Adaptive Particle Filter With Randomized Quasi-Monte Carlo Sampling for Unbalanced Distribution System State Estimation

Particle filter (PF) is an effective state estimation method for nonlinear unbalanced distribution systems with non-Gaussian noises. However, the computational inefficiency limits its real-time application in large-scale distribution systems. This paper proposes an adaptive PF (APF) method by using randomized quasi-Monte Carlo (RQMC) sampling to enhance computational efficiency. Firstly, an RQMC sampling method is introduced to enable uniform sampling based on the low discrepancy Sobol’ sequence. This even sampling accelerates the convergence rate of numerical integrations. Secondly, the standard PF is extended to be an RQMC-PF estimator by using the RQMC sampling instead of the MC sampling. Benefiting from the higher convergence rate, fewer particles are required to obtain precise state estimates, thus notably improving computation efficiency. Thirdly, to further make a trade-off between estimation accuracy and computational efficiency, the number of particles used in the RQMC-PF is adaptively determined with the root mean square of the estimated state covariance, yielding an APF. Test results on unbalanced distribution systems with different scales demonstrate that the proposed method can efficiently provide accurate estimates as well as achieve good scalability.