G. C. Montanari, A. Cavallini
Dip. Ingegneria Elettrica, University of Bologna viale Risorgimento 2, 40136 Bologna, Italy giancarlo.montanari@unibo.it
andrea.cavallini@unibo.it
M. Tozzi
TechImp HQ Srl via Toscana 11/c, 40069 Zola Predosa, Italy mtozzi@techimp.com

GLOBAL MONITORING IN SMART GRID
ENVIROMENTS
• Making MV-HV grids ready to be smart
• Diagnostic quantities
• Global Monitoring approach
• A case study
2

Smart Grids: what do they generally mean?
• Smart grid = smart metering + renewables ( distributed power generation)
• Low, sometimes medium, voltage
• Only this, indeed?
3

• E.U. vision: by far the most complete vision of SG
4

Smart Grids (EU)
• Reliability and quality
• Innovation and competitiveness
• Nature and wildlife preservation
• Low prices and efficiency
Electrical
Apparatus/Assets
• Monitoring Diagnostics
• Remote Control of
Assets and CBM
• Reliable (smart)
Insulation Systems
• Recyclable and New
Materials (nano)
5

• Let us review some basic concepts of power system asset management to see how they are related intimately to electrical appartus/insulation system monitoring
• The goal is to demonstrate that advanced monitoring tools can become the key for smart grid management
6

• Long-term AM (LTAM, yearly and beyond): Strategic planning, decide which assets need to be replaced (using which technologies) and grid expansion.
– Improved Materials & Manufacturing Techs
– Permanent diagnostics, operating conditions
• Mid-term AM (MTAM, from monthly to yearly):
Maintenance management: optimal maintenance strategy and optimal outage plan.
– Bulk diagnostic techniques (tand, pol/depol, dielectric spectroscopy, DGA) and local diag. techniques (PD) (on line)
7

• Short-term AM (STAM, from real-time to months/year): Operational management: secure and reliable operation of the system, system monitoring and control, fault restoration.
8

• With improved monitoring tools, the apparatus will be a source of information as, for instance:
– Apparatus condition
– Apparatus optimum operation
– Synchro-phasors
– Power Quality at the apparatus node
• Advanced communication tools will enable this info to be available to a broad number of operators for Operational management, Reliability evaluation, Security assessment,
Stability analysis, Load flow calculations, Power quality assessment
• This is smartening the grid
9

GLOBAL MONITORING IN SMART GRID
ENVIROMENTS
• Making MV-HV grids ready to be smart
• Diagnostic quantities
• Global Monitoring approach
• A case study
10

Diagnostic properties (Insulation Systems)
Two families of diagnostic quantities:
-Local quantities, related to defects (e.g. Partial Discharges, Hot spots)
-Global quantities, related to bulk degradation
EFFECTIVE DIAGNOSTIC TOOL (for localized defects)
Which properties to choose?
1) Those bringing the fastest ageing rate
2) Those providing info on the global state of an electrical apparatus decision on the most appropriate maintenance action
11

PD as an effective diagnostic tool
Partial discharges are at the same time CAUSE and CONSEQUENCE of insulation system degradation and provide often the fastest ageing mechanism (on organic materials)
EFFECTIVE DIAGNOSTIC TOOL (for localized defects)
Partial discharges occur when there are defects within the insulation system
PD = CONSEQUENCE
System degradation increases under Partial
Discharges action
PD = CAUSE
12

PD definition (IEC 60270)
• Partial discharge (PD): localized electrical discharge that only partially bridges the insulation between conductors and which can or can not occur adjacent to a conductor
• PD normally develop in air gaps or on insulation surfaces
13

Insulation Degradation
Partial Discharge activity
Insulation material erosion
Partial Discharge
Electrical tree in HV cable joint insulation
Complete Discharge:
Breakdown
HV electrode
Epoxy slab
Formation of treeing channels in a point-to-plane specimen
LV electrode
14

Diagnostic quantities and CBM
Effective maintenance: only at the right moment
DGA
Tandelta
Global
Diagnostics
Vibrations
Partial Discharge Analysis
Insulation condition
Insulation ageing
15

• For on-line monitoring, sensors are a key issue for: -) reliability -) sensitivity -) effectiveness of measurements and cost
• it is possible to design appropriate sensors for each apparatus and diagnostic quantity
• Regarding PD, it is possible to design the detector in order to use just one detector for all sensors (bandwidth).
16

Best Technical:
ONE detector FOR ALL
Assets.
The detector should be able to acquire PD data from all different sensors
• HF sensors
•Capacitive sensors
•Inductive sensors
•VHF sensors
•UHF sensors
•Acoustic sensors
17

