Randomly Switched Subsystem-Based State Estimation for Active Distribution Networks With Unobservable Conditions

With the high penetration of distributed generators (DGs), state estimation is essential for fast and accurate operational status tracking of active distribution networks (ADNs). However, there are generally unobservable areas in ADNs in ADNs due to the paucity of measurements. Traditional state estimation methods may encounter difficulty coping with limited measurements. Thus, aiming at the accurate and fast state perception of unobservable ADNs, this paper proposes a randomly switched subsystem-based state estimation method. First, the unobservable ADN is modeled as the randomly switched ADN (RSADN). Stochastic observability is defined to distinguish the unobservable conditions, which ensures the feasibility of state estimation. Further, considering different operational conditions, the observability enhancement mechanism is designed under limited measurement conditions to make the network observable and reduce the impact of topology switching on the system. Then, the improved unscented Kalman filter algorithm is utilized to solve the state estimation problem based on state transformation. The results of case studies confirm that the proposed method can achieve more accurate estimation results compared to other methods under unobservable conditions and exhibit satisfactory computational speed in large-scale systems.