Complex-Valued Forecasting-Aided State Estimator on FPGA for Distribution Systems

With the continuous integration of distributed generations (DGs) and flexible loads, accurate real-time forecasting-aided state estimation (FASE) is increasingly crucial for the safe operation of distribution systems. However, compared to the static state estimator (SSE) that utilizes information from only a single time instance, the Kalman filter (KF)-based FASE, while capable of leveraging predicted information to enhance state tracking performance, faces challenges in practical application due to heavy computational cost. This article proposes a complex domain constant Jacobian matrix FASE method, which enables fast and robust state estimation for distribution systems. Moreover, a corresponding massively parallel processing scheme is constructed utilizing low-power field-programmable gate arrays (FPGAs) to further enhance computational efficiency, ensuring the reliability of faster-than-real-time (FTRT) hardware-in-the-loop (HIL) applications. Case studies conducted on single-phase IEEE 33-bus as well as three-phase unbalanced IEEE 13- and 123-bus distribution systems with real-world load and DG profiles validate the advantages of the proposed method.