ADAPTIVE RELAYING

Submitted bySurabhi Vasudev

B110556EE

Objectives of Power System ProtectionSelectivitySpeedReliabilityStabilityAdequatenessSensitivityAdaptiveness

.

.

Development in Power System Relaying

.

Performance

1900 years 1960 1975 2000

Electromechanical Relays

Microprocessor-

Based Relays

(Digital)StaticRelays

Electronic

Circuits

Digital ICs(mP,DSP,ADC,)

Digital Proc. Algorithms Digital ICs

(mP,DSP,ADC, neuro-

ICfuzzy-IC)

AI-based Methods

Communication Facility

AI-Based Relays

(Intelligent)

Scope of the Study

AI Applications to Digital Protection like:Transmission Line Fault ClassificationDistance relayingMachine Winding ProtectionTransformer Differential ProtectionTransformer Fault Diagnosis

.

.

XX---Relay setting& coordination

---XXHIF detection

---XXTransformer fault diagnosis

--XXXTransformer differ. relaying

-X-XXMachine Winding Relaying

XXXXXDistance Relaying

-XXXXTL fault classification

Selectivity

SpeedSecurityDependability

Protection Area

Shortcomings of Conventional Protection Systems

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

.

.

Characteristics of Digital Relaying

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

multi-characteristic, complex algorithms.

Communication capability: enabling integration of protection & control.

Low cost: expecting lower prices. Concept: no significant change (smart

copy of conventional relays).

.

.

Motivation for AI-Based Protection

Enabling the introduction of new relaying concepts capable to design smarter, faster, and more reliable digital relays.

Examples of new concepts: integrated protection schemes, adaptive 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 Reasonin

g

Classification of AI Techniques

Expert System

Definition: Expert System is a computer program that uses knowledge and inference procedures to solve problems that are ordinarily solved through human expertise

.

.

Structure of Rule-Based Expert System

Knowledge Acquisition

Facility

Explanation Facility

User Interface

Knowledge Base

(Rules)Inference Engine

Data Base

(facts)

ANN ModelsANN Models

Feedback

Feedback

ConstructedConstructed TrainedTrained Nonlinear

Nonlinear

Adaptive ResonanceAdaptive

ResonanceHopfield

(recurrent)Hopfield

(recurrent)

LinearLinear

Kohonen(Self-

Organizing Map)

Kohonen(Self-

Organizing Map)

Unsupervised

Unsupervised

Supervised

SupervisedMLP

(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...

Defuzzification

Defuzzification

Fuzzy Inferenc

e

Fuzzy Inferenc

e

Inference methods:Max-Min

composition,Max-Average

comp., ..

Fuzzification

Fuzzification

Membership

functions

Input variable

s

Defuzzification methods:

Center of areaCenter of sums

Mean of Maxima,..

Output Decision

X1 is 20% BIG&

80% MEDIUM

Main Components of Fuzzy Logic Reasoning

Samples of 3-ph

Voltages & Currents

Filtered Samples

Simulation 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-aliasin

g

& other Filters

Anti-aliasin

g

& other Filters

Feature ExtractionFeature

Extraction

Training Set

Training Set

Testing Set

Testing 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 Scheme

Modules of Intelligent Transmission Line Relaying

Fault Detection

Fault Detection

Trip Signal

Data Processin

g

Data Processin

g

Transmission Line Fault

Identification

Transmission Line Fault

Identification

Direction Discrimination

Direction Discrimination

Fault Locatio

n

Fault Locatio

n

Arcing DetectionArcing

Detection

Faulted Phase

selection

Faulted Phase

selection

Fault Type Classificati

on

Fault Type Classificati

on

Decision MakingDecision Making

FeaturesV

I

Application 1

Transmission Line Fault Classification

Conventional schemes: cannot adapt to changing operating conditions, affected by noise& depend on DSP methods (at least 1-cycle).

