Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Adaptive relaying

Presente

  • Sé el primero en comentar

Adaptive relaying

  1. 1. Submitted by Surabhi Vasudev B110556EE
  2. 2. Objectives of Power System Protection  Selectivity  Speed  Reliability  Stability  Adequateness  Sensitivity  Adaptiveness
  3. 3. .  . Development in Power System Relaying . Performance 1900 years 1960 1975 2000 Electromechanical Relays Microprocesso r- Based Relays (Digital) Static Relays Electroni c Circuits Digital ICs (mP,DSP,ADC,) Digital Proc. Algorithms Digital ICs (mP,DSP,ADC, neuro-IC fuzzy-IC) AI-based Methods Communication Facility AI-Based Relays (Intelligent)
  4. 4. Scope of the Study AI Applications to Digital Protection like:  Transmission Line Fault Classification  Distance relaying  Machine Winding Protection  Transformer Differential Protection  Transformer Fault Diagnosis
  5. 5. .  . XX---Relay setting& coordination ---XXHIF detection ---XXTransformer fault diagnosis --XXXTransformer differ. relaying -X-XX Machine Winding Relaying XXXXXDistance Relaying -XXXXTL fault classification SelectivitySpeedSecurityDependabilityProtection Area Shortcomings of Conventional Protection Systems Key: “-” no problem, “X” some problems, “XX” big problems
  6. 6. .  . 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).
  7. 7. .  . 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.
  8. 8. .  . Artificial Intelligence (AI) Techniques Expert System (ES) Fuzzy Logic (FL) Approximate Reasoning Artificial Neural Network (ANN) Symbolic Knowledge Representation Computational Knowledge Representation Exact Reasoning Classification of AI Techniques
  9. 9. 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
  10. 10. .  . Structure of Rule-Based Expert System Knowledge Acquisition Facility Explanation Facility User Interface Knowledge Base (Rules) Inference Engine Data Base (facts)
  11. 11. ANN Models Feedback Constructed Trained Nonlinear Adaptive Resonance Hopfield (recurrent) Linear Kohonen (Self- Organizing Map) Unsupervised Supervised MLP (Back- Propagation Feed Forward Classification of ANN Models
  12. 12. 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. .. DefuzzificationFuzzy Inference Inference methods: Max-Min composition, Max-Average comp., .. Fuzzification Membershi p functions Input variables Defuzzification methods: Center of area Center of sums Mean of Maxima,.. Output Decision X1 is 20% BIG& 80% MEDIUM Main Components of Fuzzy Logic Reasoning
  13. 13. Samples of 3-ph Voltages & Currents Filtered Samples Simulation Environment “EMTP” Fault type, location & duration System model, parameters & operating conditions Pattern Classifier Performance Evaluation Anti- aliasing & other Filters Feature Extraction Training Set Testing Set Classifier output (training) Pattern Classifier Training target Classifier parameters Training error Testing target Testing error Classifier output (testing) Steps of Designing an AI-Based Protective Scheme
  14. 14. Modules of Intelligent Transmission Line Relaying Fault Detection Trip Signal Data Processing Transmission Line Fault Identification Direction Discrimination Fault Location Arcing Detection Faulted Phase selection Fault Type Classification Decision Making Features V I
  15. 15. 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
  16. 16. ANN4 20-15-10-1 ANN1 30-20-15-11 Control Logic Arcing fault phase-T 1/4 cycle each (5 samples) VR,VS,VT IR,IS,IT ANN3 20-15-10-1 Decision K N O W LE D G E B AS E One cycle each (20 samples) VS VT VR Arcing fault phase-S Arcing fault phase-R ANN2 20-15-10-1 Enabling Signals Fault Type RST RG Transmission Line Relaying Scheme 45000 training patterns 5-7 ms 25 ms
  17. 17. RG SG TG RS ST TR RSG STG TRG RST Normal 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
  18. 18. 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
  19. 19. 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.
  20. 20. 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.
  21. 21. 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.
  22. 22. Application 3: Machine Winding Protection Motivation  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).
  23. 23. DFT Filtering In5 In6In3 In4In1 In2 Ia2 Ib2Ib1Ia1 Ra Ic1 Ic2 A C B L-L ANN2 L-L-L ANN3 L-G ANN1 OutputOutputOutput Iad(n) = Ia1(n)- Ia2(n) Iaa(n) = ( Ia2(n) + Ia1(n) )/2 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) ANN-Based Generator Winding Fault Detection
  24. 24. Application 4: Transformer Differential Relaying Motivation  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).
  25. 25. 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
  26. 26. APPLICATION 5: Transformer Fault Diagnosis Motivation 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)
  27. 27. 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
  28. 28. 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
  29. 29. 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.
  30. 30. REFERENCES  Artificial 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

    Sé el primero en comentar

    Inicia sesión para ver los comentarios

  • SrikanthYearuva

    Sep. 13, 2017
  • seemaN9

    Feb. 10, 2019

Presente

Vistas

Total de vistas

1.982

En Slideshare

0

De embebidos

0

Número de embebidos

16

Acciones

Descargas

81

Compartidos

0

Comentarios

0

Me gusta

2

×