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Submit a ProposalComputer-Aided Power System Condition Monitoring
One Line Description
Packed with real-world case studies, industry best practices, and the latest regulatory standards, this comprehensive guide bridges theory and practice to equip researchers, academics, and power industry professionals with the cutting-edge condition monitoring tools needed to build a safer, more reliable, and efficient modern grid.
Description
Computer-Aided Power System Condition Monitoring plays a crucial role in modern power systems, offering significant benefits that contribute to the overall reliability, efficiency, and safety of the electrical grid. This book offers comprehensive coverage of the principles, methodologies, and technologies related to Computer-Aided Power System Condition Monitoring. It will address a wide range of topics, from fundamental concepts to advanced techniques, ensuring a thorough understanding of the subject. The inclusion of case studies, examples, and practical scenarios bridges the gap between theoretical knowledge and practical application. Given the importance of compliance with standards and regulations in the power industry, the book will provide information on the latest industry standards related to condition monitoring. The book bridges theory and practice by integrating industry best practices, including insights into how leading organizations implement condition monitoring, address challenges, and adopt successful strategies. With clear and accessible language and a well-organized format, this comprehensive resource is an essential guide for researchers, academics, and industry professionals researching the latest cutting-edge technologies that are transforming the modern electrical grid.
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Computer-Aided Power System Condition Monitoring plays a crucial role in modern power systems, offering significant benefits that contribute to the overall reliability, efficiency, and safety of the electrical grid. This book offers comprehensive coverage of the principles, methodologies, and technologies related to Computer-Aided Power System Condition Monitoring. It will address a wide range of topics, from fundamental concepts to advanced techniques, ensuring a thorough understanding of the subject. The inclusion of case studies, examples, and practical scenarios bridges the gap between theoretical knowledge and practical application. Given the importance of compliance with standards and regulations in the power industry, the book will provide information on the latest industry standards related to condition monitoring. The book bridges theory and practice by integrating industry best practices, including insights into how leading organizations implement condition monitoring, address challenges, and adopt successful strategies. With clear and accessible language and a well-organized format, this comprehensive resource is an essential guide for researchers, academics, and industry professionals researching the latest cutting-edge technologies that are transforming the modern electrical grid.
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Author / Editor Details
Debopoma Kar Ray, PhD is an Assistant Professor in the Department of Electrical Engineering at the MCKV Institute of Engineering, Kolkata, India. She has authored more than 34 papers published in national and international journals and conferences. Her fields of interest include power quality, fault diagnosis, signal analysis, and stability analysis.
Debopoma Kar Ray, PhD is an Assistant Professor in the Department of Electrical Engineering at the MCKV Institute of Engineering, Kolkata, India. She has authored more than 34 papers published in national and international journals and conferences. Her fields of interest include power quality, fault diagnosis, signal analysis, and stability analysis.
Tamal Roy, PhD is an Assistant Professor and Head of the Department of Electrical Engineering at the MCKV Institute of Engineering. He has more than 50 publications in international journals and conferences of repute. His research focuses on adaptive control, uncertainty modelling, and robust control of non-linear systems.
Surajit Chattopadhyay, PhD is an Associate Professor in the Department of Electrical Engineering at the Ghani Khan Choudhury Institute of Engineering and Technology, West Bengal, India. He has authored and coauthored more than 140 papers published in national and international journals and conferences and six books. His fields of interest include electric power quality, fault diagnosis, power system protection, signal analysis, and robotics applications.