Innovative approach to PD diagnosis:
Separation, Identification and Diagnosis (SID)
PD inference is the prerequisite for correct diagnosis
Separation Identification
S I
Diagnosis
D
• Noise rejection • Potential defect
• Source separation
..
harmfulness
..
(one source at a
..
• Maintenance program
..
time)
•
Life extension (trend of the weakest spots, .
time to end point)
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Pulses coming form different points have different T/F characteristics
A
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
Two PD pulses from sources at different distances from detection point
(broadband detection chain)
-0.020
0.0 100.0n
300.0n
500.0n
700.0n
900.0n
1.0u
Pulse Frequency Spectrum
5.5E-4
5.0E-4
4.5E-4
4.0E-4
3.5E-4
3.0E-4
2.5E-4
2.0E-4
1.5E-4
1.0E-4
5.0E-5
0.0E+0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Frequency [M Hz]
35.0
40.0
45.0
49.0
Pulses coming from close to the detection point:
Higher frequency content
B 0.010
0.008
0.006
0.004
0.002
0.000
-0.002
-0.004
-0.006
-0.008
-0.010
0.0 100.0n
300.0n
500.0n
700.0n
900.0n
1.0u
Pulse Frequency Spectrum
5.5E-4
5.0E-4
4.5E-4
4.0E-4
3.5E-4
3.0E-4
2.5E-4
2.0E-4
1.5E-4
1.0E-4
5.0E-5
0.0E+0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Frequency [M Hz]
35.0
40.0
45.0
49.0
Pulses coming far from the detection point:
Lower frequency content (due to attenuation)
19

Categorization induced by TF mapping
20

The concept of PD pattern
One PD event is a pulse having a large frequency content (from the
MHz to the GHz range)
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.0 100.0n
300.0n
500.0n
700.0n
900.0n
1.0u
During AC PD activity hundreds of pulses per second occur having different amplitude and phase !
Amplitude
40 ence
30
20
Freq
10
240
220
200
180
160
140
120
100
Magnit ude chan
80 nel
60
40
20
40
20
80
60
160
140
120
100
P
240
220
200
180 hase chan nel
Phase
•The PD pattern represents the density of discharges in the phase/magnitude plane.
•It is a 3-D histogram represented through color maps
Phase
21

SID
Separation, Identification and Diagnosis
Separation of pulse features
10
5
0
-5
-10
10
5
0
-5
-5
0
Feature #1
5
10
SEPARATION
MAP
ENTIRE ACQUISITION Feature #2
-10 -10
NOISE INTERNAL PD
22

• Different defects lead to different degradation rate in the insulation system
• To assess insulation condition it is necessary to investigate each PD source separately
• Basing diagnostics on a general level of PD, without any identification, may be misleading, since just the predominant phenomena will be taken into consideration…and the biggest one may not be the most dangerous
23

Example in rotating machine:
Bar to Bar (B2B) PD can be significantly larger than Slot
PD, but degradation rate associated to the slots may be faster
24

How to identify different PD types?
PD sources of the same nature give rise to similar PRPD Patterns.
-Internal PD HV
- Surface PD
HV
- Corona PD HV
25

Automatic identification (1): Fuzzy logic at work
Statistical marker evaluation
Fuzzy inference engine
Mixed stress-grading PD and microvoid activity in mediumvoltage motor.
Fuzzy ident: 87% surf, 13% internal
Next comes the same phenomenon recorded in a much more degraded machine 26

Automatic identification (2)
(V) 3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
0 45 90 135 180 225 270 315
Phase (deg)
360
Stress-grading PD clearly predominant in medium-voltage motor.
This time no fuzzyness in identification (100% surface, 0% internal)
Statistical marker evaluation
Fuzzy inference engine
27

Automatic identification: 3 rd ID level based on fuzzy logic
28

GLOBAL MONITORING IN SMART GRID
ENVIROMENTS
• Making MV-HV grids ready to be smart
• Diagnostic quantities
• Global Monitoring approach
• A case study
29

S
G
G
M
S
• A system that can correlate several diagnostic and operational quantities to achieve better condition evaluation
• Endowed with advanced connectivity and data processing (noise rejection, data compression, innovative detectors and sensors) tools
• Providing, in real time to SCADA centers, a valuable estimate of apparatus failure likelihood
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• Asset Condition Estimator (ACE).
– STAM, MTAM: quantities associated with localized defects where stress concentration often takes place: fastest mechanism for insulation failure
• PD
• Hot spots
– MTAM, LTAM: bulk aging, i.e., a generalized loss of electrical, mechanical and thermal properties of the system, besides PD and hot spots
• gas levels in oil
• Tand
• Conduction current
• PD, hot spot (e.g. Real Time Thermal Rating)
• Vibration signals.
31

