Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

A. Mohseni | Aref Doroudi | M. Karrari

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=137795
Date :  2020/09/23
Publish in :    International Transactions on Electrical Energy Systems
DOI :  https://doi.org/10.1002/2050-7038.12619
Link :  http://dx.doi.org/10.1002/2050-7038.12619
Keywords :Parameters Estimation, Data Mining, Cubature Kalman filter

Abstract :

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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 systems 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 power measurement unit (PMU). In this paper, a novel method based on cubature Kalman filter (CKF) and with a data mining method are 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 distributed control system (DCS) and phasor measurement unit (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.