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AI Based Relaying
Citation preview
Artificial Intelligence Artificial Intelligence Applications toApplications to
Digital Protection Digital Protection
ByBy
Hossam Eldin Abdallah Hossam Eldin Abdallah TalaatTalaat
System fault Diagn.System fault Diagn.Rotating MachinesRotating Machines
Scope of the Study
Transmission LineTransmission Line
1. Fault Classification
5. AC/DC Transmission
2. Direction Discriminat
3. Distance Relaying
4. Series Compensated l. 12. Distribution Protect.
13. Out of Step Protect.
14. Relay Setting& Coor
10. Alarm Processing
11. Faulted Section Est.
AI Applications to Digital Protection
Direction Discrimination
AC/DC Transmission
Systems
Fault ClassificationDistance
RelayingSeries Compensated
Line
6. Winding Protection
7. Incipient Fault Detect
TransformerTransformer
8. Differential Relaying
9. Fault Diagnosis
Differential Relaying
Transformer Fault Diagnosis
Winding ProtectionIncipient Fault Detection
Out-of-Step Protection
Faulted Section Estimation
Relay Setting& Coordination
Distribution System
Protection
Alarm Processing
Methodology
Motivation of applying AI (Problems).Motivation of applying AI (Problems). Detailed description of a selected application.Detailed description of a selected application. Other AI Applications: differences & additional Other AI Applications: differences & additional
features.features. Summary of attributes of all AI applications: Summary of attributes of all AI applications:
(ref#, functions, AI technique, Input features, (ref#, functions, AI technique, Input features, pre-processing & drawbacks).pre-processing & drawbacks).
Discussion.Discussion.
For each protection area:
Reliability
Max service continuity with min
system disconnection
Reliability
Max service continuity with min
system disconnection
Speed
Min fault time & equipment damage
Speed
Min fault time & equipment damage
Dependability
Ability to perform correctly when
needed
Dependability
Ability to perform correctly when
needed
Security
Ability to avoid unnecessary
operation
Security
Ability to avoid unnecessary
operation
Objectives of Power System Protection
Objectives of Power System Protection
ReliabilityReliability
DependabilityDependabilitySecuritySecurity
SimplicitySimplicity EconomyEconomy
SpeedSpeedSelectivitySelectivitySelectivity
Max service continuity with min
system disconnection
Selectivity
Max service continuity with min
system disconnectionSimplicity
Min equipment and circuitry
Simplicity
Min equipment and circuitry
Economy
Max performance at min cost
Economy
Max performance at min cost
Functional Requirements of Power System Protection
Performance
1900 years 1960 1975 2000
Electromechanical Relays
Microprocessor-Based Relays
(Digital)
StaticRelays
Electronic Circuits
Digital ICs(P,DSP,ADC,)
Digital Proc. Algorithms Digital ICs
(P,DSP,ADC, neuro-ICfuzzy-IC)
AI-based Methods
Communication Facility
AI-Based Relays(Intelligent)
Development in Power System Relaying
Characteristics of Digital Relaying
Self-diagnosisSelf-diagnosis: improving reliability.: improving reliability. ProgrammabilityProgrammability: multi-function, multi-: multi-function, multi-
characteristic, complex algorithms.characteristic, complex algorithms. Communication capabilityCommunication capability: enabling : enabling
integration of protection & control.integration of protection & control. Low costLow cost: expecting lower prices. : expecting lower prices. ConceptConcept: no significant change (smart : no significant change (smart
copy of conventional relays).copy of conventional relays).
XXXX------Relay setting& coordinationRelay setting& coordination
------XXXXHIF detectionHIF detection
------XXXXTransformer fault diagnosisTransformer fault diagnosis
----XXXXXXTransformer differ. relayingTransformer differ. relaying
--XX--XXXXMachine Winding RelayingMachine Winding Relaying
XXXXXXXXXXDistance RelayingDistance Relaying
--XXXXXXXXTL fault classificationTL fault classification
selectivityselectivitySpeedSpeedSecuritySecurityDependabilityDependabilityProtection AreaProtection Area
Shortcomings of Conventional Protection Systems
Key: “-” no problem, “X” some problems, “XX” big problems
Motivation for AI-Based Protection
Enabling the introduction of new relaying Enabling the introduction of new relaying concepts capable to design smarter, faster, concepts capable to design smarter, faster, and more reliable digital relays.and more reliable digital relays.
