A Kalman Filter Approach for State Estimation in Weakly Monitored Active Distribution Networks

Distribution networks are becoming active to improve security of operation under high net load volatility. Yet, the deployment of active devices creates new challenges to the operation of the weakly monitored distribution networks. One such challenge is to improve state estimation accuracy. This paper proposes to improve the estimation process of active distribution networks by embedding the active devices dynamics into the state dynamic equations, instead of translating them into the static measurement models. This is carried out with the Kalman filter (KF) by making use of the dynamic models and the static measurement models we have proposed earlier for active distribution networks. The paper provides the main steps to implement the estimation framework and illustrates its advantages under slow-responsive distribution network interactions. Results obtained show that the proposed KF approach has better performance than the static counterparts for networks subjected to multiple control decisions - the case where the relevance information on dynamics surpasses the relevance of setpoint information on static models alone.