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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Main Authors: Mingzhu Tang, Jiahao Hu, Huawei Wu, Zimin Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-10-01
Series:Frontiers in Energy Research
Subjects:
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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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