Monitoring And Control Of Electrical Power Systems Using Machine Learning Techniques

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub

The intention of this book is to give an introduction to, and an overview of, the field of artificial intelligence techniques in power systems, with a look at various application studies.

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; 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 subsequently implement. 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 the system poses challenges and difficulties. Thus, planning, installing, operating, 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 and their application are explained. 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 nanogrid 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

This book evaluates the role of innovative machine learning and deep learning 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.

Power System Monitoring and Control (PSMC) is becoming increasingly significant in the design, planning, and operation of modern electric power systems. In response to the existing challenge of integrating advanced metering, computation, communication, and control into appropriate levels of PSMC, Power System Monitoring and Control presents a comprehensive overview of the basic principles and key technologies for the monitoring, protection, and control of contemporary wide-area power systems. A variety of topical issues are addressed, including renewable energy sources, smart grids, wide-area stabilizing, coordinated voltage regulation, and angle oscillation damping—as well as the advantages of phasor measurement units (PMUs) and global positioning systems (GPS) time signal. End-of-chapter problems and solutions, along with case studies, add depth and clarity to all topics. Timely and important, Power System Monitoring and Control is an invaluable resource for addressing the myriad of critical technical engineering considerations in modern electric power system design and operation. • Provides an updated and comprehensive reference for researcher and engineers working on wide-area power system monitoring and control (PSMC) • Links fundamental concepts of PSMC, advanced metering and control theory/techniques, and practical engineering considerations • Covers PSMC problem understanding, design, practical aspects, and timely topics such as smart/microgrid control and coordinated voltage regulation and angle oscillation damping • Incorporates authors’ experiences teaching and researching in various international locales including Japan, Thailand, Singapore, Malaysia, Iran, and Australia

Intelligent Knowledge Based Systems in Electrical Power Engineering details how intelligent applications can be used in the power industry. The book gives a general and historical overview of intelligent knowledge based systems (IKBS) and artificial intelligence (AI) and a broad analysis of the application of these techniques in the electrical power industry. It includes chapters on forecasting and planning in power systems, design of electrical plant and systems, IKBS in condition monitoring, alarm processing, event and fault diagnosis and an analysis of future trends in IKBS for power engineering. No previous knowledge of IKBS is assumed, but an appreciation of electrical transmission and distribution systems would be useful.

The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.