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Performance optimization of fault di...
~
Wu, Dinghui.
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Performance optimization of fault diagnosis methods for power systems
Record Type:
Electronic resources : Monograph/item
Title/Author:
Performance optimization of fault diagnosis methods for power systems/ by Dinghui Wu ... [et al.].
other author:
Wu, Dinghui.
Published:
Singapore :Springer Nature Singapore : : 2023.,
Description:
xiii, 127 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:
Introduction -- Fault Diagnosis of Variable Pitch for Wind Turbine Based on Multi-innovation Forgetting Gradient Identification Algorithm -- Active Fault-tolerant Linear Parameter Varying Control for the Pitch Actuator of Wind Turbines -- Fault Estimation and Fault-tolerant Control of Wind Turbines Using the SDW-LSI Algorithm -- A New Fault Diagnosis Approach for the Pitch System of Wind Turbines.
Contained By:
Springer Nature eBook
Subject:
Fault location (Engineering) -
Online resource:
https://doi.org/10.1007/978-981-19-4578-6
ISBN:
9789811945786
Performance optimization of fault diagnosis methods for power systems
Performance optimization of fault diagnosis methods for power systems
[electronic resource] /by Dinghui Wu ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xiii, 127 p. :ill. (some col.), digital ;24 cm. - Engineering applications of computational methods,v. 92662-3374 ;. - Engineering applications of computational methods ;v. 9..
Introduction -- Fault Diagnosis of Variable Pitch for Wind Turbine Based on Multi-innovation Forgetting Gradient Identification Algorithm -- Active Fault-tolerant Linear Parameter Varying Control for the Pitch Actuator of Wind Turbines -- Fault Estimation and Fault-tolerant Control of Wind Turbines Using the SDW-LSI Algorithm -- A New Fault Diagnosis Approach for the Pitch System of Wind Turbines.
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.
ISBN: 9789811945786
Standard No.: 10.1007/978-981-19-4578-6doiSubjects--Topical Terms:
649702
Fault location (Engineering)
LC Class. No.: TA169.6
Dewey Class. No.: 620.00452
Performance optimization of fault diagnosis methods for power systems
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Introduction -- Fault Diagnosis of Variable Pitch for Wind Turbine Based on Multi-innovation Forgetting Gradient Identification Algorithm -- Active Fault-tolerant Linear Parameter Varying Control for the Pitch Actuator of Wind Turbines -- Fault Estimation and Fault-tolerant Control of Wind Turbines Using the SDW-LSI Algorithm -- A New Fault Diagnosis Approach for the Pitch System of Wind Turbines.
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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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Intelligent Technologies and Robotics (SpringerNature-42732)
based on 0 review(s)
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W9451107
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EB TA169.6
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