Distributed multi-agent fusion state estimation method based on finite-time average consensus for large-scale power systems

Considering that the increasing scale of power systems may lead to high measurement transmitted load and the large amount of measurements also includes many bad data and outliers, a novel distributed multi-agent fusion state estimation (DMFSE) method leveraging the finite-time average consensus algorithm and influence function is proposed for large-scale power systems in this paper. Large-scale power systems are partitioned into multiple subareas, where each subarea deploys a local estimator. Measurements from each subarea are sent directly to their respective local estimator rather than to the central estimator, which reduces the burden of extensive data transmission. The finite-time average consensus algorithm and the influence function are combined together so as to make each local estimator obtain the global state estimation results. The optimization function for the proposed DMFSE method is derived from the generalized correntropy loss function, aiming to mitigate issues arising from bad data and outliers. The simulation results obtained from the IEEE 30-bus, 118-bus and 300-bus systems demonstrate the superior performances of the proposed DMFSE method.