Intelligent Perception of Distribution Network State Based on Multi-source Data Fusion

Abstract

This article proposes an intelligent state perception method based on multi-source data fusion to address the issue of the difference between the state perception range and the actual range caused by the abnormal operation status of active distribution networks. In this paper, by combining Ant colony optimization algorithms and fusion rules, a multi-source data fusion model of the operation situation is constructed. The operation situation indicators, such as line loss rate and light load rate, are determined. Using multi-source data fusion information entropy to train samples, the range of abnormal operating states of the power grid is determined. By fusing multi-source voltage data and using the multi-source data fusion method to distinguish voltage data, four operating state perception results of voltage are obtained. Finally, the perception data is modified using Analytic Hierarchy Process to ensure that the state perception results meet the compatibility and consistency requirements with the actual results. According to the experimental results, the proposed method can accurately perceive the fluctuation of abnormal data within 40~50 ranges, which is consistent with the actual value fluctuation range, so as to obtain accurate perception results under different operating conditions.