Unscented Particle Filter Algorithm Towards Data Quality Improvement in Sustainable Distribution Power Systems

Abstract

Sustainable development of power and energy systems (PES) can effectively handle challenges of fuel shortage, environmental pollution, climate change, energy security, etc. Data of PES presents distinctive characteristics including large collection, wide coverage, diverse temporal and spatial scales, inconsistent sparsity, multiple structures and low value density, putting forward higher requirements for real-time and accuracy of data analysis, and bringing great challenges to operation analysis and coordinated control of PES. In order to realize data quality improvement and further support flexible choice of operating mode, safe and efficient coordinated control, dynamic and orderly fault recovery of sustainable PES, this paper proposes an unscented particle filter algorithm, adopting unscented Kalman filter to construct importance density functions and KLD resampling to dynamically adjust the particle number. Simulation results obtained by taking an 85-node system as a benchmark for simulation verification show that compared with traditional PF algorithm and UKF algorithm, UPF algorithm has higher estimation accuracy.