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Artificial IntelligenceApplications to

Power System Protection

ByHossam Eldin Abdallah Talaat

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System fault Diagn.Rotating Machines

Scope of the Study

Transmission 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

DirectionDiscrimination

AC/DCTransmission

Systems

Fault

Classification

DistanceRelaying

Series

CompensatedLine

6. Winding Protection

7. Incipient Fault Detect

Transformer

8. Differential Relaying

9. Fault Diagnosis

DifferentialRelaying

TransformerFault Diagnosis

Winding

Protection

Incipient FaultDetection

Out-of-Step

Protection

Faulted SectionEstimation

Relay Setting&Coordination

DistributionSystem

Protection

Alarm

Processing

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Reliability

Max servicecontinuity with min

system disconnection

Speed

Min fault time &equipment damage

Dependability

Ability to performcorrectly when

needed

Security

Ability to avoidunnecessary

operation

Objectives of Power

System Protection

Reliability

DependabilitySecurity

Simplicity Economy

SpeedSelectivity

Selectivity

Max servicecontinuity with min

system disconnection

Simplicity

Min equipment andcircuitry

Economy

Max performance atmin cost

Functional Requirements of Power System Protection

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Performance

1900 years 1960 1975 2000

Electromechanical Relays

Microprocessor-Based Relays

(Digital )

StaticRelays

ElectronicCircuits

Digital ICs( P,DSP,ADC,)

Digital Proc.Algorithms Digital ICs

( P,DSP,ADC,neuro-IC

fuzzy-IC)

AI-basedMethods

Communication Facility

AI-Based Relays

(Intelligent

)

Development in Power System Relaying

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Characteristics of Digital Relaying

Self-diagnosis : improving reliability.Programmability : multi-function, multi-

characteristic, complex algorithms.Communication capabili ty : enablingintegration of protection & control.

L ow cost : expecting lower prices.Concept : no significant change (smartcopy of conventional relays).

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XX---Relay setting& coordination---XX

HIF detection

---XXTransformer fault diagnosis--XXXTransformer differ. relaying-X-XXMachine Winding Relaying

XXXXXDistance Relaying-XXXXTL fault classification

selectivity Speed Security Dependability Protection Area

Shortcomings of Conventional Protection Systems

Key: - no problem, X some problems, XX big problems

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Motivation for AI-Based Protection

Enabling the introduction of new relayingconcepts capable to design smarter, faster,

and more reliable digital relays.Examples of new concepts: integratedprotection schemes, adaptive protection &

predictive protection.

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Artificial Intelligence(AI) Techniques

Expert System(ES)

Fuzzy Logic(FL)

ApproximateReasoning

ArtificialNeural

Network(ANN)

Symbolic KnowledgeRepresentation

ComputationalKnowledge

Representation

ExactReasoning

Classification of AI Techniques

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Characteristics of Artificial

Neural Networks (ANN)

o Network designusing trial & error (no.of layers, no. ofneurons in hiddenlayer, learning rate,etc.o Generation of largetraining set.

Powerful pattern classification. Optimization capabilities. Fast response. Fault tolerant (noise). Excellent generalization. Trend prediction. Good reliability.

DrawbacksAdvantages

MLP (Back-propagation): Classifiaction and NonlinearMapping

Kohonen (Self-organizing Map): Feature Extraction

Hopfield (Recurrent): Optimization

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Samples of 3-ph

Voltages & CurrentsFiltered SamplesSimulation

Environment

EMTP

Fault type,

location &duration

System model,parameters &

operatingconditions

PatternClassifier

Performance Evaluation

Anti-aliasing

& otherFilters

FeatureExtraction

Training Set

Testing Set

Classifier output

(training)

Pattern

Classifier

Training target

Classifierparameters

Training error

Testing target

Testing error

Classifier output

(testing)

Steps of Designing an AI-Based Protective Scheme

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Modules of Intelligent Transmission Line Relaying

FaultDetection

Trip Signal

DataProcessing

Transmission LineFault Identification

Direction

Discrimination

Fault

Location

ArcingDetection

Faulted Phase

selection

Fault TypeClassification

Decision Making

FeaturesV

I

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Application 1

Transmission Line Fault Classification

Conventional schemes: cannot adapt tochanging operating condtions, affected by

noise& depend on DSP methods (at least 1-cycle).Single-pole tripping/autorecloser SPAR

requires the knowledge of faulted phase (ondetecting SLG Single-pole tripping isinitiated, on detecting arcing fault recloser is

initiated).

Motivation

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ANN4

20-15-10-1

ANN130-20-15-11 Control Logic

Arcingfaultphase-T

1/4 cycleeach(5 samples)

VR ,V S,V T

IR ,I S,I T

ANN320-15-10-1

Decision

K NOWL

EDGE

BASE

Onecycleeach(20

samples)

VS

VT

VR

Arcingfaultphase-S

Arcingfaultphase-R

ANN220-15-10-1

Enabling Signals

Fault Type

RST

RG

Transmission Line Relaying Scheme

45000training

patterns

5-7 ms

25 ms

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Other AI Applications

Fuzzy & fuzzy-neuro classifiers used for fault typeclassification (1-cycle).