Examples of new concepts: integrated Examples of new concepts: integrated protection schemes, adaptive protection & protection schemes, adaptive protection & predictive protection.predictive protection.
Artificial Intelligence (AI) Techniques
Artificial Intelligence (AI) Techniques
Expert System (ES)
Expert System (ES)
Fuzzy Logic (FL)
Fuzzy Logic (FL)
Approximate Reasoning
Artificial Neural
Network (ANN)
Artificial Neural
Network (ANN)
Symbolic Knowledge Representation
Symbolic Knowledge Representation
Computational Knowledge
Representation
Exact Reasoning
Classification of AI Techniques
Characteristics of Expert Systems
o Lack of informationLack of informationo Brittleness (noise)Brittleness (noise)o Expertise-based shortcomingsExpertise-based shortcomingso Expert-based shortcomingsExpert-based shortcomings
• AvailabilityAvailability• ComprehensivenessComprehensiveness• GeneralizationGeneralization• ExplanationExplanation• User friendly interfaceUser friendly interface
DrawbacksDrawbacksAdavantagesAdavantages
Characteristics of Artificial Neural Networks (ANN)
o Network design Network design using trial & error (no. using trial & error (no. of layers, no. of of layers, no. of neurons in hidden neurons in hidden layer, learning rate, layer, learning rate, etc.etc.o Generation of large Generation of large training set. training set.
• Powerful pattern classification.Powerful pattern classification.• Optimization capabilities.Optimization capabilities.• Fast response.Fast response.• Fault tolerant (noise).Fault tolerant (noise).• Excellent generalization.Excellent generalization.• Trend prediction.Trend prediction.• Good reliability.Good reliability.
DrawbacksDrawbacksAdvantagesAdvantages
MLP (Back-propagation): Classifiaction and Nonlinear Mapping
Kohonen (Self-organizing Map): Feature Extraction
Hopfield (Recurrent): Optimization
Samples of 3-ph
Voltages & CurrentsFiltered SamplesSimulation
Environment“EMTP”
Simulation Environment
“EMTP”
Fault type, location & duration
Fault type, location & duration
System model, parameters &
operating conditions
System model, parameters &
operating conditions
Pattern ClassifierPattern
Classifier
Performance EvaluationPerformance Evaluation
Anti-aliasing
& other Filters
Anti-aliasing
& other Filters
Feature ExtractionFeature
Extraction
Training SetTraining Set
Testing SetTesting Set
Classifier output
(training)
Pattern ClassifierPattern
Classifier
Training target
Classifier parameters
Training error
Testing target
Testing error
Classifier output
(testing)
Steps of Designing an AI-Based Protective SchemeSteps of Designing an AI-Based Protective Scheme
Modules of Intelligent Transmission Line Relaying
Fault Detection
Fault Detection
Trip Signal
Data Processing
Data Processing
Transmission Line Fault Identification
Transmission Line Fault Identification
Direction Discrimination
Direction Discrimination
Fault Location
Fault Location
Arcing Detection
Arcing Detection
Faulted Phase selection
Faulted Phase selection
Fault Type Classification
Fault Type Classification
Decision MakingDecision Making
FeaturesV
I
Application 1
Transmission Line Fault Classification
Conventional schemes: cannot adapt to Conventional schemes: cannot adapt to changing operating condtions, affected by changing operating condtions, affected by noise& depend on DSP methods (at least 1-noise& depend on DSP methods (at least 1-cycle).cycle).