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Table of Contents
Preface
1. A Review of Computer-Aided Condition Monitoring for Low Voltage DC Power Distribution Systems
Sougata Jana, Debopoma Kar Ray, Tamal Roy and Surajit Chattopadhyay
1.1 Introduction
1.1.1 Available Control Strategies in Literature
1.1.2 Power Quality Analysis of LVDC System as per Review
1.1.3 Fault Detection Approaches as per Study
1.1.4 Recent Trends of Energy Management in LVDC System
1.1.5 Motivation of Work
1.1.6 Research Organization
1.2 Condition Monitoring Related to Control Mechanism
1.2.1 Theoretical Development
1.2.2 Control Technique Based on Decentralized Strategies
1.2.3 AI-Based Techniques
1.2.4 Comparative Study for Best-Fit Optimization
1.3 Power Quality Enhancement
1.3.1 Hypothetical Progress
1.3.2 Enhancement Techniques Based on Signal Processing
1.3.3 Enhancement Techniques Based on Empirical Algorithms
1.3.4 Comparative Analysis with Existing Technique
1.4 Condition Monitoring for Fault Minimization
1.4.1 Fault Assessment Using Signal-Based Methodology
1.4.2 Fault Assessment Using Nature-Inspired Algorithms
1.5 Smart Monitoring for Energy Optimization
1.5.1 Initiatives Concerning Energy Management
1.5.2 Machine Learning Approaches for Intelligent Monitoring and Optimization
1.6 Contrast among Existing Techniques
1.7 Specific Outcomes of the Analysis
1.8 Summary
References
2. Fault Detection Approaches for AC Microgrid Power Converters Employed in Distributed Energy Sources: A Review
Sankha Subhra Ghosh, Surajit Chattopadhyay and Arabinda Das
2.1 Introduction
2.2 General Structure of AC Microgrid
2.3 Various Elements of an AC MG
2.4 Operational Diversity of AC MG
2.5 Modes, Causes and Effects of Faults in MPCs Components
2.5.1 Power Semiconductor Switches
2.5.2 Capacitors
2.6 Fault Diagnosis Methods Adopted
2.6.1 Model-Based Methods
2.6.2 Model-Free Methods
2.6.3 FDTs in Capacitors
2.7 Case Studies
2.8 Conclusions and Direction for Future Research
References
3. Framework for Condition Monitoring of Subsystems of Power System: Case Studies and Challenges
Ambarnath Banerji, Subimal Roy Barman and Sudip Ghosh
3.1 Introduction
3.2 Case Study 1: Bearing Lubrication Oil Subsystem
3.2.1 Main Oil Pump
3.2.2 Auxiliary Oil Pump
3.2.3 DC-Emergency Oil Pump
3.3 Case Study 2: Protective Relay Subsystem
3.4 Case Study 3: LVDT Subsystem
3.5 Case Study 4: Control Subsystem of UPS
3.6 Case Study 5: Subsystems of the Grid
3.6.1 Non-Linear or Distorted Demand–Supply Mismatch Across the Regions
3.6.2 Violation of Grid Discipline
3.6.3 Scarcity in Reliable Resources
3.6.4 Failure of Grid Defence Mechanism Schemes
3.6.5 Default Primary Response from Generators
3.6.6 Inadequate Clearness and Situational Vigilance at the Load Despatch Centers
3.6.7 Inadequate Determination of Transfer Capability or Transmission Capacity
3.6.8 Dearth of Dynamic Reactive Reserves
3.6.9 Improper Operation of Protection System
3.6.10 Hindrances for Early Restoration of Systems
3.6.11 Bottlenecks Initiation from Existing Grant of Connectivity Regulations
3.6.12 Dependency on Unscheduled Interchange Rather Than Rational Market
3.6.13 Narrowing the Stipulated Range for System Frequency
3.6.14 Institutional Issues
3.7 Outcomes
3.8 Challenges
3.8.1 Cost
3.8.2 Reliability
3.8.3 Development of New Technology
3.8.4 Additional Inventory and Spares
3.8.5 Compromise System
3.9 Conclusion
References
4. Algorithms for Identification, Classification and Localization of Transmission Line Faults
