Virtual impedance based low-voltage distribution grid topology detection using double hidden layer recurrent neural network

Abstract

Low-voltage distribution grid (LVDG) topology detection refers to detecting whether the topology connection between distribution grid nodes is correct. Accurate topology connection is critical for the normal operation and planning of LVDG. However, due to the incomplete measurement device, unknown line parameters, and rapid growth of renewable energy, the topology detection of LVDG becomes one of the most prominent challenges. This paper proposes an LVDG topology detection method based on virtual impedance, utilizing measurement data from nodes in the LVDG to achieve the detection of abnormal topological connections. Specifically, the electrical distances between nodes are analyzed to establish a topology detection model using virtual impedance. Then, the double hidden layer recurrent neural network is proposed to fit the mapping relationships between variables in the power flow constraints. The virtual impedance between nodes is solved. The value of virtual impedance is used to determine whether the topological connection between nodes is correct. Finally, the test results in the actual LVDG prove the effectiveness of the proposed method.