• Operating Point Recorder (OPR): log data regarding
– Bus voltages and load currents (Synchro-phasors)
– Readings from temperature probes and/or fiber optic monitoring systems
– Environmental quantities .
• Note 1: random power flow fluctuations due to renewable sources: impact on insulation systems???
• Lots of operational data needed to correlate these fluctuations with failures.
• Note 2: what about power electronics repetitive pulses? And sporadic voltage transients? See next
32

• Power Quality Monitor (PQM).
– Harmonics: promote hot spot overheating, mechanical stress and enhance peak voltage levels.
• Intolerable when series or parallel resonances take place.
– Surge voltages and voltage dips can threat interturn insulation of transformers and motors
– External short circuits could affect the mechanical stability of transformer windings.
33

• Communication module
(COM).
– Software, database management and communication tools that allow enhanced data exchange between SGGMS and supervisory control and data acquisition
(SCADA) centers
Remote User
Internet
Web
Server
Database application
Central Unit
Web service
Diagnostic application
Thirdpart systems
Local
SCADA
Network
Data
Downloader
Acquisition
Unit #1
Acquisition
Unit #2
34

• When a permanent diagnostic monitoring system is installed, it could be useful to save all the data in a central server.
• Especially if more than one EUT are monitored, all the data can be saved in the server and collected in the DATABASE
• The DATABASE represents the history of the monitored system
EUT 1: generator EUT 2: transformer
EUT 3: HV cable
ACQUISITION BOX
PD/DP sensor
ACQUISITION BOX
PD/DP sensor
ACQUISITION BOX
PD/DP sensor
SERVER - DATABASE
35

The on line monitoring system is made up essentially of the following components:
 Sensors (one for each joint/terminal);
 Diagnostic units: (PD, Tandelta, DGA) one or more for each asset;
 Supervision & Control System (one for each complete circuit);
 Ethernet links between the detection units and the Supervision & Control
System
.
36

• One can open synoptic views to immediately and easily understand if, where and when any problem occurred during the monitoring session
37

• Simple synoptic visualization modes are available for any electrical apparatus, e.g. generators, transformers, cables and GIS
38

• One can see the trending associated to each sensor in each phase of each equipment.
• One can see the recorded data and patterns… one can play e.g. with the T-F map and set up PD alarms properly.
39

• Folders containing the stored data can be opened when alarms are raised and patterns associated with the PD activities (or what else among Diagnostic
Properties DP) can be seen immediately
40

• Advantages:
•
Centralized data storage for resource optimization;
•
Data comparison among electrical apparatuses of the same family or insulation technologies or within a single electrical apparatus (e.g. among different phases) or under different conditions (load, time, maintenance interventions);
•
Data trending allows the harmfulness level to be evaluated and threshold criteria to be fixed/modified;
•
Combined analysis of quantities other than PD (e.g. humidity, temperature, load, voltage transients, DGA);
•
Capability of customizing the alarms/warning decision trees depending on asset manager evaluation .
41

GLOBAL MONITORING IN SMART GRID
ENVIROMENTS
• Making MV-HV grids ready to be smart
• Diagnostic quantities
• Global Monitoring approach
• A case study
42

• A 250 MVA autotransformer experienced immediately after installation a significant increase of Hydrogen
• According to the IEC and IEEE specs, the level and the trend of H2 were critical after only few months. After one year the H2 level exceeded
1000 ppm
– Possible PD according to IEC60599 based on Duval Triangle
– Condition 2 according to IEEE C57.104: Exercise caution- Analyze for individual gases-Determine load dependence
• BUT:
– Is this gas increase actually due to thermal or electrical problem?
– Is the PD activity, if present, harmful or not?
– Which type of PD and where is this located?
– Which is the degradation rate?
– Which is the best action to be taken reducing costs and increasing reliability?
43

1. OIL TREATMENT
2. MONITOR PD+GAS+Bushing Tandelta before oil treatment and during Spring/Summer (most critical period)
SCOPE OF THE MONITORING SYSTEM INSTALLATION:
- MONITOR THE TRANSFORMER DURING A CRITICAL
PERIOD TO AVOID UNEXPECTED FAILURES
- ASSESS THE PD HARMFULNESS
- GIVE A PROBABILITY OF FAILURE WITHIN THE
GUARANTEE TIME
44

