Research On Fault Diagnosis Of Wind Turbine Gearbox Based On IFA-ELM

In order to effectively improve the accuracy of fault diagnosis of wind turbine gearbox, a fault diagnosis model based on improved firefly algorithm for extreme learning machine is proposed in this paper. The extreme learning machine overcomes the shortcomings of traditional neural network, such as slow convergence rate and easy to fall into local minimum points, however, the weights and thresholds of the input layer and the hidden layer are generated in a random way, this can lead to excessive number of nodes in the hidden layer, resulting in over fitting in the training process. In view of this problem, the firefly algorithm with high searching speed and high efficiency is used to optimize the parameters of the extreme learning machine. However, because of the fixed step size, the firefly algorithm is easy to fall into the local optimum in the early stage and slows down in the late convergence. Therefore, the step size of the firefly algorithm is improved to make it change with the change of the objective function in the search process so as to improve the performance of the firefly algorithm. The experimental results show that compared to the standard ELM, GA-ELM, and FA-ELM networks, the improved firefly algorithm for extreme learning machine that proposed in this paper has a higher prediction accuracy. Introduction Gearbox is the key component of the wind turbine, its normal operation is related to the working performance of the whole unit. Due to the complex structure and the poor working environment, the gearbox is very easy to malfunction. According to statistics, the fault of gearbox accounts for about 60% of the total fault of wind turbine. Due to the gearbox space is narrow and located in the upper air, it is very difficult to maintain. Therefore, it is important to diagnose the fault of gearbox quickly and accurately, which is of great significance to reduce the cost of wind power operation and maintenance and the operation reliability of wind turbines. In recent years, many scholars have done a lot of research on fault diagnosis of gear box. In this paper the extreme learning machine with faster training speed and better generalization ability is applied to fault diagnosis of gearbox of wind turbine. The Extreme Learning Machine(ELM)is a new learning algorithm based on single hidden layer feedforward neural network proposed by Huang guangbin in 2004.Different from the traditional neural network, the most important feature of ELM algorithm is the input weights and hidden layer bias are generated during the training process randomly. The whole process is completed at once, without iteration. Compared with the traditional neural network, ELM algorithm is faster, and the generalization ability of algorithm is stronger. At present, the ELM algorithm has been applied in the fields of face recognition, image classification, fault diagnosisand load forecasting. However, the input parameters of the input layer and the threshold of the hidden layer are generated randomly, which affect the stability of the network structure to a certain extent. Whether the parameters selection are proper or not affect the performance of the ELM algorithm. In order to obtain better weights and bias, the meta heuristic optimization algorithm —firefly algorithm is used to optimize the ELM algorithm in this paper. 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) Copyright © 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 50