книга Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults

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Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults

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Название: Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults
Автор: Nabamita Banerjee Roy, Kesab Bhattacharya
Страниц: 144
Формат: PDF
Размер: 13.3 MB
Качество: Отличное
Язык: Английский
Год издания: 2022


Accurate, fast, and reliable fault classification techniques are an important operational requirement in modern-day power transmission systems. Application of Signal Processing Tools and Neural Network in Diagnosis of Power System Faults examines power system faults and conventional techniques of fault analysis. The authors provide insight into artificial neural networks and their applications, with illustrations, for identifying power system faults. Wavelet transform and its application are discussed as well as an elaborate method of Stockwell transform.



The authors also employ probabilistic neural networks (PNN) and back propagation neural networks (BPNN) to identify the different types of faults and determine their corresponding locations, respectively. Both PNN and BPNN are presented in detail, and their applications are illustrated through simple programming in MATLAB. Furthermore, their applications in fault diagnosis are discussed through multiple case studies.



The soft computing techniques have shown relatively better performance in the method of fault classification with respect to speed and accuracy. The methods mainly involve the simulations of network and faults in reliable softwares like EMTP, PSCAD, and MATLAB, involving the application of signal processing tools, i.e., Wavelet transform (WT) and S-Transform (ST).



Discrete Wavelet Transform (DWT) is a powerful signal analysis tool which has been extensively used for fault detection in transmission lines. Several distinctive features are mainly extracted from the line current or voltage signals after they are being processed through DWT. Subsequently, the aforesaid extracted features are fed to a Genetic Algorithm-based fault classifier or neural network for fault classification.



Features:

  • 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 application 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 Stockwell transform


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