• PD
– UWB detector (16kHz-35 MHz)
– 6 sensors (Tap Adapters)
– Time -Frequency Map Separation algorithm
• DGA
– 2 Gas (H2,CO) + Moisture + Temperature
– Membrane technology/electrochemical sensors
• Bushing Tandelta/Capacitance
– Leakage Current
– Dissipation factor
– Insulation Resistance
45

GLOBAL MONITORING LAYOUT
1: Acquisition
Box
3
2
1
4
3: TD Sensor
2: Tap Adapter for both PD and TanD acquisition
4: DGA
46

• TWO PD phenomena were detected on-line:
– A sporadic activity due to small gas bubbles in the oil. This activity was intermittent.
Bubble PD
– A smaller, but persistent, activity detected in all the HV phases, identified as mixed internal/surface
PD.
• H2 level increase about 5 ppm/day
Surface/Internal PD
47

• The first activity, due to the bubbles, disappeared after the oil treatment.
• Second activity was still there, in all three phases at HV side
(230 kV)
Phase 4 Phase 8 Phase 12
48

Nw > 80
Qmax>500 mV
PD Trend
Phase 4
Qmax>250 mV
PD Trend
Phase 8
Nw > 100
Nw > 80
PD Trend
Phase 12
Qmax>300 mV
49

• Necessary to give COMBINED alarms and assess insulation condition
DGA DGA
PD
PD
• Meaningful trending! Not influenced by external disturbances or other PD!
50

Qmax Trend of Phase 4 without separation of Corona and Bubble from PD at interfaces
Corona
Bubbles
Possible False Alarm
PD PD
Bubbles PD + Corona Just PD
51

TF FILTERING: Smart Alarm setting
TF
FILTERING:
Trending evaluated only in this region of the map!!
CORONA + PD
CORONA
PD
52

1.2
1
0.8
0.6
0.4
0.2
0
1.2
1
0.8
0.6
0.4
0.2
0
Qmax 95%
Possible False Alarm due to External Corona
Qmax 95%
Threshold Alarm
Level
Trending without TF filtering
Qmax 95%
Threshold Alarm Level
Qmax 95%
Trending after having
TF filtered external corona
53

• No significant changes in bushing tandelta values were noted over the monitoring period (6 months)
• Polarity of detected PD indicated that PD source was not located inside the bushings.
• The H2 gas levels increased during the monitoring period with average rate of 5 ppm/day. No significant changes in the rate was noted.
CONSTANT RATE.
• PD activities were detected continuously for 6 months , demonstrating that gas increase was due to PD
• PRPD pattern investigations suggested that
– There are three defects: one each phase
– PD activity was generated by a constructional defect within the connection between the bushing and the winding leads. Location of the source was confirmed also by additional acoustic measurements
54

EXPECTED RESULTS OBTAINED RESULTS
MONITOR THE TRANSFORMER DURING A
CRITICAL PERIOD TO AVOID UNEXPECTED
FAILURES
ASSESS PD HARMFULNESS
GIVE A PROBABILITY OF FAILURE WITHIN THE
GUARANTEE TIME (1 YEAR)
• TRANSFORMER WAS MONITORED
CONTINUOUSLY AND NO CRITICAL CHANGES
IN PD TREND WAS NOTED.
• UNEXPECTED FAILURES DID NOT OCCUR.
• NO FALSE ALARMS WERE GENERATED
PD ARE NOT YET HARMFUL SINCE PD
TRENDING IS CONSTANT (amplitude alone is not the only parameter)
LOW PROBABILITY OF FAILURE IN ONE YEAR
IF TRANSFORMER OPERATED AT THE SAME
STRESS/CONDITIONS, CONSIDERING BOTH
THE MONITORING RESULTS AND
TRANSOFORMER HISTORY. BTW, PD INVOLVE
PAPER LAYERS AND CAN BECOME CRITICAL!
SUGGESTED ACTIONS: PLAN VISUAL INSPECTION AND MAINTENANCE ACTION IN THE
MOST CONVENIENT PERIOD (AUTUMN). MEANWHILE, MONITOR THE TRANSFOMER
UNTIL MAINTENANCE IS TAKEN
55

CONCLUSIONS
56

• Advantages of SGGMS:
– Trend evaluation – failure risk assessment
– Action (maintenance) planning
– Problems identified
– Proper and clever alerts activated
– Maintenance planning feasibility -> increase reliability with cost reduction
– Advanced connectivity
– Proper management of operation (load, availability)
• Smart Grid operations will profit of knowledge of the availability, reliability and operation capability of each electrical component of the grid.
57

1. Condition monitoring tools massively integrated in electrical apparatuses
2. Information extracted from the monitoring data stream in a smart way using, for instance, artificial intelligence techniques.
3. Bidirection information flow.
58