Application Of Signal Processing Tools And Artificial Neural Network In Diagnosis Of Power System Faults

Explores methods of fault identification through programming and simulation in MATLAB Examines signal processing tools and their applications with examples Provides knowledge of artificial neural networks and their applications with illustrations Uses PNN and BPNN to identify the different types of faults and obtain their corresponding locations Discusses the programming of signal processing using Wavelet Transform and S-Transform

PARTIAL DISCHARGES (PD) — DETECTION, IDENTIFICATION AND LOCALIZATION Explore state-of-the-art partial discharge measurement techniques In Partial Discharges (PD) — Detection, Identification and Localization, a team of distinguished electrical engineers delivers a comprehensive treatment of the behavior, modeling, measurement, monitoring, localization, and evaluation of partial discharges. It includes coverage of all major advancements in the field that have occurred over the last few decades. It also discusses partial discharge phenomena, detection methods, and strategies for analyzing and processing collected data. Mechanisms of insulation failure are explored, as is the denoising of partial discharge measurement data and the localization of partial discharge in large, high-voltage equipment. Non-electric principles and procedures are discussed, and the book offers a variety of tables, figures, and photographs to illustrate the concepts discussed within. Partial Discharges(PD) also provides: A thorough introduction to the physical behavior of partial discharges, including their causes and classification Comprehensive modeling of partial discharge behavior, including classical and dipole discharges Practical discussions of the measurement of partial discharges, including the electrical method, partial discharge decoupling, and pre- and post-processing of partial discharges In-depth examinations of the monitoring of partial discharge behavior, including methods and realization Perfect for electrical engineers engaged in electrical power engineering, Partial Discharges (PD) will also earn a place in the libraries of research and development specialists employed in the manufacturing, quality testing and operation of electrical systems.

Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach is a comprehensive overview of the growing interests in efficient diagnosis of power system faults to reduce outage duration and revenue losses by expediting the restoration process.This book illustrates intelligent fault diagnosis schemes for power system networks, at both transmission and distribution levels, using data acquired from phasor measurement units. It presents the power grid modeling, fault modeling, feature extraction processes, and various fault diagnosis techniques, including artificial intelligence techniques, in steps. The book also incorporates uncertainty associated with line parameters, fault information (resistance and inception angle), load demand, renewable energy generation, and measurement noises. Provides step-by-step modeling of power system networks (distribution and transmission) and faults in MATLAB/SIMULINK and real-time digital simulator (RTDS) platforms Presents feature extraction processes using advanced signal processing techniques (discrete wavelet and Stockwell transforms) and an easy-to-understand optimal feature selection method Illustrates comprehensive results in the graphical and tabular formats that can be easily reproduced by beginners Highlights various utility practices for fault location in transmission networks, distribution systems, and underground cables.

This book contains 5 chapters that discusses the following topics: Parametric signal processing approach; The signal demodulation techniques; Kullback-Leibler divergence for incipient fault diagnosis; Higher-order spectra and Fault detection and diagnosis based on principal component analysis.

This book provides comprehensive coverage of the latest advances and trends in information technology, science and engineering. Specifically, it addresses a number of broad themes, including multi-modal informatics, data mining, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The book covers a wide range of topics such as AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and a useful reference guide for newcomers to the field. This book is a compilation of the papers presented in the 4th International Conference on Multi-modal Information Analytics, held online, on April 23, 2022.

In this book, a number of innovative fault diagnosis algorithms in recently years are introduced. These methods can detect failures of various types of system effectively, and with a relatively high significance.

This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty. Renewable and non-renewable sources of energy are being used to supply the demands of societies worldwide. These sources are mainly thermo-chemo-electro-mechanical systems that are subject to uncertainty in future loading conditions, material properties, process noise, and other design parameters.It book informs the reader of existing and new ideas that will be implemented in RUL prediction of energy systems in the future. The book provides case studies, illustrations, graphs, and charts. Its chapters consider engineering, reliability, prognostics and health management, probabilistic multibody dynamical analysis, peridynamic and finite-element modelling, computer science, and mathematics.