Condition monitoring and fault detection of wind turbines and related algorithms: A review

Hameed, Z.; Hong, Yeon Sik; Cho, Y.M.; Ahn, Sung‐Hoon; Song, Chul Ki

doi:10.1016/j.rser.2007.05.008

Cited by 871 publications

(559 citation statements)

References 21 publications

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“…On the other hand, there are very few attempts to create system for intelligent monitoring based on artificial intelligence -see [7] and references given there. The experiments described in this paper show that ART ANNs can be effective tool for such task performing -the symptoms of a turbine damage can be detected using ART ANN.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, condition monitoring of wind turbines, including fault diagnostics, in particular at the early stage of a fault occurrence or even participatory actions, is an essential problem in wind turbines engineering in particular [7,9,12] and in rotating machinery engineering in general [1]. There were several attempts to develop various monitoring tools, in most cases based on various classification methods.…”

Section: Introductionmentioning

confidence: 99%

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ART-2 Artificial Neural Networks Applications for Classification of Vibration Signals and Operational States of Wind Turbines for Intelligent Monitoring

Barszcz

Bielecki

Wójcik

et al. 2013

Lecture Notes in Mechanical Engineering

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SummaryIn recent years wind energy is the fastest growing branch of the power generation industry. The largest cost for the wind turbine is its maintenance. A common technique to decrease this cost is a remote monitoring based on vibration analysis. Growing number of monitored turbines requires an automated way of support for diagnostic experts. As full fault detection and identification is still a very challenging task, it is necessary to prepare an otentially dangerous. There were several attempts to develop such tools, in most cases based on various classification methods. As the ART neural networks are capable to perform efficient classification and to recognize new states when necessary, they seems to be a proper tool for classification of vibration signals of bearing in gears in wind turbines. The verification of ART-2 networks efficiency in this task is the topic of this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ART-2 Artificial Neural Networks Applications for Classification of Vibration Signals and Operational States of Wind Turbines for Intelligent Monitoring

Barszcz

Bielecki

Wójcik

et al. 2013

Lecture Notes in Mechanical Engineering

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“…A power spectrum extraction method based on wavelet energy feature coefficient was proposed in Ref. [39]. In this method, vibration signals generated from operating rotating machinery are analyzed.…”

Section: Condition Monitoring Of Rotors Of Pmsgsmentioning

confidence: 99%

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Huang

Xiao

et al. 2017

Front. Mech. Eng.

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Electric power conversion system (EPCS), which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine (DD-WT). However, this component accounts for the most failures (approximately 60% of the total number) in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring (CM) and fault diagnostics (FD). Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

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“…They are the backbone of the distributed generation, and the loss of communication significantly reduces the effectiveness of the system. From the above discussions, it is clear that lightning discharge must be considered one of the main variables in estimating risk factors [8][9][10]. It has to be evaluated meticulously in order to adjust the cash flows of power supply plants [11].…”

Section: Introductionmentioning

confidence: 99%

Lightning monitoring system for sustainable energy supply: A review

Sidik¹,

Shahroom²,

Salam³

et al. 2015

Renewable and Sustainable Energy Reviews

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Condition monitoring and fault detection of wind turbines and related algorithms: A review

Cited by 871 publications

References 21 publications

ART-2 Artificial Neural Networks Applications for Classification of Vibration Signals and Operational States of Wind Turbines for Intelligent Monitoring

ART-2 Artificial Neural Networks Applications for Classification of Vibration Signals and Operational States of Wind Turbines for Intelligent Monitoring

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Lightning monitoring system for sustainable energy supply: A review

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