avg time to fix defects)
-defects/utility
--This is the backlog of work that exists at any given point in time that
needs to be fixed.
.Control
Simulation Control Paramaters
FINAL TIME = 260
-Week
-The final time for the simulation.
INITIAL TIME = 0
-Week
-The initial time for the simulation.
SAVEPER = TIME STEP
-Week
-The frequency with which output is stored.
TIME STEP = 0.0625
-Week
-The time step for the simulation.
APPENDIX B
Observation effectiveness factor=INPO effect factor
-dimensionless
-This is the factor that represents the effect that INPO has on the
observation in the plant.
effective
observation
rate=(
i observation
rate
operational
+
SUM(Defect fix rate x[x defect mode!]))* Observation effectiveness
factor
-Observations /Week
-This is the way the effect of INPO is felt within the plant observation.
info prody factor x[FIREDRILL]= 1
info prody factor x[LICENSING]=1
info prody factor x[SUPPORT]= INPO effect factor
info prody factor x[MODIFICATION]= INPO effect factor
info prody factor x[INFORMATION]= INPO effect factor
- dimensionless
-This is the factor that represents the way in which INPO effects the
productivity of the engineers in performing various maintenance sector
tasks.
engineer productivity nominal x[x engineering work]= DD engineer
productivity nominal x[x engineering work] * engineer productivity
factor*info prody factor x[x engineering work]
-Works/ (Person*Week)
--The
average
[FIREDRILL,
productivity
of
LICENSING,M
INFORMATION, E OUTAGE]
engineers;
SUPPORT,
a
defined
variable
MODIFICATION,
INPO effect factor= max(1,(Effect of SEN rate(SEN rate) +Effect of
SOER rate(SOER generation rate)))
-dimensionless
-This is the dimensionless multiplier that represents the effect of INPO.
Effect of SOER rate( [(0,0) (10,2)],(0,0),(1,0.2),(2,0.4),(3,0.6),(4,0.8), (5,1),
(6,1.1),(7,1.2),(8,1.3),(9,1.4),(10,1.5))
-dimensionless
-This is the effect of the number of SOERs that are generated on the
Inspection Effectiveness.
Effect
of
SEN
rate(
[(0,0)(10,2)],(0,0),(0.41958,0.199288),
(1.14219,0.405694),(2.00466,0.576512),(2.86713,0.718861),(3.91608,0.875
445),(5,1.05),(6,1.15),(7,1.25),(8,1.35),(9,1.45),(10,1.5))
-dimensionless
-This is the effect of the number of SENs generated by INPO on the
Inspection Effectiveness.
Events under Investigation= INTEG (SEN rate-SOER generation rate,
SEN rate*SOER generation delay)
-Notifications
-This is the number of significant events that are reported to INPO
that are investigated further.
SEN fraction=0.015
-dimensionless
-assume that 1.5% of all events cause SENs to be generated
SEN rate=Total Industry Events*SEN fraction
'Notifications/Week
-This is the rate at which SENs are generated
SOER fraction=0.30
-dimensionless
-assume that 30% of all SENS cause SOERS to be written
SOER generation delay= 12
-Week
-assume that it takes 3 months for a SOER investigation to be
completed and for it to be written up.
SOER generation rate=SEN rate*SOER fraction/SOER generation
delay
- Notifications /Week
-This is the rate at which SOERs are generated and distributed to the
INPO members.
Events=Fraction Events*rate of equipment break down
-Notifications/Week*plant
-This is the number of events that occur at each plant.
Total Industry Events=Events*Industry participants
-Notifications/Week
-This is the total number of events that are reported by the industry to
INPO.
Industry participants= 110
-plant
-This is the total number of INPO members
.Control
Simulation Control Paramaters
FINAL TIME = 520
'Week
-The final time for the simulation.
INITIAL TIME = 0
-Week
-The initial time for the simulation.
SAVEPER = TIME STEP
-Week
-The frequency with which output is stored.
TIME STEP = 0.0625
-Week
-The time step for the simulation.
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