Intelligent Methods in Electrical Power Systems

2024
ISBN: 978-981-97-5717-6
Verlag: Springer Nature Singapore

Buch, Englisch, 171 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 488 g

Reihe: Engineering Optimization: Methods and Applications

This book provides a comprehensive review of the latest developments in optimization based learning algorithms within the field of electrical engineering. It covers various power system applications including efficient power system operation, load forecasting, fault analysis, home automation and efficient smart grid management. Each application is accompanied by case studies and a literature review in self-contained chapters. The book is dedicated to study the effectiveness of intelligent methods in addressing the power system problems and its mitigation using optimization algorithms. It discusses several optimization algorithms such as random forest algorithm, metaheuristic algorithm, scaled conjugate gradient descent algorithm, artificial bee colony algorithm etc. and their usability in intelligent decision makers for the various optimization problems in electrical engineering. This timely book serves as a practical guide and reference sources for students, researchers and professionals.

Zielgruppe

Research

Autoren/Hrsg.

Khadse, Chetan B.

Shastri, Apoorva S.

Kale, Ishaan R.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

Review on intelligent methods in Electrical power systems.- Investigation of Electric Load Forecasting Methods: A Weka Application (Regression and Optimization).- Integration of Intelligent Systems for Efficient Smart Grid Management.- An Application of Artificial Bee Colony and Cohort Intelligence in Automatic Generation Control of Thermal Power System.- Distribution System Losses and Its Allocation: Effects of Load Power Factor and Distributed Generations.- IoT based Intelligent Home Automation System using IFTTT with Google Assistant.- A review on Meta-heuristic Optimization Methods for Efficient Power System Operation.- Ice thickness control circuit to automate the milk chilling system.- SCGB Neural Network based Micro-grid AC Side Fault Analysis.- Artificial intelligence based system for detection and classification of faults in Induction motor.

Über Autor(innen)

Chetan B. Khadse holds a PhD in "AI applications in Electrical Engineering" from Visvesvaraya National Institute of Technology Nagpur, India,  an MTech in Power Systems from Shivaji University, India, and a Bachelors from Amravati University, India. He is currently working as an Assistant Professor in the School of Electrical Engineering at the MITWPU, Pune, India. His research interests include artificial intelligence, power systems, power quality, optimization algorithms, and intelligent systems. He has published over  18 research papers in peer-reviewed reputed journals, chapters, and conferences. He is actively involved in the Center of Excellence of Electric vehicle at MITWPU where his research is mainly focused on AI techniques for the betterment of EVs. 

Ishaan R. Kale holds a Ph.D. in Nature-Inspired Optimization Techniques from the faculty of Mechanical Engineering at Symbiosis International University. He received his Master of Engineering in Mechanical Design Engineering from Maharashtra Institute of Technology, Pune University, and his Bachelor of Engineering from North Maharashtra University. Ishaan worked as an Assistant Professor at Symbiosis Institute of Technology for six years. Currently, he is working as a Research Assistant Professor at the Institute of Artificial Intelligence, MIT World Peace University. His research interests include Design Engineering, Structural Optimization, Computational Intelligence, Constraint Handling, Probability Collectives, Socio Inspired Optimization Methods, Physics-Based Optimization Methods, Cohort Intelligence, Particle Swarm Optimization, Genetic Algorithms, Hybrid Metaheuristics, Game Theory, Operation Research, and Numerical Methods.  He has published 10 research papers in peer-reviewed journals, conferences, and chapters along with one authored and one edited book. He is also involved as an active research member of the Optimization and Agent Technology Research Lab.

Apoorva S. Shastri holds a PhD in Optimization Algorithms and Applications from Symbiosis International (Deemed University), a Master of Technology (M. Tech) in VLSI Design, and a Bachelor of Engineering in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also earned a Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at the Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is a Research Assistant Professor at the Institute of Artificial Intelligence at the MITWPU, Pune, India. Her research interests include optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete, and combinatorial optimization, complex systems, manufacturing, and self-organizing systems. Apoorva developed socio-inspired optimization methodologies such as Multi-Cohort Intelligence Algorithm and Expectation Algorithm. Apoorva has published several research papers in peer-reviewed journals, chapters, and conferences along with one Springer authored book. She is a regular reviewer of different journals Elsevier and Springer. She has also served as session chair for a few international conferences.