Power System Dynamic State Estimation Using Smooth Variable Structure Filter

With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictor-corrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.

[1]  Venkata Dinavahi,et al.  On false data injection attack against dynamic state estimation on smart power grids , 2017, 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[2]  Hamed Hossein Afshari,et al.  A Review of Smooth Variable Structure Filters: Recent Advances in Theory and Applications , 2015 .

[3]  Venkata Dinavahi,et al.  Accelerated parallel WLS state estimation for large-scale power systems on GPU , 2013, 2013 North American Power Symposium (NAPS).

[4]  J. L. Aravena,et al.  Power system fault detection and state estimation using Kalman filter with hypothesis testing , 1991 .

[5]  Jinho Kim,et al.  A Variable Structure-Based Estimation Strategy Applied to an RRR Robot System , 2017, J. Robotics Netw. Artif. Life.

[6]  Claus Leth Bak,et al.  A performance comparison between extended Kalman Filter and unscented Kalman Filter in power system dynamic state estimation , 2016, 2016 51st International Universities Power Engineering Conference (UPEC).

[7]  Davide Della Giustina,et al.  Electrical distribution system state estimation: measurement issues and challenges , 2014, IEEE Instrumentation & Measurement Magazine.

[8]  Innocent Kamwa,et al.  Online State Estimation of a Synchronous Generator Using Unscented Kalman Filter From Phasor Measurements Units , 2011, IEEE Transactions on Energy Conversion.

[9]  Hadis Karimipour,et al.  Microgrid Islanding Detection Based on Mathematical Morphology , 2018, Energies.

[10]  Ashraf Saleem,et al.  Smooth Variable Structure Filter for pneumatic system identification , 2011, 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[11]  Hadis Karimipour,et al.  Parallel relaxation-based joint dynamic state estimation of large-scale power systems , 2016 .

[12]  Venkata Dinavahi,et al.  Extended Kalman Filter-Based Parallel Dynamic State Estimation , 2016, IEEE Transactions on Smart Grid.

[13]  Junbo Zhao,et al.  Dynamic State Estimation With Model Uncertainties Using $H_\infty$ Extended Kalman Filter , 2018, IEEE Transactions on Power Systems.

[14]  Saeid Habibi,et al.  The Smooth Variable Structure Filter , 2007, Proceedings of the IEEE.

[15]  I. Kamwa,et al.  Dynamic State Estimation in Power System by Applying the Extended Kalman Filter With Unknown Inputs to Phasor Measurements , 2011, IEEE Transactions on Power Systems.

[16]  Suresh Vadhva,et al.  State Estimation of a Distribution System Using WLS and EKF Techniques , 2015, 2015 IEEE International Conference on Information Reuse and Integration.

[17]  S. Andrew Gadsden,et al.  A nonlinear second-order filtering strategy for state estimation of uncertain systems , 2019, Signal Process..

[18]  Ravindra Singh.  State estimation in power distribution network operation , 2009 .

[19]  Hadis Karimipour,et al.  Robust Massively Parallel Dynamic State Estimation of Power Systems Against Cyber-Attack , 2018, IEEE Access.

[20]  Thia Kirubarajan,et al.  Kalman and smooth variable structure filters for robust estimation , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Benjamin Jeyasurya,et al.  Dynamic state estimation in power systems using Kalman filters , 2013, 2013 IEEE Electrical Power & Energy Conference.

[22]  Stephen Andrew Gadsden,et al.  SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS , 2011 .

[23]  Milan Prodanovic,et al.  State Estimation Techniques for Electric Power Distribution Systems , 2014, 2014 European Modelling Symposium.

[24]  M. Al-Shabi.  The General Toeplitz/Observability Smooth Variable Structure Filter , 2011 .