A Heuristic-Search-Based Topology Identification and Parameter Estimation Method in Low Voltage Distribution Grids With Low Observability

Accurate topology and line parameter information in a Low-Voltage Distribution Grid (LVDG) facilitates better grid monitoring, enhances fault detection, and optimizes grid operation to meet the demands of lean and intelligent management. However, LVDGs are generally characterized by low observability due to the ever-changing network structure and the lack of detection equipment. This paper presents a novel method to accurately identify topology and estimate line parameters in LVDGs with low observability. Firstly, a Distance Topology Matrix (DTM) is introduced to depict grid topology and a unique scoring mechanism is introduced to evaluate how well a DTM candidate fits the observation data. Then an enhanced Stochastic Fractal Search is proposed to find the optimized DTM. With the optimized DTM, an Improved Hierarchical Clustering approach is adopted to further recover the complete topology in the grid. The proposed method is applicable to large-scale LVDGs with unknown topology and line parameter information in the absence of phase angle information. Furthermore, this method can recover the complete topology, even in LVDGs with numerous latent nodes. Numerical test results with practical low-voltage grids from Shenzhen and the IEEE European Low Voltage Test Feeder with 55 loads demonstrate the accuracy and robustness of the proposed method.