Intelligent Systems for Power System Dynamic
Security Assessment: Review and Classification
Rui Zhang
Hong Kong Polytechnic
University
Hong Kong
rachelzhang.au@gmail.com
Yan Xu
Hong Kong Polytechnic
University
Hong Kong
ee.yanxu@polyu.edu.hk
Zhao Yang Dong
University of
Newcastle
Australia
zydong@ieee.org
Abstract—Intelligent System (IS) technologies have shown
encouraging potential in facilitating very fast power system
dynamic security assessment (DSA) in recent years. In general,
the development of the IS consists of four stages. This paper
conducts a comprehensive and detailed review on each stage and
classifies the methods involved in the stages. The classification
highlights the differences between the methods and show their
advantages and deficiencies. The value of this paper is that it can
serve as a useful and practical guideline to assist in developing
ISs for very fast DSA. Moreover, the proposed classifications can
provide deep insight into the enhancement of the IS towards
improved DSA performance.
Zhao Xu
Hong Kong Polytechnic
University
Hong Kong
eezhaoxu@polyu.edu.hk
techniques have also shown encouraging application potential
to very fast DSA [6]. The initial idea that uses pattern
recognition for security assessment dates back to as early as
1968 [7], while the first systematic investigation of artificial
intelligence (AI) to this area was performed in late 80s [8],
since then, various advanced AI techniques as well as machine
learning and data mining approaches have been tried to
develop very fast and “intelligent” DSA systems and
encouraging demonstration results have been obtained [7]-[31].
By directly capturing the mapping relationship between
system parameters (input) and the dynamic security status
(output) from a database, the DSA can be accomplished by the
ISs within second level. Additionally, the ISs can also extract
useful knowledge on system dynamic security characteristics
which can be used for security controls. Generally, the
development of the IS for DSA consists of four stages:
1) Dynamic security information database generation,
2) Input-output specification,
3) Knowledge extraction, and
4) Model validation.
The objective of this paper is to conduct a detailed review
on the four stages, and provide clear classifications to
distinguish the techniques/algorithms used in the stages. The
value of this work is that it can serve as a useful and practical
reference to assist in developing ISs for fast DSA, and to
provide insight into the enhancement of the IS scheme towards
improved performance.
The remaining part of the paper is organized as follows:
section II gives a general introduction of IS scheme for DSA,
section III to VI review the four stages in detail and proposes
classifications for each stage, section VII presents some
comments and discussions on the review and classification
results, and section VIII concludes the whole paper.
Keywords- power system, dynamic security assessment (DSA),
intelligent system (IS), review, guideline
I.
Ke Meng
Hong Kong Polytechnic
University
Hong Kong
eekemeng@polyu.edu.hk
INTRODUCTION
Dynamic security assessment (DSA) is the procedure to
determine if the power system can withstand imminent
disturbances (contingencies) without losing stability [1]. It’s
of great importance to the secure and reliable operations of the
power system. Historically, DSA is performed in off-line
based on enumerative forecasting and extensive contingency
simulations. However, since the early 90s, power systems
have seen many drastic changes, highlighted by open power
market and large connection to renewable energies which,
both have driven power system into an erratic and highly
unpredictable operating pattern and thereby made the
conventional off-line DSA practice inadequate and noneconomical. As a result, there has been a pressing need to
move DSA to real-time (or at least on-line), for continuously
monitoring system dynamic security conditions so as to reduce
the risk of dynamic insecurity which could lead to catastrophic
blackout.
At present, state-of-the-art technologies that enable realtime (or on-line) DSA can be divided into two categories:
i) Speeding up time-domain simulations by distributed
computing architecture and earlier termination [2], [3];
ii) Direct or hybrid DSA approaches, such as Transient
Energy Function (TEF)-based method [4], Extended Equal
Area Criterion (EEAC) method [5].
As a promising alternative, Intelligent System (IS)
II.
