A New Optimized Method of Excitation Signal for Closed-Loop Identification of Power System Based on Ambient Signals

Abstract

Excitation signal optimization is an important part in the power system identification based on ambient signals. An adaptive discrete Kalman filter method is proposed to select an optimal signal for closed-loop identification in this paper. This method is carried out through the use of the measurement innovation sequence as piecewise stationary process inside an estimation window. It also overcomes the shortcomings of relying on the correctness of the mathematical and statistical models excessively. The feature of random load changing in power system is fully considered in this method. Then under the energy constraints of input and output signals, this method can be used to solve the excitation signal which satisfies the performance of power system and the noise covariance estimation matrices are acquired. By using this method, the optimal identification model can be obtained. Simulation results show the effective performance of the proposed method. Compared with other methods, the quality of the closed-loop identification model based on ambient signals is improved by using the excitation signal optimal method proposed in this paper.