Pre-processing: 1- Changes in V&I,2- FFT to obtain fundamental V&I,3- Energy contained in 6 high freq. bands obtainedfrom FFT of 3-ph voltage.

Measures from two line ends.Implementation of a prototype for ANN-basedadaptive SPAR relay using transputer system

(T800).

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Application 2:

Distance Relaying

Motivation

Changing the fault condition, particularly inthe presence of DC offset in currentwaveform, as well as network changes leadto problems of underreach or overreach.Conventional schemes suffer from theirslow response.

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AI Applications in Distance Relaying

Using ANN schemes with samples of V&Imeasured locally, while training ANN withfaults inside and outside the protection zone.

Same approach but after pre-processing to getfundamental of V&I through half cycle DFTfilter.

Combining conventional with AI: using ANNto estimate line impedance based on V&Isamples so as to improve the speed ofdifferential equation based algorithm.

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AI Applications in Distance Relaying

Pattern Recognition is used to establish theoperating characteristics of zone-I. Theimpedance plane is partitioned into 2 parts:normal and fault. Pre-classified records areused for training.Application of adaptive distance relay usingANN,where the tripping impedance isadapted under varying operating conditions.Local measurements of V&I are used toestimate the power system condition.

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Application 3:

Machine Winding Protection

MotivationIf the generator is grounded byhigh impedance, detection ofground faults is not easy (faultcurrent < relay setting).

Conventional algorithms sufferfrom poor reliability and low speed(1-cycle).

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DFT Filtering

In5 In6In3 In4In1 In2

Ia2 I b2Ib1I a1

R a

I c1 I c2

A

C

B

L-LANN2

L-L-LANN3

L-GANN1

OutputOutputOutput

Iad(n) = I a1(n)- I a2(n)Iaa(n) = ( I a2(n) + I a1(n) )/2

ANN-Based Generator Winding Fault Detection

Current Manipulator

Icd(n) Ica(n)I bd(n) I ba(n)Iad(n) Iaa(n)

Sampling

I b2 (n) Ic2(n)Ic1(n) I a2(n)Ia1(n) I b1(n)

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Hardware Implementation

Fuzzy Processors:Siemens SAE81C99: 256/128 I/O, 16384rules, 10 M fuzzy logic instruction per sec.

Siemens SAE81C991: 4096/1024 I/O,131072 rules, 10 M FL instruction per sec.

Neuro-Processors:

Analog or Digital implementation but notyet commercialized.Example: 1000 neuron, 1M synapses,

1.37M connection per sec.

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Hardware Implementation

Advanced Communication Systems:Synchronized sampling can be obtained at0.2-0.5ms using Global Positioning

Systems (GPS) satellite.

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CONCLUSIONS

Expert Systems of system fault diagnosis andrelay coordination has been practicallyImplemented.

Some prototypes of ANN-based relays have been implemented and tested using laboratorysetups.

Major problem facing the practical applicationof AI-based relays is the generation of training

patterns from comprehensive computer

simulation.

Application7:

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Setting and coordination of relays incomplex power networks requires computer

aids especially for meshed networks.The problem is non-algorithmic, i.e.,application of expert system ES is needed.

Application 7:

Relays Setting & CoordinationMotivation

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Formation of

Primary/ BackupPairs Rules

Loop

EnumerationRules

Break

PointsRules

RelativeSequence Vector

Rules

Set ofSequential Pairs

Rules

Setting andCoordination

Rules

Facts

Control Rules

InferenceEngine

Agenda

1314

1817

2211

20

21

19

12

16

15

2423

1

43

52

Expert System for Setting& Coordination of Distance Relays

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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 timeunit (0.2 s)

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Structure of Rule-Based Expert System

KnowledgeAcquisition

Facility

ExplanationFacility

User Interface

KnowledgeBase

(Rules)Inference Engine

DataBase

(facts)

Definition: Expert Systemis a computer programthat uses knowledge andinference procedures tosolve problems that are

ordinarily solved throughhuman expertise

Cl ifi i f ANN M d l

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ANN Models

Feedback

Constructed Trained Nonlinear

AdaptiveResonance

Hopfield(recurrent)

Linear

Kohonen

(Self-Organizing

Map)

Unsupervised Supervised

MLP

(Back-Propagation

FeedForward

Classification of ANN Models

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Fuzzy If-Then Rules

If X1 is BIG and X2 is SMALLThen Y is ON,

If X1 is BIG and X2 is BIGThen Y is OFF.

..

DefuzzificationFuzzyInference

Inference methods:Max-Min composition,Max-Average comp., ..

Fuzzification

Membershipfunctions

Inputvariables

Defuzzificationmethods:

Center of areaCenter of sums

Mean of Maxima,..

OutputDecision

X1 is 20% BIG&

80% MEDIUM

Main Components of Fuzzy Logic Reasoning

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MEASUR

I NG

U NIT

V

VV

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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

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