Alok Mukherjee, Palash Kumar Kundu and Arabinda Das
4.1 Introduction
4.2 Transmission Line Faults
4.3 Importance of Fault Classification and Localization for Transmission Line Faults
4.4 Ancient Methods of Transmission Line Fault Analysis
4.5 Application of Computer Aided Tools for Analyzing Transmission Line Faults
4.6 Transmission Line Fault Diagnosis Architecture
4.7 Different Modern Methods of Fault Analysis in Transmission Lines
4.7.1 Wavelet Transform-Based Fault Analysis
4.7.2 Correlation-Based Analysis of Fault Signals
4.7.3 Principal Component Analysis-Based Power System Fault Analysis
4.7.4 Fault Analysis Based on Traveling Wave Model
4.7.5 Fault Localization Scheme Based on Time–Frequency Domain Approaches
4.7.6 Analysis Using Time-Synchronized Models and Phasor Measurement Unit
4.7.7 Fault Analysis Using Supervised Models Such as Artificial Neural Network
4.7.8 Fault Analysis Based on Probabilistic Neural Networks-Based Supervised Learning Model
4.7.9 Fault Analysis Using Support Vector Machine-Based Learning Models
4.7.10 Fuzzy Inference System-Based Transmission Line Fault Analysis Models
4.7.11 Decision Directed Acyclic Graph
4.7.12 Fault Analysis Schemes Using Hybrid Methods Comprising Techniques Such as WT, ANN, Fuzzy Logic Inference, and SVM
4.7.13 Other Latest Methods of Fault Analysis
4.8 Summary
References
5. Advanced Techniques for Fault Diagnosis in Wide Area
Monitoring System
B. Sneha, Rohit Babu and Sheila Mahapatra
Abbreviations
5.1 Introduction
5.2 Block Diagram of PMU
5.3 Typical Diagram of WAMS
5.4 Methodology
5.4.1 Details of Fault Locations
5.4.2 Secure Transmission Line Faults
5.4.3 Electric Power System Fault Diagnosis Importance
5.5 Case Study
5.6 Conclusion
Bibliography
6. Strategic BESS and EV Integration in Distribution Networks: A Cost-Effective Approach
Manas Mukherjee
6.1 Introduction
6.2 Problem Formulation
6.3 Voltage-Dependent Load Models
6.4 Optimal Rating of BESS
6.5 Analysis of BESS Problem
6.6 Improved PSO
6.7 Numerical Results
6.8 Conclusion
References
7. Fault Analysis in Hybrid Microgrid Using Statistical Tool-Based Optimization Technique
Debopoma Kar Ray, Abhrojyoti Chatterjee, Tamal Roy and Surajit Chattopadhyay
7.1 Introduction
7.2 Network Under Analysis
7.3 Unsymmetrical Fault Detection
7.4 Algorithm for Fault Assessment
7.5 Comparative Analyses with Existing Techniques
7.6 Conclusion
References
8. Structural Modeling of Underground Cable Fault Detection
of Subterranean Cables Using Proteus Simulator
Mohammad Irfan and Neeta Sahay
8.1 Introduction
8.2 Methodology
8.3 Software Implementation
8.4 Working Procedure
8.5 Results
8.6 Conclusion
8.6.1 Advantages
8.6.2 Limitation
8.6.3 Future Extent
References
9. Renewable Energy Forecasting Based on Deep Learning Algorithms (DLA)
Sunil Kumar Choudhary
9.1 Introduction
9.2 Deep Learning Wind and Solar Energy Forecasting Taxonomy
9.2.1 Convolutional Neural Networks (CNNs) for Image-Based Forecasting: Utilizing Satellite Imagery and Weather Maps
9.2.2 Recurrent Neural Networks (RNNs) for Time-Series Forecasting: Capturing Temporal Dependencies in Data
9.2.3 Long Short-Term Memory (LSTM) Networks for Sequential-Data Analysis: Addressing Long-Term Dependencies
9.2.4 Generative Adversarial Networks (GANs) for Data Augmentation: Enhancing Training Data and Robustness
9.2.5 Hybrid Architectures for Integrated Forecasting: Combining Strengths for Enhanced Predictions
9.3 Data and Data Set
9.4 Data Preprocessing Methods
