Power System State Estimation Based On PMU And Quadratic Integration Method

This paper first briefly reviews the development of the application of the Phasor Measurement Unit (PMU) and the state estimation approach on the power system and then mainly focuses on applying the PMU data and the quadratic integration method to the power system's static and dynamic state estimation algorithms.This paper first raises the approach of applying the quadratic integration methods to the construction of the power system model and also uses the order lowing technique to change the power system model to a quadratic or less order algebra model. And due to the quadratic integration has a 3 order precision, compared with the traditional model formed with Eurler method, this newly constructed power system model has the advantages of high precision and avoiding numerical oscillation.This paper also does research on several key problems of the power system state estimation, such as the PMU collected data's calibrating, the weights'setting, the observability confirmation and the bad data's detection and identification. This paper raises new algorithm for the observability and bad data detection and identification problems and the numerical experiments have show that these algorithms are effective. This paper raises three different static state estimators for the power system and the numerical experiments show that the estimator B has the best performance, and it could not only provide a high estimation accuracy but also a good ability of bad data detection and identification.This paper constructs a dynamic state estimator for the power system, and the numerical experiments results show that the dynamic state estimator has a very high estimation accuracy compared with other algorithm nowadays and it could meet the need of online moniting and the newly developed relay equipments.