A New Stochastic Search Technique Combined With Scenario Approach for Dynamic State Estimation of Power Systems

In this paper, a new closed loop dynamic state estimation (DSE) method is proposed providing state forecasts (before receiving the corresponding measurements) in addition to state estimation (after receiving the measurements). This method comprises a new stochastic search technique and scenario generation approach. The proposed stochastic search technique, benefiting from high search capability, is a new hybridization of differential evolution and bacterial foraging methods. The suggested scenario generation approach is composed of bus load prediction, lattice Monte Carlo simulation (LMCS) and optimal power flow (OPF). This approach can model bus load forecast uncertainty. Most of existing state estimation methods (such as weighted least square) can provide state estimations only in cases that the power system is observable. However, the proposed DSE method can solve the state estimation problem for both observable and unobservable power systems with reasonable accuracy. The proposed DSE method is extensively tested on the well-known IEEE 30-bus and IEEE 118-bus test systems with different sets of measurements and its obtained results are compared with the results of some other state estimation methods. These comparisons confirm the validity of the developed approach.