Research on failure prediction of wind turbines in offshore wind clusters

Offshore wind is a very promising renewable energy source, and offshore wind power has been widely used. However, the maintenance of offshore wind power is a very inconvenient task, which not only is difficult to operate but also can be very costly, and it is essential to anticipate offshore wind power failures in advance. In this paper, we focus on wind turbine faults for analysis, wherein we use a high-performance radial basis function (RBF) neural network, and to obtain the center of its RBF, the normalization constant and the weighting coefficients from the hidden layer to the output layer for better performance, we use an optimized particle swarm optimization algorithm so as to obtain more accurate parameters and therefore seek better performance. We focus on two very high failure rates of wind turbines for testing, and the results show that our proposed method can predict the occurrence of failures 10-20 h in advance and thus have a good performance.