Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been...

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Autores principales:	Ling Zhou, Qiancheng Zhao, Xian Wang, Anfeng Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	auto-associative neural network sparrow search algorithm anemometer fault diagnosis cluster analysis Technology T
Acceso en línea:	https://doaj.org/article/3d2cefc4a313421a98ef107165d3e8c7
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spelling	oai:doaj.org-article:3d2cefc4a313421a98ef107165d3e8c72021-11-11T15:44:55ZFault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN10.3390/en142169051996-1073https://doaj.org/article/3d2cefc4a313421a98ef107165d3e8c72021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6905https://doaj.org/toc/1996-1073When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).Ling ZhouQiancheng ZhaoXian WangAnfeng ZhuMDPI AGarticleauto-associative neural networksparrow search algorithmanemometerfault diagnosiscluster analysisTechnologyTENEnergies, Vol 14, Iss 6905, p 6905 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	auto-associative neural network sparrow search algorithm anemometer fault diagnosis cluster analysis Technology T
spellingShingle	auto-associative neural network sparrow search algorithm anemometer fault diagnosis cluster analysis Technology T Ling Zhou Qiancheng Zhao Xian Wang Anfeng Zhu Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
description	When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).
format	article
author	Ling Zhou Qiancheng Zhao Xian Wang Anfeng Zhu
author_facet	Ling Zhou Qiancheng Zhao Xian Wang Anfeng Zhu
author_sort	Ling Zhou
title	Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
title_short	Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
title_full	Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
title_fullStr	Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
title_full_unstemmed	Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN
title_sort	fault diagnosis and reconstruction of wind turbine anemometer based on rwssa-aann
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3d2cefc4a313421a98ef107165d3e8c7
work_keys_str_mv	AT lingzhou faultdiagnosisandreconstructionofwindturbineanemometerbasedonrwssaaann AT qianchengzhao faultdiagnosisandreconstructionofwindturbineanemometerbasedonrwssaaann AT xianwang faultdiagnosisandreconstructionofwindturbineanemometerbasedonrwssaaann AT anfengzhu faultdiagnosisandreconstructionofwindturbineanemometerbasedonrwssaaann
_version_	1718434078936530944

Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

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