Dynamic Topology Awareness in Active Distribution Networks Under DG Uncertainties Using GMM-PSEs and KL Divergence

Abstract

The frequent switching operations and triggering events of automation devices are poorly documented in distribution management systems, leading to challenging work regarding the timely detection of system state changes. Additionally, the limited measurements in distribution networks and the randomness of plug-and-play devices make obtaining real-time observations of system states difficult. Furthermore, distributed generation (DG) output uncertainties exacerbate the situation. Thus, we propose a dynamic topology awareness (DTA) approach to make judgments regarding operational states and identify all possible topology changes (including normal operations, islanding and outages) in the absence of real-time DG measurements. The method consists of two steps. The first step applies parallel Gaussian mixture model-based probabilistic state estimators (GMM-PSEs) of subareas and extended subareas to calculate the probability distributions of border bus voltages with full consideration of DG uncertainties. The second step utilizes the Kullback-Leibler (KL) divergence measure to evaluate the difference between the two probability distributions estimated by subareas and extended subareas, thereby obtaining an indicator (average KL divergence) for identifying possible topologies. The effectiveness of the proposed method is examined on the IEEE 33-bus and IEEE 123-bus test cases. It is computationally efficient and accurate when identifying all possible topologies under limited measurements and DG uncertainties.