Wind Turbine Pitch System Fault Detection Using ssODM-DSTA - Tang, Mingzhu; Hu, Jiahao; Wu, Huawei; Wang, Zimin (Frontiers in Energy Research, 2021)
A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution learning machine(ssODM) optimized by dynamic state transition algorithm (DSTA)[ssODM-DSTA]was proposed to solve the problem that it is difficult to obtain the optimal hyper-parameters of the fault detection model for the pitch system. This method was adopted to input the three hyper-parameters of the semi-supervised optimal margin distribution learning machine into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. By using random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, using the pearson correlation method to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training set and a test set, respectively to train and test the proposed fault detection model. By collecting the real-time wind turbine pitch system fault data from domestic wind farms to carry out fault detection experiments. Results showed that the proposed method had fault positive rate(FPR) and fault negative rate(FNR), compared with other optimization algorithms.