A Local Density Clustering Based Method for Disturbance Source Identification of Power quality in Distribution Systems

Abstract

This paper presents a power quality disturbance identification method based on wavelet transform (WT) and local density clustering (LDC). Wavelet transform is used to extract the characteristic value of power quality disturbance, and multi-level local density clustering is used to construct a classifier for power quality disturbance classification and recognition. Firstly, the time series signal of power quality disturbance is decomposed into low-frequency and high-frequency signals by wavelet decomposition. Combined with the difference of each signal in wavelet energy spectrum, the characteristic value of power quality disturbance is extracted, which is used as the sample of power quality characteristic value for LDC clustering, and the power quality classification model is established. Finally, the classification model is used to classify power quality disturbances. In the example experiment, eight common power quality disturbances and two corresponding composite disturbances are selected for classification and identification. Experimental results show that this method can effectively classify a variety of power quality problems, and multi-level local density clustering has low computational complexity, high recognition accuracy and less time-consuming.