Название: Application of Machine Learning and Deep Learning Methods to Power System Problems
Автор: Morteza Nazari-Heris, Somayeh Asadi
Издательство: Springer
Год: 2022
Страниц: 390
Язык: английский
Формат: pdf (true), epub
Размер: 48.9 MB

This book evaluates the role of innovative Machine Learning (ML) and Deep Learning (DL) methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

Accordingly, the application of various Machine Learning and Deep Learning methods such as artificial neural networks (ANNs), expert systems, fuzzy systems, evolutionary-based methods, deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) has been introduced as effective methods to handle the decision-making process of modeling power systems. The use of machine learning and deep learning, which are data analysis techniques for building analytical models for a variety of subjects (e.g., energy, healthcare, bioinformatics, transportation), is a promising solution to overcome the current challenges of power systems. Machine learning and deep learning, as a part of Artificial Intelligence (AI) family, are very effective methods for facilitating the decision-making process of power systems operation, planning, and control by learning from the raw data, identifying patterns, and making decisions with minimum human intervention.

The advancement and development of power systems, as well as considerable challenges such as the uncertain nature of renewable energy sources such as power output of wind turbines and photovoltaic cells, load demand, and electrical energy market, requires a high-performance approach for appropriate decision-making on the operation, planning, and control of such systems. At the same time, the importance of data clustering and security of power systems highlights the need for a high-performance method to handle the operation, planning, and control of such systems. Accordingly, the use of different Machine Learning and Deep Learning methods as effective techniques with acceptable performance (high accuracy) and reliability in dealing with current issues in power systems in terms of management and operation approaches of the system and forecasting the system parameters is discussed in this book. Application of Machine Learning and Deep Learning Methods to Power System Problems aims to evaluate the application of machine learning/deep learning to issues and challenges of power systems considering recent developments and advances in planning, operation, and control of such systems in both collecting a comprehensive review based on the literature and applying machine learning to power system planning, operation, and control.