Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE

Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE

After fault occurs, the fault diagnosis of wind turbine system is required accurately and quickly. This paper presents a fault diagnostic method for open-circuit faults in the converter of permanent magnet synchronous generator drive for the wind turbine. To avoid misjudgement or missed judgement ca...

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Main Authors: Hao Duan, Ming Lu, Yongteng Sun, Jinyu Wang, Cheng Wang, Zuguo Chen
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/5306473
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spelling doaj-83157ae5858a49ebb4929e9fdd1fb5e12020-11-25T03:07:54ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/53064735306473Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSEHao Duan0Ming Lu1Yongteng Sun2Jinyu Wang3Cheng Wang4Zuguo Chen5School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaSchool of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaSchool of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaSchool of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaSchool of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaSchool of Information and Electrical Engineering, Hunan University of Science and Technology, Xiang Tan 411201, ChinaAfter fault occurs, the fault diagnosis of wind turbine system is required accurately and quickly. This paper presents a fault diagnostic method for open-circuit faults in the converter of permanent magnet synchronous generator drive for the wind turbine. To avoid misjudgement or missed judgement caused by improper thresholds, the proposed method applies Local Mean Decomposition and Multiscale Entropy into the converter of wind power system fault diagnosis for the first time. This paper uses a novel multiclass support vector machine to classify the faults hardly diagnosed by other methods. Simulation results show that the method has the characteristics of high adaptability, high accuracy, and less diagnosis time.http://dx.doi.org/10.1155/2020/5306473
collection DOAJ
language English
format Article
sources DOAJ
author Hao Duan
Ming Lu
Yongteng Sun
Jinyu Wang
Cheng Wang
Zuguo Chen
spellingShingle Hao Duan
Ming Lu
Yongteng Sun
Jinyu Wang
Cheng Wang
Zuguo Chen
Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
Complexity
author_facet Hao Duan
Ming Lu
Yongteng Sun
Jinyu Wang
Cheng Wang
Zuguo Chen
author_sort Hao Duan
title Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
title_short Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
title_full Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
title_fullStr Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
title_full_unstemmed Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE
title_sort fault diagnosis of pmsg wind power generation system based on lmd and mse
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description After fault occurs, the fault diagnosis of wind turbine system is required accurately and quickly. This paper presents a fault diagnostic method for open-circuit faults in the converter of permanent magnet synchronous generator drive for the wind turbine. To avoid misjudgement or missed judgement caused by improper thresholds, the proposed method applies Local Mean Decomposition and Multiscale Entropy into the converter of wind power system fault diagnosis for the first time. This paper uses a novel multiclass support vector machine to classify the faults hardly diagnosed by other methods. Simulation results show that the method has the characteristics of high adaptability, high accuracy, and less diagnosis time.
url http://dx.doi.org/10.1155/2020/5306473
work_keys_str_mv AT haoduan faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
AT minglu faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
AT yongtengsun faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
AT jinyuwang faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
AT chengwang faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
AT zuguochen faultdiagnosisofpmsgwindpowergenerationsystembasedonlmdandmse
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