Smart cyber-physical power systems : solutions from emerging technologies. Volume 2 / edited by Ali Parizad, Hamid Reza Baghaee, Saifur Rahman.

Format:

Book

Contributor:

Parizad, Ali, editor.

Baghaee, Hamid Reza, editor.

Rahman, Saifur, editor.

Wiley InterScience (Online service)

Series:

IEEE Press series on power and energy systems ; 131.

IEEE Press series on power and energy systems ; 131

Language:

English

Subjects (All):

Cooperating objects (Computer systems).

Electric power systems--Automation.

Electric power systems.

Artificial intelligence.

Physical Description:

1 online resource (xxxix, 576 pages) : color illustrations.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc. ; Piscataway, NJ : IEEE Press, [2025]

Contents:

About the Editors

List of Contributors

Foreword (John D. McDonald)

Foreword (Massoud Amin)

Preface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies

Acknowledgments

1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation 1 Ali Parizad and Constantine Hatziadoniu

1.1 Introduction

1.2 Cyber-attacks on the State Variables of the Power System

1.3 Information Theory

1.4 Gray Level Transformation

1.5 Linear Transformation

1.6 Logarithmic Transformations

1.7 Power-Law Transformations

1.8 Simulation Results

1.9 Conclusion

References

2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems 49 Sohom Datta, Zhangshuan Hou, Milan Jain, and Syed Ahsan Raza Naqvi

2.1 The Need for AI/ML in Modern Power Systems

2.2 AL/ML Algorithms in Power System Applications

2.3 AI/ML-Based Applications in the Electricity Grid

2.4 Future of AI/ML in Power Systems

3 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems 67 Ramij Raja Hossain, Qiuhua Huang, Kaveri Mahapatra, and Renke Huang

3.1 Introduction

3.2 Overview of RL/DRL

3.3 Grid Control Perspectives

3.4 Importance of Physics-Informed DRL in Grid Control and Different Methods

3.5 Grid Control Applications of Physics-Informed DRL

3.6 Discussion and Research Directions

3.7 Conclusions

4 Digital Twin Approach Toward Modern Power Systems 79 Sabrieh Choobkar

4.1 Digital Twin Concept

4.2 Digital Twin: The Convergence of Recent Technologies

4.3 Cyber-Physical System and Digital Twin

4.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems

4.5 Conclusions

5 Application of AI and Machine Learning Algorithms in Power System State Estimation 93 Behrouz Azimian, Reetam Sen Biswas, and Anamitra Pal

5.1 Introduction

5.2 Motivation and Theoretical Background

5.3 DNN Architecture for DSSE and TI

5.4 SMD Measurement Selection for DSSE and TI

5.5 Smart Meter Data Consideration

5.6 Implementation of DNN-Based TI and DSSE

5.7 Conclusion

Acknowledgment

Appendix

6 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings 131 Mahoor Ebrahimi, Mahan Ebrahimi, Miadreza Shafie-khah, Hannu Laaksonen, and Pierluigi Siano

6.1 Introduction

6.2 Problem Formulation

6.3 Application of AI in Energy Management of Smart Homes

6.4 Simulation and Results

6.5 Conclusion

7 Protection Challenges and Solutions in Power Grids by AI/Machine Learning 149 Ali Bidram

7.1 Introduction

7.2 Zonal Setting-Less Modular Protection Using ml

7.3 Traveling Wave Protection of dc Microgrids Using ml

7.4 Conclusion

8 Deep and Reinforcement Learning for Active Distribution Network Protection 171 Mohammed AlSaba and Mohammad Abido

8.1 Introduction and Motivation

8.2 Problem Statement

8.3 Proposed Methodology for Fault Detection and Classification

8.4 Case Study and Implementation

8.5 Results and Discussion

8.6 Hardware in-the-Loop Testing

8.7 Conclusion

9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes 189 Meghana Ramesh, Jing Xie, Monish Mukherjee, Thomas E. McDermott, Anjan Bose, and Michael Diedesch

