Artificial intelligence applications in electrical transmission and distribution systems protection / editors, Almoataz Y. Abdelaziz, Shady Hossam Eldeen Abdel Aleem, Anamika Yadav.

Format:

Book

Contributor:

Abdelaziz, Almoataz Youssef, editor.

Abdel Aleem, Shady H. E., editor.

Yadav, Anamika, editor.

Language:

English

Subjects (All):

Artificial intelligence--Industrial applications.

Artificial intelligence.

Electric power transmission--Data processing.

Electric power transmission.

Electric power systems--Protection.

Electric power systems.

Physical Description:

1 online resource (513 pages)

Edition:

First edition.

Place of Publication:

Boca Raton ; London ; New York : CRC Press, Taylor & Francis Group, 2022.

Summary:

"Artificial Intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems as AI-based schemes are fast, adaptive, and robust and are applicable without any knowledge of the system parameters. This book considers the application of AI methods for the protection of different types and topologies of transmission and distribution lines. It explains the latest pattern-recognition- based methods as applicable to detection, classification, and location of a fault in the transmission and distribution lines, and to manage smart power systems including all the pertinent aspects. Features: Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction, and estimation, exclusive to power system protection issues. Presents introduction to enhanced electricity system analysis using decision-making tools. Covers AI Applications in different protective relaying functions. Discusses Issues and challenges in the protection of transmission and distribution systems. Includes dedicated chapter on case studies, and applications. This book is aimed at Graduate students, Researchers and Professionals in Electrical Power System Protection, Stability, and Smart Grids"-- Provided by publisher.

Contents:

Intro

Half Title

Title Page

Copyright Page

Contents

Preface

Editors

Contributors

1. Application of Metaheuristic Algorithms in Various Aspects of Electrical Transmission and Systems Protection

1.1 Introduction

1.2 Mathematical Representation of Optimization Problem

1.3 Metaheuristic Algorithms

1.4 Optimal Relay Coordination

1.4.1 Formulation of Relay Coordination Problem

1.4.2 Illustrative Example

1.4.3 State of Research in Optimal Relay Coordination

1.5 Optimal PMU Placement

1.5.1 Formulation of PMU Placement Problem

1.5.2 Illustrative Example

1.5.3 State of Research in Problem of PMU Placement

1.6 Estimation of Fault Section on Distribution Network

1.6.1 Formulation of Fault Section Estimation Problem as an Optimization Problem

1.6.2 Illustrative Example

1.6.3 State of Research in Fault Section Estimation

1.7 Estimation of Fault Location on Transmission Lines

1.7.1 Formulation of Fault Location Estimation Problem as an Optimization Problem

1.7.2 Illustrative Example

1.7.3 State of Research in Fault Location Estimation

1.8 Conclusion

References

2. AI-Based Scheme for the Protection of Power Systems Networks Due to Incorporation of Distributed Generations

2.1 Introduction to Distributed Generation (DG)

2.1.1 What Is Distributed Generating (DG)?

2.1.2 Advantages of DG over Conventional Power Generation

2.1.3 Applications of DG

2.2 Impact of Integration of Distributed Generation on the Power System

2.3 Problems during DG Interconnection

2.3.1 Operating (Economic) Issues

2.3.2 Technical Issues

2.3.3 Protection/Safety Issues

2.4 Islanding (Formation of Electrical Island)

2.4.1 Power Quality Issue

2.4.2 Personnel Safety

2.4.3 Out of Synchronism Reclose

2.5 Islanding Detection

2.5.1 Remote Method.

2.5.2 Active Islanding Detection Method

2.5.3 Passive Islanding Detection Method

2.5.4 Hybrid Method of Islanding Detection

2.6 Application of Artificial Intelligence for Islanding Detection

2.6.1 Fuzzy Logic

2.6.2 Artificial Neural Network (ANN)

2.6.3 Machine Learning Classifier

2.7 Case Study of Classifier (Machine Learning)-Based Islanding Detection

2.7.1 Relevance Vector Machine

2.7.2 Simulation and Test Cases

2.7.3 Feature Vector Formation

2.7.4 Training of RVM Classifier

2.7.5 Result and Discussion

2.8 Protection Miscoordination due to DG Interconnection

2.8.1 Issue of Protection Miscoordination

2.8.2 Application of AI Technique for Restoration of Protection Coordination

2.9 Summary

3. An Intelligent Scheme for Classification of Shunt Faults Including Atypical Faults in Double-Circuit Transmission Line

