Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage

Heavy losses in the wind power generation system are incurred when the wind turbine blades are severely damaged beyond repair. Therefore, an immediate repair at an initial stage is an economical method. There are limitations and difficulties in diagnosing initial damage to wind turbine blades. For example, the measurement cannot be performed during actual operation, the diagnostic machine learning model calculations are too complex, and so on. This study measured the noise signals in the operation of wind turbines. The k-nearest neighbor (k-NN) of supervised learning was used as the diagnostic method. The calculation of generalized fractal dimensions (GFDs) was used as diagnostic feature selection. The result shows an accuracy of 98.9%. Compared to other algorithms, the k-NN algorithm is simple and easy to understand and implement. To reduce the amount of calculation of the machine learning model, the optimum numerical combination of three major parameters is found in this study. These major parameters are (1) scale index of GFDs, (2) number of neighbor points in the algorithm, and (3) range formula. According to the findings, the k-NN algorithm model can achieve high ac-curacy. The optimum numerical combination of three major parameters provides a rapid, convenient, and efficient diagnostic method.