Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter. (14th September 2020) - Mohseni, Adel; Doroudi, Aref; Karrari, Mehdi
Summary: The excitation system is an important system of power plants, which has an effective role in power system dynamic and stability. Consequently, developing an approach for the excitation system's model and parameter estimation is a necessary research goal. In real situations, it is often needed to identify and estimate unknown parameters of the excitation system by field recorded signals. The recorded signal can be internal from the distributed control system (DCS) or external from the phasor measurement unit (PMU). In this article, a novel method based on cubature Kalman filter and data mining is proposed to identify the parameters of a standard type excitation system. Firstly, the best available signals for parameters estimation are chosen. Secondly, a method is proposed to parameters estimation of excitation systems efficiently, when both the DCS and PMU with different sample rates are employed to record the measurement data. Data mining is performed at intervals that DCS data is missing (or unavailable), while PMU data is available. Experimental data are used for validation of the proposed approach. Abstract : Excitation system's parameters estimation is proposed based on Cubature Kalman filter and a data mining method. The data mining is performed at intervals that distributed control system data is unavailable, while PMU data is available.