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Aiming at solving the problem that the parameters of a fault detection model are difficult to be optimized, the paper proposes the fault detection of the wind turbine variable pitch system based on large margin distribution machine (LDM) which is optimized by the state transition algorithm (STA). By setting the three parameters of the LDM model as a three-dimensional vector which was searched by STA, by using the accuracy of fault detection model as the fitness function of STA, and by adopting the four state transformation operators of STA to carry out global search in the form of point, line, surface, and sphere in the search space, the global optimal parameters of LDM fault detection model are obtained and used to train the model. Compared with the grid search (GS) method, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA), the proposed model method has lower false positive rate (FPR) and false negative rate (FNR) in the fault detection of wind turbine variable pitch system in a real wind farm.

For the large generators with different branch winding potential overlap, the existing stator ground fault location methods are difficult to determine the faulty branch. Therefore, this paper presents a novel fault location method based on the flexible optical current transformer. Based on the Faraday magneto-optical effect principle, the optical fiber reflection reciprocal interference technology is used to detect the rotation angle. The flexible optical current transformer is used to measure the equivalent zero-sequence current of each generator branch, and then the dynamic time warping distance (DTW) algorithm is used to compare the similarity of each zero-sequence current, to identify the correct faulty branch and achieve accurate grounding fault location. The simulation results verify the effectiveness of the proposed method, and the results are still reliable and sensitive when the transition resistance is 500 Ω.