Research on Fault Diagnosis of Wind Power System Based on PWKNN Algorithm
In order to accurately diagnose the fault category of wind power system,a diagnosis method based on improved weighted k-nearest neighbor particle swarm optimization algorithm(PWKNN)is proposed in this paper.PWKNN reflects the importance of features by adjusting the weight,and the distance judgment strategy is used to calculate the same probability of multi class classification.The weight and parameter k of PWKNN are optimized by particle swarm optimization(PSO)algo-rithm.The classifier is trained by feature extraction,combined with the Pearson correlation coefficient of feature selection irrel-evant features are eliminated,the output time of the classifier is reduced.Four classification states of 300W wind turbine are tested.The comparison with the traditional classifier shows that the improved PWKNN has higher classification accuracy.Fea-ture selection can reduce the average number of features from 16 to 2.8,and the output time can be reduced by 61％.
万方数据
维普