GENERAL INTRODUCTION OF INTELLIGENT SYSTEM
Power system dynamic security assessment (DSA) involves
the evaluation of the system dynamic behavior with respect to
the criteria of three stability categories, i.e. rotor angle
stability, voltage stability, and frequency stability, [1]. Since
power system in essence is a non-linear and time-varying
system, the exact evaluation of it dynamic behavior requires
This work is partially supported by an open grant project of the Intelligent
Electric Power Grid Key Laboratory of Sichuan Province of China.
978-1-4577-0365-2/11/$26.00 ©2011 IEEE
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the calculation of a large set of differential-algebraic equations
in the dynamic time frame (normally up to 10 s after the
disturbance(s) cleared). This process can be computationally
demanding since modern power system is highly dimensional
and the number of contingency to be considered is always
considerable.
As already mentioned, analytical approaches for DSA
include time-domain simulation and direct or hybrid methods,
they all rely on the mathematical formulation of the system
dynamic behavior, however, they can suffer from
insufficiently fast computation speed, conservative results, and
limited provision of additional “interesting” information on
system dynamic security characteristics. The advance of
Artificial Intelligence (AI), Machine Learning (ML), and Data
Mining (DM) techniques has provided another promising
scheme for very fast DSA. Typically, an Intelligent System (IS)
can be developed with these techniques, to directly capture the
mapping relationship between system parameters (input) and
the dynamic security conditions (output), and extract the
additional useful knowledge for extended dynamic security
analysis (e.g. preventive and corrective controls). During the
on-line application phase, once the input is fed, the output, i.e.
dynamic security status, can be determined within split second.
Consequently, it can significantly facilitate real-time DSA in
practice. In addition to the very fast computation speed, its
other advantages over analytical approaches include:
y Knowledge discovery, the IS can discover and extract
useful information on system dynamic security
characteristic, which are unseen by conventional
analytical approaches.
y Less data requirement, unlike analytical methods which
require an accurate and detailed description of the power
system, the IS only needs significant and/or available
input parameters to determine the stability condition of a
power system.
y Unified DSA capability, if properly developed, the IS
can be versatile to the three stability analysis tasks (rotor
angle, voltage, and frequency stability), provided the
problem complies with appropriate format.
Typically, to develop an IS for DSA, there are four stages as
shown in Fig.1.
characteristics is generated, the database should be accurate
and unbiased, and it should also be able to match practical
operating conditions which need to be evaluated. Secondly,
the input and the output of the IS is defined, i.e. which system
parameters should be used to determine the system security,
and how the security is described. Thirdly, the knowledge is
extracted from the database and is reformulated for on-line
application. Finally, a validation is performed on the
developed IS, to examine its accuracy, reliability, and
robustness, usually, refinement and/or correction are needed if
the IS is shown insufficiently good.
For each stage, in particular stage 2 and 3, there are
different techniques and algorithms can be adopted, which
however can lead to different DSA model and the practical
applications, this in turn will result in accuracy and reliability
requirements. In the subsequent sections of the paper, a
comprehensive review on each stage will be presented, and the
classification of distinct methods will be also given.
III.
DYNAMIC SECURITY INFORMATION DATABASE
GENERATION
The first stage of developing the IS is to generate a
sufficient database that contains the dynamic security
characteristic of the power system, i.e. dynamic security
information database. It is critical to the success of the IS
since it not only determines the DSA accuracy, but also has a
significant impact on the robustness of the IS. Normally, the
database can be constructed by historical DSA archives and
simulation, and it should cover a comprehensive range of
operating points (OPs), both typical and untypical conditions.
Generally, it can be classified into off-line and on-line classes
depending on the timing that database is generated.
In literature, most of the databases are generated in off-line,
based on a widely ranged set of operating conditions (varied
power flow profiles and network topologies) and contingency
simulations. By contrast, in [18], the authors proposed a
periodic updating scheme which is associated with an on-line
database generation. The idea is to produce database using online operating information, once the IS is found not perform
well on the new instances, the new database together with the
old one (off-line generated) will be used to update the IS. It
should recognize that the on-line database generation
combined with updating is advantageous, since it can catch the
real operating conditions, by which the robustness and
accuracy can be enhanced/maintained. However, it should
note that the on-line dynamic simulation remains computation
expensive, and the updating of IS can be also time-consuming
depending on the knowledge extraction algorithms, if the
computation time is too long there can hardly harvest practical
value [24].