9.5 Deterministic Forecasting Techniques
9.5.1 Models Based on CNN
9.5.2 Models Based on RNN
9.5.3 Models Based on Stacked Autoencoder
9.5.4 Models Based on Deep Belief Network
9.5.5 Other Deep Models
9.5.6 Hybrid and Ensemble Models
9.6 Probabilistic Forecasting Methods
9.7 Evaluation and Comparison Methods
9.8 Discussion, Challenges, and Future Research Directions
9.9 Conclusion
References
10. Improve the System Reliability with the Help of Modified African Vulture Optimization Algorithm
Sanjoy Kumar Saha
10.1 Introduction
10.2 Mathematical Definition of the Objective Function for Ideal DG Placement
10.3 Optimal Posting of DG Using African Vulture Optimization Algorithm
10.3.1 African Vulture Optimization Algorithm (AVOA)
10.4 Reliability Assessment of Distribution Network
10.5 Results and Discussion
10.6 Conclusion
References
11. Early Detection of Stator Fault in Induction Motor Using Off-Diagonal Sequence Impedance and KNN Classifier
Sudeep Samanta and Jitendra Nath Bera
11.1 Introduction
11.2 Stator Inter-Turn Faulty Model of an Induction Motor
11.3 Fault Detection Methodology
11.3.1 Fundamental of Fault Index (FI)
11.3.2 Evaluation of Sequence Components by Sample Shifting Technique (SST)
11.3.3 Dataset Preparation
11.3.3.1 Stage I: Before Diagnostic Stage
11.3.3.2 Stage II: Learning Stage
11.3.3.3 Stage III: Monitoring Stage
11.4 Fault Detection by KNN Classifier
11.4.1 Mathematical Equation of KNN Classifier Model
11.4.2 Hardware Setup for Experimentation
11.4.3 Training Process of KNN Classifier
11.4.4 Testing Process of KNN Classifier
11.4.5 Experimental Results
11.5 Conclusion
References
12. Intelligent Control of Smart Grid Operation Using Phasor Measurement Units
Suman Ghosh, Chandan Kr. Chanda, Aveek Chattopadhyaya, Shyamal Kumar Roy and Surajit Chattopadhyay
12.1 Introduction
12.2 Fundamentals of PMUs
12.3 Intelligent Control of the Smart Grid Operations Using PMUs
12.4 Latency Associated with PMUs
12.5 Communication Technology Requirements for the Applications of PMUs
12.6 Specific Outcome
12.7 Challenges for Phasor Measurement Unit Applications
12.8 Conclusion
References
13. Machine Learning and Internet of Things‑Based AC–DC Electrical Parameter Monitoring and Measurement
Shyamal Kumar Roy, Aveek Chattopadhyaya, Suman Ghosh and Amit Debnath
13.1 Introduction
13.2 Objectives
13.3 Motivation
13.3.1 AC Voltage Controlling
13.3.2 Zero-Crossing Detector Circuit Using a Microcontroller
13.3.3 Phase Angle Control of SCR Using AT89C51
13.3.4 Establishing a Realtime Database for the Application
13.3.5 Instantaneous Angular Speed Measurement
13.3.6 AC and DC Current Measurement
13.3.7 Circuit Breaking and Overcurrent Protection
13.3.8 Opto-Isolation
13.3.9 Machine Learning Algorithm
13.3.10 Building of Secure Android Device Application Software
13.4 Experimental Studies
13.4.1 Data Storage and Transmission Over the Internet Using Google Firebase
13.4.2 Electrical Isolation for Microcontroller
13.4.3 Zero-Crossing Detection
13.4.4 16 ∗ 2 LCD Display Interfacing Test
13.4.5 Testing Using an LM35 IC (Used as a Digital Thermometer)
13.5 Techniques and Modus Operandi
13.5.1 Application and the Online Database
13.5.2 The Machine Learning Algorithm
13.5.3 AC Voltage Controlling Using the Development Board
13.5.4 AC Current Measurement
13.5.5 DC Current Measurement
13.5.6 Power and Energy Measurement
13.5.7 Establishment of Internet Connection for the Device IoT Systems to Work
13.6 Applications
13.7 Conclusion
Bibliography
