Performance Optimization of Fault Diagnosis Methods for Power Systems (Hardcover, 1st ed. 2023)

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This book focuses on the performance optimization of fault diagnosis methods for power systems including both model-driven ones, such as the linear parameter varying algorithm, and data-driven ones, such as random matrix theory. Studies on fault diagnosis of power systems have long been the focus of electrical engineers and scientists. Pursuing a holistic approach to improve the accuracy and efficiency of existing methods, the underlying concepts toward several algorithms are introduced and then further applied in various situations for fault diagnosis of power systems in this book. The primary audience for the book would be the scholars and graduate students whose research topics including the control theory, applied mathematics, fault detection, and so on.

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Product Description

This book focuses on the performance optimization of fault diagnosis methods for power systems including both model-driven ones, such as the linear parameter varying algorithm, and data-driven ones, such as random matrix theory. Studies on fault diagnosis of power systems have long been the focus of electrical engineers and scientists. Pursuing a holistic approach to improve the accuracy and efficiency of existing methods, the underlying concepts toward several algorithms are introduced and then further applied in various situations for fault diagnosis of power systems in this book. The primary audience for the book would be the scholars and graduate students whose research topics including the control theory, applied mathematics, fault detection, and so on.

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Product Details

General

Imprint

Springer Verlag, Singapore

Country of origin

Singapore

Series

Engineering Applications of Computational Methods, 9

Release date

September 2022

Availability

Expected to ship within 10 - 15 working days

First published

2023

Authors

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Dimensions

235 x 155mm (L x W)

Format

Hardcover

Pages

127

Edition

1st ed. 2023

ISBN-13

978-981-19-4577-9

Barcode

9789811945779

Categories

LSN

981-19-4577-2



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