IV.
Fig.1. Four stages of development of the IS for DSA.
INPUT AND OUTPUT SPECIFICATION
The second stage is to specify the input and output of the IS,
the use of different input-output can lead to distinct ISs in
terms of training and application.
Firstly, a data base that contains system dynamic security
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The input is the vector of system state parameters that
characterize the current system state, usually called feature,
they can be classified into pre- and post- fault features.
Pre-fault features are usually the steady-state parameters, in
general are power flow quantities [8]-[24]. They are basically
available in EMS, SCADA and PMUs. Post-fault features are
dynamic variables during/after the contingency. They can be
relative rotor angle, angular velocity, dynamic voltage
trajectory, and even synthetic parameters [25]-[31]. Besides,
the contingency can also be as one dimension of the input.
With pre-fault features, dynamic security can be assessed
before contingency comes, if current operating point is shown
unstable, operators can arm preventive controls, such as
generation rescheduling, to modify the current operating point
to avoid the risk of insecurity. This can ensure the power
system operates at a preventive state against anticipated
contingencies. However, it will incur expensive cost due to the
usually required shifting of power generation among
generators.
By contrast, when using post-fault features as input, DSA
can only be performed after contingency really occurs; and if
security is evaluated to be lost shortly, emergency controls
such as generator tripping and/or load shedding should be
activated immediately. This countermeasure brings no prepaid cost, but will lead to tremendous economic and social
cost if the removal of generators and/or load buses is
unavoidable. Besides, the use post-fault features requires a
certain response time to predict the stability, which has been
shown at least 1s after the fault is cleared [31]. This critical
time can be too long for operators to take timely remedial
actions to stop the very fast instability development (loss of
synchronism may arise within 150ms).
Besides, another difference between the two types of
features is their dependence on network topology, which
determines the flexibility of the database. Generally, pre-fault
features are much more dependent on the network structure
than that of post-fault topology.
However, no matter pre- or post- fault features, they can be
very large in dimension, especially for today’s increasingly
large-scale power systems. Consequently, a usual sub-step in
this stage is the feature selection, i.e. to select critical features
among the candidates as the input of the IS. Generally, feature
selection consists of selecting a subset from the whole input
set that are highly related to the output, by which the
irrelevant/redundant features can be removed and the size of
database can be significantly reduced, leading to a faster
training speed and higher accuracy [10]. Besides, by feature
selection one can identify significantly relevant features to
system dynamic security, and hence obtain insight on system
weak point, based on which the control strategies can be
designed to enhance system stability [21], [23]. In literature,
fisher discrimination [10], divergence criterion [11], principle
component analysis (PCA) [20] and wrapper approaches [14],
[21] have been adopted for systematically selecting features. A
comprehensive study and review on these feature selection
techniques and introduction of two alternative approaches is
presented in [35].
For the output, which is the parametric values representing
the dynamic security conditions, they can be classified into
numerical and nominal categories. The numerical output can
indicate the continuous security degree [20], so that the DSA
will be a regression problem. Alternatively, nominal output
can only represent the discrete security status, e.g.
secure/insecure [13], or levels of security [23], it makes the
DSA as a classification problem. Besides, it’s also noted that
the control actions can also be used as the output, to develop
the IS for dynamic security controls [32]. In [32], the
corrective control actions including load shedding, transformer
tap changer blocking, generator tripping and fast valving are
coded as the output.
V.
KNOWLEDGE EXTRACTION
The third and usually the core part in developing the IS is
the knowledge extraction stage. In this stage, the
learning/mining algorithms are used to extract the knowledge
on the mapping relationship between input and output, and
formulate the knowledge as a classifier (for classification) or
predictor (for regression).
