Wind Turbine Pitch System Fault Detection Using ssODM-DSTA
A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the...
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Frontiers Media S.A.
2021-10-01
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Series: | Frontiers in Energy Research |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.750983/full |
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author | Mingzhu Tang Jiahao Hu Huawei Wu Zimin Wang |
author_facet | Mingzhu Tang Jiahao Hu Huawei Wu Zimin Wang |
author_sort | Mingzhu Tang |
collection | DOAJ |
description | A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the pitch system. This method was adopted to input the three hyperparameters of the ssODM into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. Using a random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, the Pearson correlation method is used to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training dataset and a test dataset to train and test the proposed fault detection model, respectively. The real-time wind turbine pitch system fault data were collected from domestic wind farms to carry out fault detection experiments. The results have shown that the proposed method had a positive fault rate (FPR) and fault negative rate (FNR), compared with other optimization algorithms. |
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issn | 2296-598X |
language | English |
last_indexed | 2024-12-20T05:36:17Z |
publishDate | 2021-10-01 |
publisher | Frontiers Media S.A. |
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spelling | doaj.art-0a1f3da3fcfe459c869dfec365b04e112022-12-21T19:51:36ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-10-01910.3389/fenrg.2021.750983750983Wind Turbine Pitch System Fault Detection Using ssODM-DSTAMingzhu Tang0Jiahao Hu1Huawei Wu2Zimin Wang3School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, ChinaHubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, ChinaA fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the pitch system. This method was adopted to input the three hyperparameters of the ssODM into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. Using a random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, the Pearson correlation method is used to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training dataset and a test dataset to train and test the proposed fault detection model, respectively. The real-time wind turbine pitch system fault data were collected from domestic wind farms to carry out fault detection experiments. The results have shown that the proposed method had a positive fault rate (FPR) and fault negative rate (FNR), compared with other optimization algorithms.https://www.frontiersin.org/articles/10.3389/fenrg.2021.750983/fullfault detectionwind turbinepitch systemdynamic state transition algorithmsemi-supervised optimal margin distribution machinerandom forest |
spellingShingle | Mingzhu Tang Jiahao Hu Huawei Wu Zimin Wang Wind Turbine Pitch System Fault Detection Using ssODM-DSTA Frontiers in Energy Research fault detection wind turbine pitch system dynamic state transition algorithm semi-supervised optimal margin distribution machine random forest |
title | Wind Turbine Pitch System Fault Detection Using ssODM-DSTA |
title_full | Wind Turbine Pitch System Fault Detection Using ssODM-DSTA |
title_fullStr | Wind Turbine Pitch System Fault Detection Using ssODM-DSTA |
title_full_unstemmed | Wind Turbine Pitch System Fault Detection Using ssODM-DSTA |
title_short | Wind Turbine Pitch System Fault Detection Using ssODM-DSTA |
title_sort | wind turbine pitch system fault detection using ssodm dsta |
topic | fault detection wind turbine pitch system dynamic state transition algorithm semi-supervised optimal margin distribution machine random forest |
url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.750983/full |
work_keys_str_mv | AT mingzhutang windturbinepitchsystemfaultdetectionusingssodmdsta AT jiahaohu windturbinepitchsystemfaultdetectionusingssodmdsta AT huaweiwu windturbinepitchsystemfaultdetectionusingssodmdsta AT ziminwang windturbinepitchsystemfaultdetectionusingssodmdsta |