Single-pole tripping/autorecloser SPAR Single-pole tripping/autorecloser SPAR requires the knowledge of faulted phase (on requires the knowledge of faulted phase (on detecting SLG Single-pole tripping is initiated, detecting SLG Single-pole tripping is initiated, on detecting arcing fault recloser is initiated).on detecting arcing fault recloser is initiated).
Motivation
ANN420-15-10-1
ANN130-20-15-11 Control Logic
Arcing faultphase-T
1/4 cycle each
(5 samples)
VR,VS,VT
IR,IS,IT
ANN320-15-10-1
Decision
KNOWLEDGE
BASE
One cycle each(20
samples)
VS
VT
VR
Arcing faultphase-S
Arcing faultphase-R
ANN220-15-10-1
Enabling Signals
Fault Type
RST
RG
Transmission Line Relaying Scheme
45000 training patterns
5-7 ms
25 ms
RGSGTGRSSTTRRSGSTGTRGRSTNormal
Input Layer
Hidden Layer 1
Output Layer
(11 )
VR(k)
IR(k)
VS(k)IS(k)
VT(k)IT(k)
VT(k-4)
IT(k-4)
.
.
.
.
.
.
Hidden Layer 2(15 )
(20 )
(30 )
Input voltage
¤t samples
Detailed Topology of ANN1
Other AI Applications
Fuzzy & fuzzy-neuro classifiers used for fault type Fuzzy & fuzzy-neuro classifiers used for fault type classification (1-cycle).classification (1-cycle).
Pre-processing: 1- Changes in V&I, Pre-processing: 1- Changes in V&I, 2- FFT to obtain fundamental V&I, 2- FFT to obtain fundamental V&I, 3- Energy contained in 6 high 3- Energy contained in 6 high freq. bands obtained from FFT of 3-ph voltage.freq. bands obtained from FFT of 3-ph voltage.
Measures from two line ends.Measures from two line ends. Implementation of a prototype for ANN-based Implementation of a prototype for ANN-based
adaptive SPAR relay using transputer system (T800).adaptive SPAR relay using transputer system (T800).
Application 2:
Distance RelayingDistance Relaying
MotivationMotivation
Changing the fault condition, particularly in Changing the fault condition, particularly in the presence of DC offset in current the presence of DC offset in current waveform, as well as network changes lead waveform, as well as network changes lead to problems of underreach or overreach.to problems of underreach or overreach.
Conventional schemes suffer from their Conventional schemes suffer from their slow response.slow response.
AI Applications in Distance RelayingAI Applications in Distance Relaying
Using ANN schemes with samples of V&I Using ANN schemes with samples of V&I measured locally, while training ANN with measured locally, while training ANN with faults inside and outside the protection zone.faults inside and outside the protection zone.
Same approach but after pre-processing to Same approach but after pre-processing to get fundamental of V&I through half cycle get fundamental of V&I through half cycle DFT filter.DFT filter.
Combining conventional with AI: using Combining conventional with AI: using ANN to estimate line impedance based on ANN to estimate line impedance based on V&I samples so as to improve the speed of V&I samples so as to improve the speed of differential equation based algorithm.differential equation based algorithm.
AI Applications in Distance Relaying AI Applications in Distance Relaying
Pattern Recognition is used to establish the Pattern Recognition is used to establish the operating characteristics of zone-I. The operating characteristics of zone-I. The impedance plane is partitioned into 2 parts: impedance plane is partitioned into 2 parts: normal and fault. Pre-classified records are normal and fault. Pre-classified records are used for training.used for training.
Application of adaptive distance relay using Application of adaptive distance relay using ANN,where the tripping impedance is ANN,where the tripping impedance is adapted under varying operating conditions. adapted under varying operating conditions. Local measurements of V&I are used to Local measurements of V&I are used to estimate the power system condition.estimate the power system condition.
Application 3::
Machine Winding ProtectionMachine Winding ProtectionMotivationMotivation
If the generator is grounded by If the generator is grounded by high impedance, detection of high impedance, detection of ground faults is not easy (fault ground faults is not easy (fault current < relay setting).current < relay setting).