According to different algorithms, the classifier/predictor
can be divided into rule- and blackbox- based models.
For rule-based IS, the input-output relation formulated by
the IS is interpretable by human, a famous example is
Decision Tree (DT), which represent the knowledge as the
decision-making splits and branches [8]. Besides, data mining
approaches can also provide transparent rules [16]-[17], [22][23], [29], Fuzzy techniques [29] can represent the rules with
complying human linguistic habits.
For blackbox-based IS, the input-output can’t be (or at least
very difficult to be) interpreted by human, the representative
example is Artificial Neural Network (ANN) [9], which
formulate the knowledge as a set of weights on the links of the
ANN nodes. The recently popular Supporting Vector Machine
(SVM) [13] may also belong to blackbox, although it makes
classification decisions according to the maximum-margin
hyperplane (classification boundary), the boundary is hardly
to be explicitly interpreted and utilized by human.
In terms of transparency and extended IS usage, the rulebased IS is evidently advantageous over blackbox-based IS,
and more important is that the interpretable knowledge can be
utilized to design dynamic security controls. In [33], the DT is
used to identify the stability region, based on which a
generation rescheduling scheme is developed to restore system
dynamic security. In [15], the decision-making branches of
DT are used to determine load shedding actions to restore
stability.
In addition to the interpretability of the IS, some other
factors in evaluating the intelligent algorithms are even more
important, including the accuracy, robustness, and training
time, etc.
The accuracy and robustness basically depends on the
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quality of the algorithms, generally, ANN has the excellent
non-linear modeling capability, but it can suffer from overfitting as the training samples increase; SVM, DT and data
mining approach on the other hand, may be limited to ceiling
accuracy. Currently, there is a trend to develop hybrid models
which make use of more than one algorithm/classifier to
improve the accuracy and robustness [28]-[30].
The training time/speed is another critical dimension of the
algorithms, since it determine the feasibility of the on-line
updating of the IS which is important to the on-line DSA
performance. Also, if the training speed is too low, the whole
computation efficiency of the IS can be reduced. Generally,
the training time depends on the size of the training data. And
typically, ANN and SVM is reported to need considerable
training time, DT is shown much faster than them [24]. In [24],
a superiorly fast IS for transient stability assessment is
developed using the recently proposed Extreme Learning
Machine (ELM) approaches [34], and it’s shown the IS needs
far less training time which enable efficiently on-line selfupdating.
It’s worth mentioning that in AI area, there is a popular
classification criterion known as supervised (such as ANN,
SVM, DT and the alike) and unsupervised learning algorithms
[36], depending on the use of prior knowledge (e.g. class
labels) during the knowledge extraction process. And the
difference between them is that unsupervised learning is
unbiased and objective in extracting the knowledge. Under
this criterion, it’s important to mention some data mining
approaches, such as Pattern Discovery (PD) method as utilized
in [21] and [22] belongs to unsupervised learning.
VI.
performance and robustness of the IS, on-line database
generation is preferable since it allows the on-line updating of
the IS. However, it’s important to point that the feasibility of
on-line updating also heavily depends on the computation
speed of the database generation and the (re)training of the IS.
For input definition, if the preventive DSA is required, prefault features should be used; if emergency DSA is needed,
post-fault features should be used. However, under the latter
situation, the requirement of accuracy and speed is much more
stringent since any misclassification or excessive response
time can lead to unable to predict the stability timely and/or
severe consequence.
For the output definition, the numerical values make the
knowledge extraction as a regression process, while nominal
values make it classification process.
For the knowledge extraction algorithms/techniques, the
accuracy, extrapolability, robustness, as well as training time
are critical factors to be considered. It has been shown that
hybrid models tend to result in higher quality performance
[28]-[30].
In practical development of DSA ISs, the researchers should
integrate all the factors into selecting the techniques for each
stage.
