TAPINE: Topology-Adaptive Physics-Informed Neural Estimator for Fast DSSE

With the increasing integration of distributed energy resources (DERs) and electric vehicles (EVs), fast and accurate distribution system state estimation (DSSE) has become indispensable for real-time monitoring and optimal control of modern power distribution systems. However, conventional DSSE methods face considerable challenges when addressing frequent topology changes. These challenges include high computational costs due to iterative optimization, sensitivity to parameter uncertainties, and limited adaptability of existing data-driven models to evolving network configurations. To overcome these limitations, this paper proposes a topology-adaptive DSSE framework based on a physics-informed autoencoder. The encoder utilizes LEAPNet to extract measurement features and capture topology-dependent variations, thereby generating accurate and topology-aware state estimates. The decoder reconstructs input measurements by embedding Kirchhoff’s laws, ensuring that estimated states adhere to physical power system constraints. Through joint optimization of the encoder and decoder, the network learns physically consistent mappings between measurements and system states. Extensive simulation studies on distribution networks demonstrate that the proposed model achieves superior accuracy, computational efficiency, and physical consistency compared with conventional DSSE approaches.