Index
Back to Top
Preface
1. A Review of Computer-Aided Condition Monitoring for Low Voltage DC Power Distribution Systems
Sougata Jana, Debopoma Kar Ray, Tamal Roy and Surajit Chattopadhyay
1.1 Introduction
1.1.1 Available Control Strategies in Literature
1.1.2 Power Quality Analysis of LVDC System as per Review
1.1.3 Fault Detection Approaches as per Study
1.1.4 Recent Trends of Energy Management in LVDC System
1.1.5 Motivation of Work
1.1.6 Research Organization
1.2 Condition Monitoring Related to Control Mechanism
1.2.1 Theoretical Development
1.2.2 Control Technique Based on Decentralized Strategies
1.2.3 AI-Based Techniques
1.2.4 Comparative Study for Best-Fit Optimization
1.3 Power Quality Enhancement
1.3.1 Hypothetical Progress
1.3.2 Enhancement Techniques Based on Signal Processing
1.3.3 Enhancement Techniques Based on Empirical Algorithms
1.3.4 Comparative Analysis with Existing Technique
1.4 Condition Monitoring for Fault Minimization
1.4.1 Fault Assessment Using Signal-Based Methodology
1.4.2 Fault Assessment Using Nature-Inspired Algorithms
1.5 Smart Monitoring for Energy Optimization
1.5.1 Initiatives Concerning Energy Management
1.5.2 Machine Learning Approaches for Intelligent Monitoring and Optimization
1.6 Contrast among Existing Techniques
1.7 Specific Outcomes of the Analysis
1.8 Summary
References
2. Fault Detection Approaches for AC Microgrid Power Converters Employed in Distributed Energy Sources: A Review
Sankha Subhra Ghosh, Surajit Chattopadhyay and Arabinda Das
2.1 Introduction
2.2 General Structure of AC Microgrid
2.3 Various Elements of an AC MG
2.4 Operational Diversity of AC MG
2.5 Modes, Causes and Effects of Faults in MPCs Components
2.5.1 Power Semiconductor Switches
2.5.2 Capacitors
2.6 Fault Diagnosis Methods Adopted
2.6.1 Model-Based Methods
2.6.2 Model-Free Methods
2.6.3 FDTs in Capacitors
2.7 Case Studies
2.8 Conclusions and Direction for Future Research
References
3. Framework for Condition Monitoring of Subsystems of Power System: Case Studies and Challenges
Ambarnath Banerji, Subimal Roy Barman and Sudip Ghosh
3.1 Introduction
3.2 Case Study 1: Bearing Lubrication Oil Subsystem
3.2.1 Main Oil Pump
3.2.2 Auxiliary Oil Pump
3.2.3 DC-Emergency Oil Pump
3.3 Case Study 2: Protective Relay Subsystem
3.4 Case Study 3: LVDT Subsystem
3.5 Case Study 4: Control Subsystem of UPS
3.6 Case Study 5: Subsystems of the Grid
3.6.1 Non-Linear or Distorted Demand–Supply Mismatch Across the Regions
3.6.2 Violation of Grid Discipline
3.6.3 Scarcity in Reliable Resources
3.6.4 Failure of Grid Defence Mechanism Schemes
3.6.5 Default Primary Response from Generators
3.6.6 Inadequate Clearness and Situational Vigilance at the Load Despatch Centers
3.6.7 Inadequate Determination of Transfer Capability or Transmission Capacity
3.6.8 Dearth of Dynamic Reactive Reserves
3.6.9 Improper Operation of Protection System
3.6.10 Hindrances for Early Restoration of Systems
3.6.11 Bottlenecks Initiation from Existing Grant of Connectivity Regulations
3.6.12 Dependency on Unscheduled Interchange Rather Than Rational Market
3.6.13 Narrowing the Stipulated Range for System Frequency
3.6.14 Institutional Issues
3.7 Outcomes
3.8 Challenges
3.8.1 Cost
3.8.2 Reliability
3.8.3 Development of New Technology
3.8.4 Additional Inventory and Spares
3.8.5 Compromise System
3.9 Conclusion
References
4. Algorithms for Identification, Classification and Localization of Transmission Line Faults
Alok Mukherjee, Palash Kumar Kundu and Arabinda Das