Conventional algorithms suffer Conventional algorithms suffer from poor reliability and low speed from poor reliability and low speed (1-cycle).(1-cycle).
DFT Filtering
In5 In6In3 In4In1 In2
Ia2 Ib2Ib1Ia1
Ra
Ic1 Ic2
A
C
B
L-LANN2
L-L-LANN3
L-GANN1
OutputOutputOutput
Iad(n) = Ia1(n)- Ia2(n)Iaa(n) = ( Ia2(n) + Ia1(n) )/2
ANN-Based Generator Winding Fault Detection
Current Manipulator
Icd(n) Ica(n)Ibd(n) Iba(n)Iad(n) Iaa(n)
Sampling
Ib2(n) Ic2(n)Ic1(n) Ia2(n)Ia1(n) Ib1(n)
Application 4::
Transformer Differential RelayingTransformer Differential RelayingMotivationMotivation
Conventional differential relays may fail in Conventional differential relays may fail in discriminating between internal faults and discriminating between internal faults and other conditions (inrush current, over-other conditions (inrush current, over-excitation of core, CT saturation, CT ratio excitation of core, CT saturation, CT ratio mismatch, external faults,..).mismatch, external faults,..).
Detection of 2Detection of 2nd and 5 and 5th harmonics is not harmonics is not sufficient (harmonics may be generated sufficient (harmonics may be generated during internal faults).during internal faults).
Multi-Criteria Differential Relay based on Self-Organizing Fuzzy Logic
One differential relay per phase.One differential relay per phase. 12 criteria are used and integrated by FL.12 criteria are used and integrated by FL. Examples of criteria: (IExamples of criteria: (I=differential current)=differential current)
highest expected inrush current
current for over-excitation
< 30%
I1
I2/I1
I1
I5/I1
Definition Criterion StatementSign
MEASURING
UNIT
i1
v
i2CT
CT
CB
CB
Trip
3
1
W2
W3
2
FuzzificationWeighting
FactorsW1
3
2
1 1
Ruling-out the hypothesis of inrush current
3
10
2hypothesis of stationary overexcitation of a transformer
core
hypothesis of an external S.C. combined with CTs saturation
hypothesis of an external fault combined with ratios mismatch
4
4
7
12
MIN
>
Tripping threshold
Fuzzy Logic Based Multi-Criteria Differential Transformer Relay
3
1.0
0.120.08 0.16
3
0.0
Non-Inrush
Other AI ApplicationsOther AI Applications
ANN approaches with training using inrush current, ANN approaches with training using inrush current, external & internal faults.external & internal faults.
Input features: 3-ph current samples expressed as Input features: 3-ph current samples expressed as differential and retraining OR apply FFT to get differential and retraining OR apply FFT to get fundamental, 2fundamental, 2ndnd & 5 & 5thth harmonics. harmonics.
A prototype is implemented using DSP card with A prototype is implemented using DSP card with the objective of reconstruction of distorted CT the objective of reconstruction of distorted CT secondary current due to saturation. Tested on 50 secondary current due to saturation. Tested on 50 MVA plant transformer with time response 5-10 ms.MVA plant transformer with time response 5-10 ms.
Conventional methods, e.g., Dissolved Gas Conventional methods, e.g., Dissolved Gas Analysis (DGA), suffers from imprecision Analysis (DGA), suffers from imprecision & incompleteness.& incompleteness.
