The invention discloses a power
system dynamic
estimation method based on unscented Kalman particle filtering, and relates to the technical field of power
system monitoring, analysis and control. Themethod comprises the following steps: step 1, initializing a current
state variable of the power
system, and calculating an initial state mean value and variance; 2, setting k as 1; 3, carrying out importance sampling to obtain a particle swarm and a weight at the k moment; 4, carrying out particle swarm splitting and
weight adjustment on the particle swarm; 5, judging whether Neff<Nth is established or not, if yes, turning to the step 6, and if not, turning to the step 8; 6,
copying and eliminating the particle swarm to obtain a new particle swarm and weight; 7, carrying out weight normalization again; 8, calculating a state
estimation value of the power system; and step 9, judging whether k >=
Omega is established or not, if not, setting k = k + 1, and turning to the step 3, and ending the dynamic
estimation of the power system if so. According to the method, the suggested
density distribution problem is improved, the estimation precision can be effectively improved, and the problemof particle shortage is effectively solved.