Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to...

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Main Authors: Yancai Xiao, Na Kang, Yi Hong, Guangjian Zhang
Format: Article
Language:English
Published: MDPI AG 2017-01-01
Series:Entropy
Subjects:
misalignment
DFWT
IEMD energy entropy
PSO
SVM
Online Access:http://www.mdpi.com/1099-4300/19/1/6
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spelling doaj-dbdd664df43c4aad95eb5f655333c54f2020-11-24T23:23:02ZengMDPI AGEntropy1099-43002017-01-01191610.3390/e19010006e19010006Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVMYancai Xiao0Na Kang1Yi Hong2Guangjian Zhang3School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaMisalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.http://www.mdpi.com/1099-4300/19/1/6misalignmentDFWTIEMD energy entropyPSOSVM
collection DOAJ
language English
format Article
sources DOAJ
author Yancai Xiao
Na Kang
Yi Hong
Guangjian Zhang
spellingShingle Yancai Xiao
Na Kang
Yi Hong
Guangjian Zhang
Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
Entropy
misalignment
DFWT
IEMD energy entropy
PSO
SVM
author_facet Yancai Xiao
Na Kang
Yi Hong
Guangjian Zhang
author_sort Yancai Xiao
title Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
title_short Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
title_full Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
title_fullStr Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
title_full_unstemmed Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
title_sort misalignment fault diagnosis of dfwt based on iemd energy entropy and pso-svm
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2017-01-01
description Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.
topic misalignment
DFWT
IEMD energy entropy
PSO
SVM
url http://www.mdpi.com/1099-4300/19/1/6
work_keys_str_mv AT yancaixiao misalignmentfaultdiagnosisofdfwtbasedoniemdenergyentropyandpsosvm
AT nakang misalignmentfaultdiagnosisofdfwtbasedoniemdenergyentropyandpsosvm
AT yihong misalignmentfaultdiagnosisofdfwtbasedoniemdenergyentropyandpsosvm
AT guangjianzhang misalignmentfaultdiagnosisofdfwtbasedoniemdenergyentropyandpsosvm
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