3.1 Introduction

3.2 Description of an Indian Power System Network

3.3 Ensemble Tree Classifier (ETC) Model for Classification of CSFs, CCFs, and EVFs

3.3.1 Designing of Exclusive Data Sets

3.3.2 Discrete Wavelet Transform (DWT)

3.3.3 Bagged Decision Tree

3.3.4 Boosted Decision Tree

3.3.5 Training/Validation of Proposed ETC Model

3.4 Comparative Assessment of Proposed ETC Model Based Classifier Modules

3.5 Relative Assessment of Proposed Scheme with Other AI Technique-Based Fault Classification Schemes

3.6 Effect of Variation in Sampling Rate on Performance of Proposed Classification Scheme

3.7 Conclusion

Acknowledgments

4. An Artificial Intelligence-Based Detection and Classification of Faults on Transmission Lines

4.1 Introduction

4.2 The Basic Concepts of Distance Protection

4.2.1 Causes of Current Increase upon Fault Occurrence

4.2.2 Causes of Faults

4.2.3 Types of Faults.

4.2.4 Sources of Errors in Detection and Classification of Faults

4.2.5 Distance Relay MHO Characteristic

4.3 AI-Based Fault Diagnosis System

4.3.1 Training Data for Artificial Neural Network: (Input/Target) Pairs

4.3.2 Feed Forward Artificial Neural Network

4.3.2.1 Multi-Layer Perceptron Neural Network

4.3.2.2 Radial Basis Function Network

4.3.2.3 Chebyshev Neural Network

4.3.2.4 Probabilistic Neural Network as a Detailed Example of FFNN

4.3.3 Support Vector Machine as an Example of ML

4.3.4 Convolution Neural Network as an Example of DL

4.4 Conclusion

5. Intelligent Fault Location Schemes for Modern Power Systems

5.1 Introduction

5.2 Conventional Fault Location Review

5.2.1 Traveling Wave-Based Fault Locators

5.2.2 Impedance Measurement-Based Fault Locators

5.2.3 Requirements for Fault Location Process

5.3 AI-Based Fault Location Schemes

5.3.1 ANN-Based Fault Location Computation

5.3.2 FL-Based Fault Location Computation

5.3.3 GA-Based Fault Location Computation

5.3.4 WT-Based Fault Location Computation

5.4 Recent Trends in Distribution Network and Smart Grid Requirements

5.5 Smart Fault Location Techniques

5.5.1 Fault Indicators

5.5.2 Distributed Smart Meters

5.5.3 IoT for Data Collections

5.5.4 Unmanned Aerial Vehicles (Drones)

5.6 Concluding Remarks

6. An Integrated Approach for Fault Detection, Classification and Location in Medium Voltage Underground Cables

6.1 Introduction

6.2 Autoregressive Modeling

6.3 Extreme Learning Machine

6.3.1 Training Extreme Learning Machine

6.4 Integrated Approach of the Protection Scheme

6.5 Test System

6.5.1 Simulation Parameters for Training and Testing

6.6 Fault Detection

6.7 Fault Classification

6.8 Fault Location

6.9 Results and Discussion.

6.9.1 Comparative Evaluation

6.10 Summary

7. A New High Impedance Fault Detection Technique Using Deep Learning Neural Network

7.1 Introduction

7.2 Fault Model

7.3 The Proposed Deep Learning Approach

7.4 The Simulated Experiments and Discussions

7.5 Case Study

7.6 Conclusions

Appendix

8. AI-Based Scheme for the Protection of Multi-Terminal Transmission Lines

8.1 Introduction to Multi-Terminal Transmission Line

8.2 Need of a Multi-Terminal Transmission Line

