The inertia constant of an electric power system determines the frequency behavior immediately after a disturbance. The increasing penetration of renewable energy sources is leading to a smaller and more variable inertia, thereby compromising the frequency stability of modern grids. Therefore, a real-time estimation of the inertia would be beneficial for grid operators, as they would become more aware of the frequency stability of their grids. This paper focuses on an estimation method based on the extended Kalman filter. To be started, such method requires the knowledge of the time of disturbance, which, in turn, needs to be estimated. The purpose of this paper is to evaluate the sensitivity of the extended Kalman filter based inertia estimation method to the assumed time of disturbance.

Analysis of the Sensitivity of the Extended Kalman Filter Based Inertia Estimation Method to the Assumed Time of Disturbance

DEL GIUDICE, DAVIDE;Grillo, S.
2018-01-01

Abstract

The inertia constant of an electric power system determines the frequency behavior immediately after a disturbance. The increasing penetration of renewable energy sources is leading to a smaller and more variable inertia, thereby compromising the frequency stability of modern grids. Therefore, a real-time estimation of the inertia would be beneficial for grid operators, as they would become more aware of the frequency stability of their grids. This paper focuses on an estimation method based on the extended Kalman filter. To be started, such method requires the knowledge of the time of disturbance, which, in turn, needs to be estimated. The purpose of this paper is to evaluate the sensitivity of the extended Kalman filter based inertia estimation method to the assumed time of disturbance.
2018
Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018
978-1-5386-5186-5
electric power systems, extended Kalman filter, inertia, real-time estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1079538
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