Research on multi-source partial discharge localization based on improved FCM-LOF fuzzy clustering algorithm

Abstract

At present, the ultra-high frequency method is widely used in the single-source partial discharge (PD) location of substation sites; however, there are issues with the positioning accuracy being too low, and it is difficult to meet the needs of multi-source PD positioning. Based on the local outlier factor (LOF) outlier idea and fuzzy c-means (FCM) clustering algorithm, this paper proposes the FCM-LOF algorithm to be applied to the research of multi-source PD positioning. This algorithm removes the discrete points of the original time difference data set based on the principle of the 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, using smoothing filter processing and the energy accumulation method, we collect the time difference of dual- and triple-source PD signals as the data set to be processed, and carry out the laboratory simulation experiment. Secondly, comparing the clustering effects of the k-means and FCM algorithms, it is found that the clustering accuracy of the FCM algorithm is significantly better than that of the k-means algorithm, and the positioning error is reduced by 27.3%. Then, using 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 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. We also study the factors affecting the positioning accuracy of the improved algorithm. The research in this paper can provide a powerful reference for multi-source PD detection of power equipment.