Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection
- Length: 512 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-10-22
- ISBN-10: 0367552345
- ISBN-13: 9780367552343
- Sales Rank: #0 (See Top 100 Books)
Artificial intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems because 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 an 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 a 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.
Cover 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 Appendix References 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 References 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 References 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 12.4 Structure of the Introduced ES 12.4.1 The Mechanism by Which the Introduced ES Work 12.5 An ES for Optimal Coordination of DOCR 12.5.1 Optimal Coordination Facts 12.5.2 Optimal Coordination Rules 12.6 Verification of the Introduced ES 12.6.1 IEEE 3-Bus Test System 12.6.2 The 8-Bus Test System 12.6.3 The IEEE 5-Bus Test System 12.7 Conclusion References 13. Optimal Overcurrent Relay Coordination Considering Standard and Non-Standard Characteristics 13.1 Introduction 13.1.1 Methods for Coordination of DOCRs 13.2 DOCRs Coordination Problem 13.2.1 Boundaries of the Coordination Problem 13.2.1.1 Limits on Relay Characteristics 13.2.1.1.1 Limits on Pickup Current Setting 13.2.1.1.2 Limits on TDS 13.2.1.1.3 Parameters of Characteristics Relay Curve 13.2.1.2 Boundaries on DOCRs Coordination 13.3 Recent Optimization Techniques 13.3.1 WCA and MWCA 13.3.1.1 Conventional WCA 13.3.1.2 MWCA Algorithm 13.3.2 MFO and IMFO Algorithms 13.3.2.1 The MFO Algorithm 13.3.2.2 The IMFO Algorithm 13.4 Results and Discussion 13.4.1 Description of Test Systems 13.4.1.1 The Nine-Bus Network 13.4.1.2 The 15-Bus Test System 13.4.2 Formulated the Coordination Problem Using Standard-CRC 13.4.2.1 Using MWCA for Solving the Coordination Problem 13.4.2.1.1 Case 1: Nine-Bus Network 13.4.2.1.2 Case 2: 15-Bus Network 13.4.3 Solving the Problem of Coordination with Conventional CRC and Non- Conventional CRC 13.4.3.1 Scenario 1: Using Conventional CRC in Solving the Problem of Coordination 13.4.3.1.1 Nine-Bus system 13.4.3.1.2 15-Bus Network 13.4.4 Scenario 2: Using Non-Conventional CRC in Solving the Problem of Coordination 13.4.4.1 Nine-Bus Network 13.4.4.2 15-Bus Network 13.5 Conclusions References 14. Artificial Intelligence Applications in DC Microgrid Protection 14.1 Introduction 14.2 Technical Considerations of DC Microgrid Protection 14.2.1 DC Fault Current Characteristics 14.2.1.1 Analysis of the First Stage of the Fault Current 14.2.1.2 Analysis of the Second Stage of the Fault Current 14.2.2 Technical Issues 14.2.2.1 Equipment Fault-Tolerant 14.2.2.2 Grounding System 14.2.2.3 DC Protective Devices 14.2.2.4 Protection Algorithm Capabilities 14.3 DC Microgrid Protection Approaches 14.4 AI-Based Approaches Effectiveness Investigation 14.4.1 WT Principles 14.4.2 Feature Extraction 14.4.3 Feature Extraction Results 14.4.4 Pattern Recognition with ANN 14.4.5 Classification Results 14.4 Conclusion References 15. Soft Computing-Based DC-Link Voltage Control Technique for SAPF in Harmonic and Reactive Power Compensation 15.1 Introduction 15.2 System Topology of SAPF 15.3 Reference Generation Techniques for SAPF System 15.3.1 Hybrid Control Approach Based Synchronous Reference Frame Method for Active Filter Design (HSRF) 15.4 Design of Proposed Fuzzy Logic Controller in SAPF System 15.5 Proposed Controller Design Technique for Switching Pattern Generation in SAPF System 15.6 Simulation Results for Harmonic Compensation Using SAPF 15.7 Experimental Results 15.8 Conclusions References 16. Artificial Intelligence Application for HVDC Protection 16.1 Introduction 16.1.1 Protection Tools Based on Artificial Intelligence 16.1.1.1 Generation 16.1.1.2 Description 16.1.1.3 Decision Making 16.2 Overview of HVDC Technology 16.3 HVDC Protection 16.3.1 DC Fault Phenomena 16.3.2 Multi-Terminal HVDC Protection 16.4 AI-Based Fault Detection 16.5 AI-Based Fault Classification 16.6 Al-Based Fault Location 16.7 AI-Based Commutation Failure (CF) Identification 16.8 Discussion 16.9 Conclusion References 17. Intelligent Schemes for Fault Detection, Classification, and Location in HVDC Systems 17.1 Introduction 17.2 An Overview of HVDC Systems 17.2.1 CSC-HVDC Systems 17.2.2 VSC-HVDC Systems 17.2.3 Requirements and Challenges 17.3 Fault Detection and Classification in CSC-HVDC Systems 17.3.1 Input Features 17.3.2 Learning Algorithms/Models 17.4 Fault Location in CSC-HVDC Systems 17.4.1 Input Features 17.4.2 Learning Algorithms/Models 17.5 Fault Detection and Classification in VSC-HVDC Systems 17.5.1 Input Features 17.5.2 Learning Algorithms/Models 17.6 Fault Location in VSC-HVDC Systems 17.6.1 Input Features 17.6.2 Learning Algorithms/Models 17.7 Considerations for Practical Implementations 17.7.1 Implementation Costs 17.7.2 Unseen New Cases 17.7.3 High-Resistance Faults 17.7.4 Temporary Arc Faults 17.7.5 Fault Locations Very Close to Line Terminals 17.7.6 Operation of Adjacent Circuit Breakers 17.7.7 Lightning Disturbances 17.7.8 Measurement Noises/Errors 17.7.9 Inaccurate Line Parameters 17.7.10 Communication Delay, Disturbance, and Failure 17.7.11 Time Synchronization Errors 17.8 Conclusion References 18. Fault Classification and Location in MT-HVDC Systems Based on Machine Learning 18.1 Introduction 18.2 Machine Learning-Based Fault Diagnostic Technique 18.2.1 Support Vector Machines 18.2.2 Feature Extraction and Selection 18.3 DC Faults in MT-HVDC Systems 18.4 Voltage Source Converters 18.5 Control System of Voltage Source Converters 18.6 Control of MT-HVDC System 18.7 MT-HVDC Test System and Simulation Results 18.7.1 DC Voltage Analysis 18.7.2 Frequency-Based Analysis 18.7.3 Machine Learning Algorithm 18.8 Conclusion Acknowledgement References Index
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