This book focuses on the various challenges arising in power systems and how AI techniques help to overcome such challenges. This book examines various important areas of power system analysis and the implementation of AI-driven analysis techniques. Multiple AI techniques are explained as well as their application.
This book focuses on the various challenges arising in power systems and how AI techniques help to overcome such challenges. This book examines various important areas of power system analysis and the implementation of AI-driven analysis techniques. Multiple AI techniques are explained as well as their application.
Dr. Nagendra Singh is an associate professor in the department of Electrical Engineering at Trinity College of Engineering and Technology, Karimanagar, India. Dr. Sitendra Tamrakar is an associate professor in the department of computer science and engineering at Nalla Malla Reddy Engineering College, Hyderabad, India. Arvind Mewara has worked as associate professor in the department of Computer Engineering at Technocrats Institute of Technology, Bhopal, India. Presently he is a full time Ph.D. scholar at Motilal Nehru National Institute of Technology, Allahabad, India. Dr. Sanjeev Kumar Gupta is a professor and dean at the Rabindranath Tagore University, Bhopal.
Inhaltsangabe
List of Contributors. 1 Faults Diagnosis Using AI and ML. 2 Load Frequency Control for Multi-Area Power System Using PSO-Based Technique. 3 Power Quality Enhancement by PV-UPQC for Non-Linear Load. 4 A Hybrid Energy Management for Stand-Alone Microgrids Using Grey Wolf Optimization System. 5 Energy Management of Nanogrid through Flair of Deep Learning from IoT Environments. 6 An Elitism-Based SAMP-JAYA Algorithm for Optimal VA Loading of Unified Power Quality Conditioner. 7 Applications of Artificial Intelligence. 8 Role of Artificial Intelligence and Machine Learning in Power Systems with Fault Detection and Diagnosis. 9 AC Power Optimization Technique. 10 Data Transformation: A Preprocessing Stage in Machine Learning Regression Problems. 11 Predicting Native Language with Machine Learning: An Automated Approach. 12 Artificial Intelligence and Machine Learning Techniques in Power Systems Automation. Index.
List of Contributors. 1 Faults Diagnosis Using AI and ML. 2 Load Frequency Control for Multi-Area Power System Using PSO-Based Technique. 3 Power Quality Enhancement by PV-UPQC for Non-Linear Load. 4 A Hybrid Energy Management for Stand-Alone Microgrids Using Grey Wolf Optimization System. 5 Energy Management of Nanogrid through Flair of Deep Learning from IoT Environments. 6 An Elitism-Based SAMP-JAYA Algorithm for Optimal VA Loading of Unified Power Quality Conditioner. 7 Applications of Artificial Intelligence. 8 Role of Artificial Intelligence and Machine Learning in Power Systems with Fault Detection and Diagnosis. 9 AC Power Optimization Technique. 10 Data Transformation: A Preprocessing Stage in Machine Learning Regression Problems. 11 Predicting Native Language with Machine Learning: An Automated Approach. 12 Artificial Intelligence and Machine Learning Techniques in Power Systems Automation. Index.
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