IEC/IEEE code for DGA relates the fault IEC/IEEE code for DGA relates the fault type to the ratios of gases; e.g.,type to the ratios of gases; e.g.,
IF (CIF (C22HH22/C/C22HH44 =0.1-3) AND (CH =0.1-3) AND (CH44/H/H22 < 0.1) AND < 0.1) AND
(C(C22HH44/C/C22HH66 < 1) THEN (the fault is High energy partial < 1) THEN (the fault is High energy partial
discharges)discharges)
APPLICATION 5APPLICATION 5::
Transformer Fault DiagnosisTransformer Fault DiagnosisMotivationMotivation
Diagnosis Results
Diagnosis Results
IEC/IEEE Transformer
DGA Criterion
Transformer Fault Diagnosis
System
Data Base of Dissolved Gas Test Records
Genetic Algorithm (GA)
Optimizer
Set up Membership Functions & Fuzzy Rules
Transformer Fault Diagnosis using GA-based Fuzzy Classification
C2H4/C2H6
C2H2/C2H4
S M L
S
M
L
S
M
CH4/H2
L
Each subspace is described by a fuzzy if-then rule based on the patterns of training set.
The enormous no of signals and alarms after a fault The enormous no of signals and alarms after a fault occurrence complicates the fault diagnosis process.occurrence complicates the fault diagnosis process.
ES versus ANN:ES versus ANN: ES is better for: large power systems and explanatory ES is better for: large power systems and explanatory
facility.facility. ANN is better for: noisy inputs, low cost and fast ANN is better for: noisy inputs, low cost and fast
response.response. Some practical implementations of ES: Wisconsin, Some practical implementations of ES: Wisconsin,
Taiwan and Portugal.Taiwan and Portugal.
Application 6Application 6::
Alarm ProcessingAlarm Processing
Hardware Implementation Fuzzy Processors:Fuzzy Processors: Siemens SAE81C99: 256/128 I/O, 16384 Siemens SAE81C99: 256/128 I/O, 16384
rules, 10 M fuzzy logic instruction per sec.rules, 10 M fuzzy logic instruction per sec. Siemens SAE81C991: 4096/1024 I/O, Siemens SAE81C991: 4096/1024 I/O,
131072 rules, 10 M FL instruction per sec.131072 rules, 10 M FL instruction per sec. Neuro-Processors:Neuro-Processors: Analog or Digital implementation but not Analog or Digital implementation but not
yet commercialized.yet commercialized. Example: 1000 neuron, 1M synapses, Example: 1000 neuron, 1M synapses,
1.37M connection per sec.1.37M connection per sec.
Hardware Implementation Advanced Communication Systems:Advanced Communication Systems: Synchronized sampling can be obtained at Synchronized sampling can be obtained at
0.2-0.5ms using Global Positioning 0.2-0.5ms using Global Positioning Systems (GPS) satellite. Systems (GPS) satellite.
CONCLUSIONS
Expert Systems of system fault diagnosis and Expert Systems of system fault diagnosis and relay coordination has been practically relay coordination has been practically Implemented.Implemented.
Some prototypes of ANN-based relays have been Some prototypes of ANN-based relays have been implemented and tested using laboratory setups.implemented and tested using laboratory setups.
Major problem facing the practical application of Major problem facing the practical application of AI-based relays is the generation of training AI-based relays is the generation of training patterns from comprehensive computer patterns from comprehensive computer simulation. simulation.
Setting and coordination of relays in Setting and coordination of relays in complex power networks requires computer complex power networks requires computer aids especially for meshed networks.aids especially for meshed networks.
The problem is non-algorithmic, i.e., The problem is non-algorithmic, i.e., application of expert system ES is needed.application of expert system ES is needed.
