The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT
In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares suppor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/11/1877 |
| _version_ | 1811277910065020928 |
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| author | Yancai Xiao Ruolan Dai Guangjian Zhang Weijia Chen |
| author_facet | Yancai Xiao Ruolan Dai Guangjian Zhang Weijia Chen |
| author_sort | Yancai Xiao |
| collection | DOAJ |
| description | In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms. |
| first_indexed | 2024-04-13T00:25:13Z |
| format | Article |
| id | doaj.art-036bedb95c6f43d19fb608074d6a43ea |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T00:25:13Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-036bedb95c6f43d19fb608074d6a43ea2022-12-22T03:10:37ZengMDPI AGEnergies1996-10732017-11-011011187710.3390/en10111877en10111877The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWTYancai Xiao0Ruolan Dai1Guangjian Zhang2Weijia Chen3School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Electrical Engineering, North China Electric Power University, Baoding 071003, ChinaIn the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.https://www.mdpi.com/1996-1073/10/11/1877temperature predictionleast squares support vector machine (LSSVM)pruning algorithmjoint normalizationprincipal component analysis (PCA)particle swarm optimization (PSO) |
| spellingShingle | Yancai Xiao Ruolan Dai Guangjian Zhang Weijia Chen The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT Energies temperature prediction least squares support vector machine (LSSVM) pruning algorithm joint normalization principal component analysis (PCA) particle swarm optimization (PSO) |
| title | The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT |
| title_full | The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT |
| title_fullStr | The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT |
| title_full_unstemmed | The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT |
| title_short | The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT |
| title_sort | use of an improved lssvm and joint normalization on temperature prediction of gearbox output shaft in dfwt |
| topic | temperature prediction least squares support vector machine (LSSVM) pruning algorithm joint normalization principal component analysis (PCA) particle swarm optimization (PSO) |
| url | https://www.mdpi.com/1996-1073/10/11/1877 |
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