Research on Multi-source Partial Discharge Localization Based on Improved FCM-LOF Fuzzy Clustering Algorithm

Abstract At present, the UHF method has been widely used in the single-source partial discharge (PD) location of the substation site, but there are problems that the positioning accuracy is not high and it is difficult to meet the needs of multi-source partial discharge positioning. Based on the LOF outlier idea and FCM fuzzy clustering algorithm, this paper proposes the FCM-LOF algorithm to be applied to the research of multi-source partial discharge positioning. This algorithm removes the discrete points of the original time difference data set based on the principle of local density threshold, reduces the clustering center error, and improves the accuracy of multi-source positioning. The main research contents of this article are as follows. Firstly, based on smoothing filter processing and energy accumulation method collect the time difference of dual and triple source partial discharge signals as the data set to be processed, and carry out the laboratory simulation experiment. Secondly, comparing the clustering effects of k-means and FCM algorithms, it is found that the clustering accuracy of FCM algorithm is significantly better than that of k-means algorithm, and the positioning error is reduced by 27.3%. Then, based on the neighborhood density and outlier factor to eliminate abnormal data, combined with FCM fuzzy clustering, an improved FCM-LOF algorithm is proposed. Compared with the FCM algorithm, the positioning error of this algorithm is reduced by 11.6%. It is suitable for multi-source positioning occasions and has a greater improvement in noisy environments. Finally, the improved algorithm is applied to a field simulation test, which verifies the accuracy of the algorithm, and also studies the factors affecting the positioning accuracy of the improved algorithm. The research in this paper can provide a powerful reference for multi-source partial discharge detection of power equipment.