Dynamic state estimation of power systems using intelligent particle filtering based on ant colony optimisation for continuous domains

In this study, a dynamic state estimation (DSE) approach is proposed for power systems based on particle filter (PF) and ant colony optimisation for continuous domains (ACO(R)). Usually, the Kalman-based estimators with Gaussian noise assumption are utilised for DSE. However, this assumption is questionable for real power system data. Although PF methods offer a potential solution for this issue, the basic PF algorithm is time-consuming and suffers from sample impoverishment problem and degeneracy of the propagated samples. In this study, the search capability of ACO(R) is utilised as an adaptive probability density function estimator to overcome these shortcomings and reduce the required particle numbers. The proposed approach minimises the computational effort by reducing the required particle numbers. The ninth-order model of the synchronous generator has been applied in this study. The performance of the proposed method is investigated through a two-area-four-machine test system as well as the IEEE 39-bus (New England) test system and it is compared with several advanced Kalman filter-based and PF-based approaches. The simulation results obtained for different case studies demonstrate the effectiveness and robustness of the proposed method against noises, abrupt state changes, and gross measurement errors.