بسم الله الرحمن الرحيم

بَر�ب حَر� حاشَر� للاشَر�َص�دَرىَص�دَرىىى َو�َو�أمَرأمَرلىلىَي�سَرَي�سَر

ىى

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

&current 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

MEASURING

UNIT

VV

V

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