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.