Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO

Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established...

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Autores principales:	Xiaojiao Gu, Xiaoying Ma
Formato:	article
Lenguaje:	EN
Publicado:	JVE International 2021
Materias:	generator bearing least squares support vector machine state prediction characteristic parameter Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/7ba122ef75904ba39ec18365c8056219
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spelling	oai:doaj.org-article:7ba122ef75904ba39ec18365c80562192021-11-15T19:20:56ZOperating state prediction for wind turbine generator bearing based on ULSSVM and QPSO1392-87162538-846010.21595/jve.2021.21830https://doaj.org/article/7ba122ef75904ba39ec18365c80562192021-08-01T00:00:00Zhttps://www.jvejournals.com/article/21830https://doaj.org/toc/1392-8716https://doaj.org/toc/2538-8460Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established in accordance with the change law of characteristic parameters over time. Space least squares support vector machine (SLSSVM) model is established in accordance with the law of mutual influence between characteristic parameters. Secondly, the QPSO algorithm is used to optimize the parameters of each least squares support vector machine (LSSVM) model. When the difference between the predicted value and the measured value reaches the minimum, the optimal LSSVM parameter set is output. Then the improved Dempster-Shafer (D-S) theory is used to determine the weights of TLSSVM and SLSSVM. A united model of time LSSVM and space LSSVM is established. The characteristic parameters are predicted. The prediction results and the reference matrix are fused and reduced in dimension. Finally, the generator bearing operating status is predicted based on the location of the prediction results. The results show that the proposed method is helpful to realize the operating state prediction of the wind turbine bearing.Xiaojiao GuXiaoying MaJVE Internationalarticlegenerator bearingleast squares support vector machinestate predictioncharacteristic parameterMechanical engineering and machineryTJ1-1570ENJournal of Vibroengineering, Vol 23, Iss 7, Pp 1563-1577 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	generator bearing least squares support vector machine state prediction characteristic parameter Mechanical engineering and machinery TJ1-1570
spellingShingle	generator bearing least squares support vector machine state prediction characteristic parameter Mechanical engineering and machinery TJ1-1570 Xiaojiao Gu Xiaoying Ma Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
description	Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established in accordance with the change law of characteristic parameters over time. Space least squares support vector machine (SLSSVM) model is established in accordance with the law of mutual influence between characteristic parameters. Secondly, the QPSO algorithm is used to optimize the parameters of each least squares support vector machine (LSSVM) model. When the difference between the predicted value and the measured value reaches the minimum, the optimal LSSVM parameter set is output. Then the improved Dempster-Shafer (D-S) theory is used to determine the weights of TLSSVM and SLSSVM. A united model of time LSSVM and space LSSVM is established. The characteristic parameters are predicted. The prediction results and the reference matrix are fused and reduced in dimension. Finally, the generator bearing operating status is predicted based on the location of the prediction results. The results show that the proposed method is helpful to realize the operating state prediction of the wind turbine bearing.
format	article
author	Xiaojiao Gu Xiaoying Ma
author_facet	Xiaojiao Gu Xiaoying Ma
author_sort	Xiaojiao Gu
title	Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
title_short	Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
title_full	Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
title_fullStr	Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
title_full_unstemmed	Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO
title_sort	operating state prediction for wind turbine generator bearing based on ulssvm and qpso
publisher	JVE International
publishDate	2021
url	https://doaj.org/article/7ba122ef75904ba39ec18365c8056219
work_keys_str_mv	AT xiaojiaogu operatingstatepredictionforwindturbinegeneratorbearingbasedonulssvmandqpso AT xiaoyingma operatingstatepredictionforwindturbinegeneratorbearingbasedonulssvmandqpso
_version_	1718426851222749184

Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO

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