4.1 Introduction
4.2 Transmission Line Faults
4.3 Importance of Fault Classification and Localization for Transmission Line Faults
4.4 Ancient Methods of Transmission Line Fault Analysis
4.5 Application of Computer Aided Tools for Analyzing Transmission Line Faults
4.6 Transmission Line Fault Diagnosis Architecture
4.7 Different Modern Methods of Fault Analysis in Transmission Lines
4.7.1 Wavelet Transform-Based Fault Analysis
4.7.2 Correlation-Based Analysis of Fault Signals
4.7.3 Principal Component Analysis-Based Power System Fault Analysis
4.7.4 Fault Analysis Based on Traveling Wave Model
4.7.5 Fault Localization Scheme Based on Time–Frequency Domain Approaches
4.7.6 Analysis Using Time-Synchronized Models and Phasor Measurement Unit
4.7.7 Fault Analysis Using Supervised Models Such as Artificial Neural Network
4.7.8 Fault Analysis Based on Probabilistic Neural Networks-Based Supervised Learning Model
4.7.9 Fault Analysis Using Support Vector Machine-Based Learning Models
4.7.10 Fuzzy Inference System-Based Transmission Line Fault Analysis Models
4.7.11 Decision Directed Acyclic Graph
4.7.12 Fault Analysis Schemes Using Hybrid Methods Comprising Techniques Such as WT, ANN, Fuzzy Logic Inference, and SVM
4.7.13 Other Latest Methods of Fault Analysis
4.8 Summary
References
5. Advanced Techniques for Fault Diagnosis in Wide Area
Monitoring System
B. Sneha, Rohit Babu and Sheila Mahapatra
Abbreviations
5.1 Introduction
5.2 Block Diagram of PMU
5.3 Typical Diagram of WAMS
5.4 Methodology
5.4.1 Details of Fault Locations
5.4.2 Secure Transmission Line Faults
5.4.3 Electric Power System Fault Diagnosis Importance
5.5 Case Study
5.6 Conclusion
Bibliography
6. Strategic BESS and EV Integration in Distribution Networks: A Cost-Effective Approach
Manas Mukherjee
6.1 Introduction
6.2 Problem Formulation
6.3 Voltage-Dependent Load Models
6.4 Optimal Rating of BESS
6.5 Analysis of BESS Problem
6.6 Improved PSO
6.7 Numerical Results
6.8 Conclusion
References
7. Fault Analysis in Hybrid Microgrid Using Statistical Tool-Based Optimization Technique
Debopoma Kar Ray, Abhrojyoti Chatterjee, Tamal Roy and Surajit Chattopadhyay
7.1 Introduction
7.2 Network Under Analysis
7.3 Unsymmetrical Fault Detection
7.4 Algorithm for Fault Assessment
7.5 Comparative Analyses with Existing Techniques
7.6 Conclusion
References
8. Structural Modeling of Underground Cable Fault Detection
of Subterranean Cables Using Proteus Simulator
Mohammad Irfan and Neeta Sahay
8.1 Introduction
8.2 Methodology
8.3 Software Implementation
8.4 Working Procedure
8.5 Results
8.6 Conclusion
8.6.1 Advantages
8.6.2 Limitation
8.6.3 Future Extent
References
9. Renewable Energy Forecasting Based on Deep Learning Algorithms (DLA)
Sunil Kumar Choudhary
9.1 Introduction
9.2 Deep Learning Wind and Solar Energy Forecasting Taxonomy
9.2.1 Convolutional Neural Networks (CNNs) for Image-Based Forecasting: Utilizing Satellite Imagery and Weather Maps
9.2.2 Recurrent Neural Networks (RNNs) for Time-Series Forecasting: Capturing Temporal Dependencies in Data
9.2.3 Long Short-Term Memory (LSTM) Networks for Sequential-Data Analysis: Addressing Long-Term Dependencies
9.2.4 Generative Adversarial Networks (GANs) for Data Augmentation: Enhancing Training Data and Robustness
9.2.5 Hybrid Architectures for Integrated Forecasting: Combining Strengths for Enhanced Predictions
9.3 Data and Data Set
9.4 Data Preprocessing Methods
9.5 Deterministic Forecasting Techniques
9.5.1 Models Based on CNN
