Research on fault diagnosis of wind turbine control system based on Artificial Neural Network

This paper presents an algorithm of Artificial Neural Network (ANN) pattern recognition method which is applied to the operations of wind turbine control system (WTCS). This paper presents two kinds of improved algorithms of Neural Network (NN) based on the basic principles to improve the convergence speed of the network. To avoid the network falling into the local minimum the genetic algorithm for optimization of neural network fault diagnosis method has been successfully applied to the WTCS. Firstly, this paper proposes several improved training algorithms of neural network. It also makes simulation using the existing data. Then, several WTCS sensor faults which are made by artificial are simulated. Finally, six kinds of WTCS failures that often occur are simulated by using the neural network mode which is optimized by genetic algorithm. The simulation results prove that the improved algorithm is a fast and efficient method which avoids the network falling into the local minimum and it also shows that the used neural network has excellent ability which is famous for parallel processing ability, associative memory, self organizing and self learning.

I. INTRODUCTION

Wind power is a kind of renewable energy and plays an important role in the future energy supply. Without question, the safe o peration of control system which is the center of wind turbine is essential to ensure the running of the unit. However, wind turbine usually works under the bad environment. The gearbox and bearing failure [1]–[3] and various sensor faults often occur, such as sensor bias fault, sensor constant gains and so on. When fault occurs it will seriously affect the engineering quality and cause great economic losses. So the fault diagnosis of wind turbine control system is becoming more of a concern.