Single-pole tripping/autorecloser SPAR requires the knowledge of faulted phase (on detecting SLG Single-pole tripping 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 classification (1-cycle).

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

Measures from two line ends.Implementation of a prototype for ANN-

based adaptive SPAR

Application 2:

Distance Relaying

Motivation

Changing the fault condition, particularly in the presence of DC offset in current waveform, as well as network changes lead to problems of underreach or overreach.

Conventional schemes suffer from their slow response.

AI Applications in Distance Relaying

Using ANN schemes with samples of V&I measured locally, while training ANN with faults inside and outside the protection zone.

Same approach but after pre-processing to get fundamental of V&I through half cycle DFT filter.

Combining conventional with AI: using ANN to estimate line impedance based on V&I samples so as to improve the speed of differential equation based algorithm.

AI Applications in Distance Relaying

Pattern Recognition is used to establish the operating characteristics of zone-I. The impedance plane is partitioned into 2 parts: normal and fault. Pre-classified records are used for training.

Application of adaptive distance relay using ANN,where the tripping impedance is adapted under varying operating conditions. Local measurements of V&I are used to estimate the power system condition.

Application 3:

Machine Winding ProtectionMotivation

If the generator is grounded by high impedance, detection of ground faults is not easy (fault current < relay setting).

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

DFT FilteringIn5

In6

In3

In4

In1

In2

Ia2 Ib

2

Ib

1

Ia

1

Ra

Ic

1

Ic

2

A

C

B

L-LANN

2

L-L-LANN

3

L-GANN

1OutputOutpu

tOutput

Iad(n) = Ia1(n)- Ia2(n)Iaa(n) = ( Ia2(n) + Ia1(n) )/2

Current ManipulatorIcd(n)

Ica(n)Ibd(n) Iba(n)

Iad(n) Iaa(n)

Sampling

Ib2(n)

Ic2(n)Ic1(n)Ia2(n)Ia1(n)

Ib1(n)

ANN-Based Generator Winding Fault Detection

Application 4:

Transformer Differential RelayingMotivation

Conventional differential relays may fail in discriminating between internal faults and other conditions (inrush current, over-excitation of core, CT saturation, CT ratio mismatch, external faults,..).

Detection of 2nd and 5th harmonics is not sufficient (harmonics may be generated during internal faults).

Multi-Criteria Differential Relay based on Self-Organizing Fuzzy Logic

One differential relay per phase.12 criteria are used and integrated by FL.Examples of criteria: (ID=differential current)

q1

q3

q4

q6

q1> highest expected inrush current

q3 < 10-15%

q4 > current for over-excitation

q6 < 30%

ID1

ID2/ID1

ID1

ID5/ID1

Definition

Criterion StatementSign

APPLICATION 5:Transformer Fault DiagnosisMotivation

Conventional methods, e.g., Dissolved Gas Analysis (DGA), suffers from imprecision & incompleteness.IEC/IEEE code for DGA relates the fault type to the ratios of gases; e.g.,IF (C2H2/C2H4 =0.1-3) AND (CH4/H2 < 0.1) AND (C2H4/C2H6 < 1) THEN (the fault is High energy partial discharges)

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

Each subspace is described by a fuzzy if-then rule based on the patterns of training set.

C2H4/C2H6

C2H2/C2H4

S M L

S

M

L

S

M

CH4/H2

L

CONCLUSION

The applications of Artificial Intelligence in the arena of Relaying employs the methods of ANN,ES and FL.Adaptiveness and smartness get highly improved by inculcating the AI methods into Conventional Relaying.

There is a great scope of exceptional developments in this arena ,hence imparting a smart outlook for the entire power system.

REFERENCESArtificial Intelligence Techniques in Power

Systems by K. Warwick, Arthur Ekwue, Raj Aggarwal, Institution of Electrical Engineers.

http://web.stanford.edu/class/cs227/Lectures/lec01.pdf

Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy logic  By Marian B. Gorzalczany