Wind turbine fault prediction based on genetic algorithm optimization

This paper proposes a wind turbine fault prediction model based on genetic algorithm optimization. Firstly, a large amount of wind turbine operation data is collected and feature engineering processing is completed. Secondly, a fault prediction model is established as a benchmark model based on BP neural network. Again, using genetic algorithm to optimize the parameters of this model to improve its prediction performance can effectively improve its prediction performance. The genetic algorithm continuously optimizes the model parameters to find the optimal solution by simulating the natural biological evolution process. Finally, the optimized model is experimentally verified and compared and analyzed with the traditional method. The experimental results show that the wind turbine fault prediction model optimized based on genetic algorithm achieves significant improvement in both accuracy and generalization ability. Compared with the traditional method, the optimized model is able to predict wind turbine faults more accurately, discover potential problems in advance and take corresponding maintenance measures, so as to improve the reliability and efficiency of the generator.