Artificial Intelligence Techniques in Power Systems Operations and Analysis

ISBN-13: 9781032294865 / Angielski

Nagendra Singh; Sitendra Tamrakar; Arvind Mewada

Opis

An electrical power system consists of large number of generation, transmission, and distribution subsystems. It is a very large and complex system and hence its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequent implementation. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing so maintaining load demand without overloading of the system poses challenges and difficulties.

Thus, planning, installing, operation, and controlling such a large system requires new technology. Artificial Intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.

Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques are explained as well as their application. Also featured are relevant data sets and case studies. Highlights include:

• Power quality enhancement by PV-UPQC for non-linear load
• Energy management of a nano grid through flair of deep learning from iot environments
• Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis
• AC power optimization techniques
• Artificial intelligence and machine learning techniques in power systems automation

Spis treści

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.

O autorze

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.