As a kind of measuring method

wide area measurement system (WAMS) inevitably has measuring errors. In order to obtain a better control strategy and more accurate analysis results

it is necessary to do filtering processing for the actual measured data before application. A new method of dynamic filtering estimation for the actual measured data is proposed

and a dynamic state estimating model of a generator is established based on the second-order dynamic equation. Considering the nonlinearity of the model

the particle filtering (PF) with the unique advantage of dealing with non-linear and non-Gaussian stochastic system estimation problems is also used. To solve the problems of computation taking up space

large amounts of calculation and sample degradation

sequential importance resampling (SIR) is introduced on the basis of PF algorithm. Meanwhile

extended Kalman filter (EKF) is also adopted to do the state estimation

with the results compared with those using the approach proposed. Besides

to measure the estimation effect quantitatively

an evaluation index system based on the estimate path similarity is set. Finally

through the simulation calculation of a CEPRI 7-bus system

it is shown that the estimation based on the PF has a higher correlation with the actual result and a smaller error compared with the root mean square of the real value

a result better than the estimation based on the EKF and effectively reducing the influence of erroneous data. This work is supported by National Natural Science Foundation of China (No. 51177010

No. 51377017).