8.2.1 Benefits of a Multi-Terminal Transmission Line

8.2.2 Limitations of a Multi-Terminal Transmission Line

8.2.3 Protection and Other Technical Issues with Multi-Terminal Transmission Line

8.3 Conventional Protection Schemes

8.3.1 Distance Protection Scheme

8.3.2 Current Differential Scheme

8.4 Advanced Multi-End Protection Schemes

8.4.1 Synchronized and Unsynchronized Measurement-Based Schemes

8.4.2 Fundamental and Transient Frequency-Based Schemes

8.4.2.1 Fundamental Frequency-Based Schemes

8.4.2.2 Transient Frequency-Based Schemes

8.5 AI or Knowledge-Based Schemes

8.5.1 ANN-Based Schemes

8.5.2 Fuzzy Interference Systems

8.5.3 Support Vector Machine-Based Schemes

8.6 Adaptive Protection Schemes

8.7 Conclusion

9. Data Mining-Based Protection Methodologies for Series Compensated Transmission Network

9.1 Introduction

9.2 Relaying Challenges in Series Compensated Transmission Network

9.2.1 Under- and Overreaching of Relays

9.2.2 Current and Voltage Inversion

9.2.3 Precarious Operation of MOV

9.2.4 Harmonics and Transients

9.3 Data Mining-Based Protection Mechanism

9.3.1 DWT and Non-Parametric ML (KNN) Based Fault Events Classification Scheme

9.3.2 DWT and Non-Parametric ML (SVM) Based Fault Events Classification Scheme.

9.3.3 DWT and Non-Parametric ML (PNN) Based Fault Events Classification Scheme

9.4 Feasibility and Competency Analysis

9.4.1 Transforming Fault Events Identification

9.5 Summary

10. AI-Based Protective Relaying Schemes for Transmission Line Compensated with FACTS Devices

10.1 Introduction

10.2 FACTS Technology

10.3 Protection Issues with FACTS Technology Integration

10.4 Overview of AI

10.5 AI-Based Application in FACTS-Compensated Transmission Line Protection

10.5.1 Training Data Collection and Processing

10.5.2 Training Algorithms

10.6 Conclusion and Perspectives

11. AI-Based PMUs Allocation for Protecting Transmission Lines

11.1 Introduction

11.2 Basics of PMUs and WAMS

11.2.1 Basic PMU Structure

11.2.2 PMU Placement Rules

11.2.3 PMU Placement Problem Formulation

11.2.3.1 Case #1: Base case

11.2.3.2 Case #2: Considering ZIBs

11.2.3.3 Case #3: Loss of a Single PMU

11.2.3.4 Case #4: Single Line Outage

11.3 Conventional Mathematical Techniques for PMUs Allocation

11.3.1 Exhaustive Search

11.3.2 Integer Programming

11.3.3 Integer Quadratic Programming

11.4 AI Application to PMUs Allocation

11.5 Case Study

11.5.1 IEEE 14-Bus System

11.5.1.1 Case #1: Base Case

11.5.1.2 Case #2: Considering ZIBs

11.5.1.3 Case #3: Loss of a Single PMU

11.5.1.4 Case #4: Single Line Outage

11.5.2 IEEE 30-Bus System

11.5.2.1 Case #1: Base Case

11.5.2.2 Case #2: Considering ZIBs

11.5.2.3 Case #3: Loss of a Single PMU

11.5.2.4 Case #4: Single Line Outage

11.6 Application of PMUs in Protecting Transmission Lines

12. An Expert System for Optimal Coordination of Directional Overcurrent Relays in Meshed Networks

12.1 Introduction

12.2 Importance of the ES and Its Objectives.

12.3 Problem Formulation of the Optimal Coordination of DOCR.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

0-367-55237-X

1-000-45459-2

9780367552374

OCLC:

1264474384