The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

Yancai Xiao,Guangjian Zhang,Weijia Chen,Ruolan Dai

doi:10.3390/en10111877

Yancai Xiao, Guangjian Zhang + Show 2 more

Open Access

https://doi.org/10.3390/en10111877

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Abstract

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.

Highlights

Wind energy is a kind of clean and renewable energy
In literature [34], a hybrid model based on wavelet transform (WT) and least squares support vector machine was proposed to forecast short-term load
In order to solve the sparsity of least squares support vector machine (LSSVM), an improved LSSVM based on pruning algorithm is proposed to predict the temperature of high-speed output shaft of the gearbox in this paper

Summary

Introduction

Wind energy is a kind of clean and renewable energy. Wind energy generation has a positive significance to improving the diversity of energy supply and reducing environmental pollution [1]. Monitoring temperature of high-speed output shaft of gearbox and predicting its changes in the latter according to the current temperature is very important for improving the performance of the transmission system and realizing the condition maintenance of wind turbines [6]. Guo Peng et al used the method of temperature trend analysis to monitor the operation state of gearbox in wind turbines They used the method of nonlinear state estimation to establish the gearbox temperature model under normal working condition and predict the temperature [10]. In literature [15], wind turbine fault prediction methodology is proposed by using the support vector machine (SVM) method. Used the unequal interval grey model to predict the generator speed and temperature of the wind turbines [17] These introduced methods have their own characteristics. In literature [34], a hybrid model based on wavelet transform (WT) and least squares support vector machine was proposed to forecast short-term load

Method

LSSVM Prediction Algorithm

Joint Normalization of Data

PCA Dimension Reduction Processing

PSO Algorithm

Forecasting Evaluation Index

Predictive

Results

Conclusions