A Bayesian Network Model for Distribution Network Topology Modification

Due to the continuous expansion of the distribution network, the current management recording method is lacking, and the branch records during the operation of the distribution network are not accurate, which may lead to the deviation of the state estimation. With the development of modern big data, it is convenient to collect a large number of feeder data. By analyzing the correlation between voltage and power in the branches of the distribution network, the structure of the distribution network can be identified and corrected. In this paper, an algorithm based on Bayesian model is proposed to analyze the decision coefficients extracted from DistFlow to realize the identification and correction of the existing topology structure. Experimental results show that this method has an efficient correction effect.