Application 7Application 7::
Relays Setting & CoordinationRelays Setting & CoordinationMotivationMotivation
Formation of Primary/ Backup
Pairs Rules
Loop Enumeration
Rules
Break Points Rules
Relative Sequence Vector
Rules
Set ofSequential Pairs
Rules
Setting and Coordination
Rules
FactsFacts
Control Rules
Inference Engine
Agenda
1314
1817
2211
20
21
19
12
16
15
2423
1
43
52
Expert System for Setting& Coordination of Distance Relays
loop 1 23 22loop 2 24 11 loop 3 11 21 18 16 14loop 4 23 21 18 16 14..loop 11 19 11 21loop 12 19 23 21
break-points 23 11 17 12break-points 23 11 15 12break-points 23 11 13 12chosen-B.P. 23 11 17 12
RSV 23 11 17 12 15 13 24 22 14 16 21
SSP 23 21 23 22SSP 11 24 11 21.SSP 21 23 21 11 21 13 21 16
loop 1 23 22loop 2 24 11 loop 3 11 21 18 16 14loop 4 23 21 18 16 14..loop 11 19 11 21loop 12 19 23 21
break-points 23 11 17 12break-points 23 11 15 12break-points 23 11 13 12chosen-B.P. 23 11 17 12
RSV 23 11 17 12 15 13 24 22 14 16 21
SSP 23 21 23 22SSP 11 24 11 21.SSP 21 23 21 11 21 13 21 16
Rule 3: Primary/Backup PairsIf Relay (R1) is located on line (L1) at bus (B1), AND Line (L1) is connected between bus (B1) & bus (B2), AND Relay (R2) is located on line (L2) at bus (B2); AND Line (L2) is not line (L1),THEN Relay (R1) acts as a buckup to relay (R2)
Rule 9: Zone-2 OverlapIf Relay (R1) is a buckup to relay (R2), AND Zone-2 setting for relay (R1) is (X12), AND Zone-1 setting for relay (R2) is (X21), AND Relay (R1) is located on line (L1) AND Line (L1) has a reactance equal (Xp), AND (X12-Xp) > (X21), AND Time delays of zone-2 of (R1) and zone-2 of (R2) are equal;THEN Increase time delay of zone-2 for relay (R1) by one grading time unit (0.2 s)
Rule 3: Primary/Backup PairsIf Relay (R1) is located on line (L1) at bus (B1), AND Line (L1) is connected between bus (B1) & bus (B2), AND Relay (R2) is located on line (L2) at bus (B2); AND Line (L2) is not line (L1),THEN Relay (R1) acts as a buckup to relay (R2)
Rule 9: Zone-2 OverlapIf Relay (R1) is a buckup to relay (R2), AND Zone-2 setting for relay (R1) is (X12), AND Zone-1 setting for relay (R2) is (X21), AND Relay (R1) is located on line (L1) AND Line (L1) has a reactance equal (Xp), AND (X12-Xp) > (X21), AND Time delays of zone-2 of (R1) and zone-2 of (R2) are equal;THEN Increase time delay of zone-2 for relay (R1) by one grading time unit (0.2 s)
Structure of Rule-Based Expert System
Knowledge Acquisition
Facility
Explanation Facility
User Interface
Knowledge Base
(Rules)
Inference EngineData Base
(facts)
Definition: Expert System is a computer program that uses knowledge and inference procedures to solve problems that are ordinarily solved through human expertise
ANN ModelsANN Models
FeedbackFeedback
ConstructedConstructed TrainedTrained NonlinearNonlinear
Adaptive Resonance
Adaptive Resonance
Hopfield(recurrent)
Hopfield(recurrent)
LinearLinear
Kohonen(Self-
Organizing Map)
Kohonen(Self-
Organizing Map)
UnsupervisedUnsupervised SupervisedSupervised
MLP(Back-
Propagation
MLP(Back-
Propagation
Feed Forward
Feed Forward
Classification of ANN Models
Fuzzy If-Then Rules
If X1 is BIG and X2 is SMALL Then Y is ON,
If X1 is BIG and X2 is BIG Then Y is OFF.
..
DefuzzificationDefuzzificationFuzzy Inference
Fuzzy Inference
Inference methods:Max-Min composition,Max-Average comp., ..
FuzzificationFuzzification
Membership functions
Input variables
Defuzzification methods:
Center of areaCenter of sums
Mean of Maxima,..
Output Decision
X1 is 20% BIG&
80% MEDIUM
Main Components of Fuzzy Logic Reasoning
R12
R55
R54
R53
R52
R51
R15
R14
R13
R11
R24 R44R34
R25 R35 R45
R23 R43R33
R22 R32 R42
R41R31R21
A1 A2 A3 A4 A5
A1
A2
A3
A4
A5
c- Fuzzy Rule-based Classification