Phase identification in distribution systems by data mining methods

Data mining is one of the statistical means that extracts useful information from an extremely large set of raw data. Therefore, data mining methods are under vigorous development and are commonly used in artificial intelligence fields such as image processing and robot industry. There has also been recently applications of data mining in electric power industry, such as classification, clustering and forecasting. In this research work, clustering techniques are adopted to identify the phase connectivity in power systems. Supported by smart meter data obtained from end-users on the low-voltage (LV) feeder, phase identification is properly discussed in this paper. Firstly, the LV network model is modeled using simulation tool OpenDSS. Secondly, the phase identification algorithm of the LV network is developed in Matlab by using K-means clustering as well as the Gaussian Mixture Model (GMM) clustering. Finally, the IEEE European Low Voltage Test Feeder is used to verify the proposed method. Results: indicate that these two methods enable phase identification to realize its goals, which is to precisely address the active loads as well as the correlated phase of corresponding load.