9.1 Introduction

9.2 Intelligent Modeling and Its Applications

9.3 Case Study

9.4 Conclusions

10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation 209 Yingqi Liang, Junbo Zhao, and Dipti Srinivasan

10.1 Introduction

10.2 Challenges for Using Big Data Techniques in Smart Grids

10.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness

10.4 Applications of Big Data Techniques for Smart Grid Operation

10.5 Numerical Results

10.6 Concluding

11 Data-Driven Methods in Modern Power System Stability and Security 255 Jinpeng Guo, Georgia Pierrou, Xiaoting Wang, Mohan Du, and Xiaozhe Wang

11.1 Introduction

11.2 Data-Driven Wide-Area Damping Control

11.3 Data-Driven Wide-Area Voltage Control

11.4 Data-Driven Inertia Estimation for Frequency Control

11.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment

11.6 Using PCE to Assess the Ramping Support Capability of a Microgrid

12 Application of Quantum Computing for Power Systems 313 Yan Li, Ganesh K. Venayagamoorthy, and Liang

12.1 Quantum Computing in Renewable Energy Systems

12.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems

12.3 Typical Applications of Quantum Computing

13 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts 323 Zejia Jing, Ali Parizad, and Saifur Rahman

13.1 Introduction

13.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting

13.3 Conclusion

14 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results 363 Zejia Jing, Ali Parizad, and Saifur Rahman

14.1 Introduction

14.2 Case Study and Result of Building Hourly Energy Consumption Forecasting

14.3 Building Occupancy Measurement

14.4 Conclusion

15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems 417 Zejia Jing, Ali Parizad, and Saifur Rahman

15.1 Introduction

15.2 PV Energy Forecasting

15.3 Conclusion

16 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability 449 Pouria Akbarzadeh Aghdam, Hamid Khoshkhoo, and Ahmad Akbari

16.1 Introduction

16.2 Stability Control Methods

16.3 Characteristics of Optimal Stability Controller

16.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability

16.5 Taxonomy of RL

16.6 Proposed Algorithm

16.7 Reinforcement Learning Algorithm Components

16.8 Algorithm Implementation Process

16.9 Simulations and Results

16.10 Scenario I

16.11 Scenario II

16.12 Scenario III

16.13 Conclusion

17 Deep Learning Techniques for Solving Optimal Power Flow Problems 471 Vassilis Kekatos and Manish K. Singh

17.1 Introduction

17.2 Sensitivity-Informed Learning for OPF

17.3 Deep Learning for Stochastic OPF

17.4 Conclusions

18 Research on Intelligent Prediction of Spatial-Temporal Dynamic Frequency Response and Performance Evaluation 501 Xieli Sun, Longyu Chen, and Xiaoru Wang

18.1 Introduction

18.2 Modeling Process and Evaluation Method

18.3 Case Study

18.4 Conclusion

19 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations 525 Ali Parizad, Hamid Reza Baghaee, Vahid Alizadeh, and Saifur Rahman

19.1 Introduction

19.2 Paradigm Shifts in Power Transmission and Management

19.3 Innovations in Electric Mobility and Sustainable Transportation

19.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems

19.5 Cyber-Physical Systems Enhancing Societal Well-Being

19.6 Toward a Decentralized and Automated Future

19.7 Overcoming Challenges with Advanced Technologies

19.8 Revolutionizing Modern Power Systems with Real-Time Simulators

19.9 Emerging Trends Shaping the Future Energy Landscape

19.10 Conclusion

Index.

Notes:

Includes bibliographical references and index.

Electronic reproduction. Hoboken, N.J. Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on March 20, 2025).

ISBN:

9781394334575

1394334575

9781394334599

1394334591

9781394334582

1394334583

Publisher Number:

90103820165

Access Restriction:

Restricted for use by site license.