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

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.

[1]  Zhenyu Huang,et al.  Generator dynamic model validation and parameter calibration using phasor measurements at the point of connection , 2014, IEEE Transactions on Power Systems.

[2]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[3]  Lingling Fan,et al.  Identification of synchronous generator model with frequency control using unscented Kalman filter , 2015 .

[4]  Abhinav Singh,et al.  Estimating dynamic model parameters for adaptive protection and control in power system , 2015, 2015 IEEE Power & Energy Society General Meeting.

[5]  Zhenyu Huang,et al.  Application of extended Kalman filter techniques for dynamic model parameter calibration , 2009, 2009 IEEE Power & Energy Society General Meeting.

[6]  Zhenyu Huang,et al.  Calibrating multi-machine power system parameters with the extended Kalman filter , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  P. Wall,et al.  Inertia estimation using PMUs in a laboratory , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[8]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[9]  Stefano Barsali,et al.  Benchmark systems for network integration of renewable and distributed energy resources , 2014 .

[10]  I. Rhodes A tutorial introduction to estimation and filtering , 1971 .

[11]  T. Inoue,et al.  Estimation of power system inertia constant and capacity of spinning-reserve support generators using measured frequency transients , 1997 .

[12]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[13]  Pieter Tielens,et al.  The relevance of inertia in power systems , 2016 .

[14]  Lingling Fan,et al.  Extended Kalman filtering based real-time dynamic state and parameter estimation using PMU data , 2013 .

[15]  F. Gonzalez-Longatt,et al.  Estimation of generator inertia available during a disturbance , 2012, 2012 IEEE Power and Energy Society General Meeting.

[16]  Timothy M. Hansen,et al.  Virtual Inertia: Current Trends and Future Directions , 2017 .

[17]  Vladimir Terzija,et al.  Simultaneous Estimation of the Time of Disturbance and Inertia in Power Systems , 2014, IEEE Transactions on Power Delivery.

[18]  Greg Welch,et al.  Dynamic State Estimation of a Synchronous Machine Using PMU Data: A Comparative Study , 2015, IEEE Transactions on Smart Grid.