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The primary goal of the electric utilities is to deliver safe, reliable, and satisfying power supply to their customers. In modern society, customers are more sensitive to the interruptions resulting from network faults. Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach is a comprehensive overview of the growing research interest in diagnosing the faults in both transmission lines and distribution grids efficiently to reduce outage duration and revenue losses by expediting the restoration process. This book illustrates an intelligent fault diagnosis (IFD) scheme for power system networks (distribution and transmission) incorporating the load demand and renewable energy generation uncertainties combining advanced signal processing techniques and machine learning tools. It presents the grid modelling, fault modelling, and fault diagnosis techniques in steps that will benefit the readers in designing their models. By modeling power distribution networks considering the integration of RER along with the uncertainty associated with load demand and renewable energy generation, it employs machine learning tools (feed-forward neural network, support vector machine, and extreme learning machine) in combination with the advanced signal processing techniques (discrete wavelet transform and Stockwell transform) for development of the IFD schemes. Moreover, this book presents various adaptive fault diagnosis schemes for two-terminal, three-terminal, and series compensated transmission lines, presenting the grid modelling, fault modelling, and fault diagnosis techniques in steps that will benefit postgraduate students of electrical power and energy systems in designing models. Provides step-by-step modeling of power system networks (distribution and transmission) and faults in MATLAB/SIMULINK and real-time digital simulator (RTDS) platformsPresents the feature extraction processes using advanced signal processing techniques (discrete wavelet and Stockwell transforms) and an easy-to-understand optimal feature selection methodIllustrates comprehensive results in the graphical and tabular formats that can be easily reproduced by any beginner researcher
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