TABLE I CLASSIFICATION OF DYNAMIC SECURITY
ASSESSMENT INTELLIGENT SYSTEMS
Stage
Classification
Database generation
Off-line
On-line
Input definition
Pre-fault features
Post-fault features
Output definition
Numerical
Nominal
Knowledge extraction
Rules
Blackbox
MODEL VALIDATION
The last stage, validation, is to test the developed IS in
terms of accuracy, extrapolability, etc. Depending on the
output (nominal or numerical), the error rate or
misclassification rate can be used. In literature, some more
informative indices are also proposed to be used to test the
accuracy of ISs. For example, in [15], the so called “false
alarm rate” and “missed alarm rate” are employed; in [29], the
so called “misdetection”, “reliability”, “yield” are used.
We would like not to propose a classification for this stage
since it’s hard and meaningless to compare the performance
indices, but we herein would like to mention the testing
method, which is important to evaluate the IS
comprehensively. In literature, most of the researches examine
their ISs using only one testing data set, however, it may not
be sufficient. In [19], the authors propose to adopt v-folder
cross validation to measure accuracy and the over-fitting
performance of the IS, and it tends to be able to more
comprehensively grasp the IS accuracy.
VIII. CONCLUSION
Intelligent Systems have been identified as a promising
alternative of conventional dynamic security assessment
methods for large power system. In general, the development
of the IS for DSA comprises four stages, known as 1) Dynamic
security information database generation, 2) Input-output
definition, 3) Knowledge extraction, and 4) Model validation.
This paper conducts a comprehensive and detailed review on
each stage and classifies the methods used in the stages. The
classification highlights the differences between the methods
and show their advantages and deficiencies. The values of this
paper is that it can provide assistance in developing an practical
IS for fast DSA, moreover, it can provide deep insight into the
improvement of the IS for better DSA performance.
VII. DISCUSSION
The classification of the ISs for DSA is summarized in
Table I.
For the database generation stage, to enhance the on-line
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BIOGRAPHY
Rui Zhang obtained her diploma from Ngee Ann Polytechnic, Singapore, in
2005 and Bachelor degree from University of Queensland, Australia, in 2009,
both in electrical engineering. She was previously with Mawan Electric Power
Co. Ltd., China. Currently, she is with the “Intelligent Electric Power Grid
Key Laboratory of Sichuan Province” and Hong Kong Polytechnic University.
Her research interests include power system operation and control, intelligent
system applications to power engineering.
Yan Xu (S’10) obtained his B.Eng. degree from South China University of
Technology, Guangzhou, China, in 2008. Currently, He is a Ph.D. student at
Hong Kong Polytechnic University. His research interests include power
system stability and control, power system planning, renewable energy, and
intelligent system applications to power engineering.
Zhao Yang Dong (M’99, SM’06) obtained his Ph.D. degree from University
of Sydney, Australia in 1999. He is now Ausgrid(EnergyAustralia) Chair
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Professor and Director of the Centre for Intelligent Electricity Networks,
University of Newcastle, Australia. He also held academic and industrial
positions with Hong Kong Polytechnic University, University of Queensland,
Australia and Transend Networks, Tasmania, Australia. His research interest
includes power system planning, power system security, stability and control,
load modeling, electricity market, and computational intelligence and its
application in power engineering.
Zhao Xu (S’00-M’06) received his B.Eng., M.Eng, and Ph.D. degree from
Zhejiang University, China, in 1996, National University of Singapore,
Singapore, in 2002, and University of Queensland, Australia, in 2006,
respectively. From 2006–2009, he was an Assistant Professor and later an
Associate Professor at the Centre for Electric Technology, Technical
University of Denmark, Lyngby. Since 2010, he has been with the
Department of Electrical Engineering, Hong Kong Polytechnic University.
His research interests include demand side, electric vehicles to grid, grid
integration of wind power, electricity market planning and management, and
AI applications.
Ke Meng (M’10) received his Ph.D. degree from University of Queensland,
St. Lucia, Australia, in 2009. He is now a research fellow at the Department of
Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong
Kong. His research interest includes wind power, intelligent algorithms,
power system stability analysis and control.
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