9.5.2 Models Based on RNN
9.5.3 Models Based on Stacked Autoencoder
9.5.4 Models Based on Deep Belief Network
9.5.5 Other Deep Models
9.5.6 Hybrid and Ensemble Models
9.6 Probabilistic Forecasting Methods
9.7 Evaluation and Comparison Methods
9.8 Discussion, Challenges, and Future Research Directions
9.9 Conclusion
References
10. Improve the System Reliability with the Help of Modified African Vulture Optimization Algorithm
Sanjoy Kumar Saha
10.1 Introduction
10.2 Mathematical Definition of the Objective Function for Ideal DG Placement
10.3 Optimal Posting of DG Using African Vulture Optimization Algorithm
10.3.1 African Vulture Optimization Algorithm (AVOA)
10.4 Reliability Assessment of Distribution Network
10.5 Results and Discussion
10.6 Conclusion
References
11. Early Detection of Stator Fault in Induction Motor Using Off-Diagonal Sequence Impedance and KNN Classifier
Sudeep Samanta and Jitendra Nath Bera
11.1 Introduction
11.2 Stator Inter-Turn Faulty Model of an Induction Motor
11.3 Fault Detection Methodology
11.3.1 Fundamental of Fault Index (FI)
11.3.2 Evaluation of Sequence Components by Sample Shifting Technique (SST)
11.3.3 Dataset Preparation
11.3.3.1 Stage I: Before Diagnostic Stage
11.3.3.2 Stage II: Learning Stage
11.3.3.3 Stage III: Monitoring Stage
11.4 Fault Detection by KNN Classifier
11.4.1 Mathematical Equation of KNN Classifier Model
11.4.2 Hardware Setup for Experimentation
11.4.3 Training Process of KNN Classifier
11.4.4 Testing Process of KNN Classifier
11.4.5 Experimental Results
11.5 Conclusion
References
12. Intelligent Control of Smart Grid Operation Using Phasor Measurement Units
Suman Ghosh, Chandan Kr. Chanda, Aveek Chattopadhyaya, Shyamal Kumar Roy and Surajit Chattopadhyay
12.1 Introduction
12.2 Fundamentals of PMUs
12.3 Intelligent Control of the Smart Grid Operations Using PMUs
12.4 Latency Associated with PMUs
12.5 Communication Technology Requirements for the Applications of PMUs
12.6 Specific Outcome
12.7 Challenges for Phasor Measurement Unit Applications
12.8 Conclusion
References
13. Machine Learning and Internet of Things‑Based AC–DC Electrical Parameter Monitoring and Measurement
Shyamal Kumar Roy, Aveek Chattopadhyaya, Suman Ghosh and Amit Debnath
13.1 Introduction
13.2 Objectives
13.3 Motivation
13.3.1 AC Voltage Controlling
13.3.2 Zero-Crossing Detector Circuit Using a Microcontroller
13.3.3 Phase Angle Control of SCR Using AT89C51
13.3.4 Establishing a Realtime Database for the Application
13.3.5 Instantaneous Angular Speed Measurement
13.3.6 AC and DC Current Measurement
13.3.7 Circuit Breaking and Overcurrent Protection
13.3.8 Opto-Isolation
13.3.9 Machine Learning Algorithm
13.3.10 Building of Secure Android Device Application Software
13.4 Experimental Studies
13.4.1 Data Storage and Transmission Over the Internet Using Google Firebase
13.4.2 Electrical Isolation for Microcontroller
13.4.3 Zero-Crossing Detection
13.4.4 16 ∗ 2 LCD Display Interfacing Test
13.4.5 Testing Using an LM35 IC (Used as a Digital Thermometer)
13.5 Techniques and Modus Operandi
13.5.1 Application and the Online Database
13.5.2 The Machine Learning Algorithm
13.5.3 AC Voltage Controlling Using the Development Board
13.5.4 AC Current Measurement
13.5.5 DC Current Measurement
13.5.6 Power and Energy Measurement
13.5.7 Establishment of Internet Connection for the Device IoT Systems to Work
13.6 Applications
13.7 Conclusion
Bibliography
Index
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