The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems

Authors

  • Holger Ignacio Cevallos Ulloa Escuela Superior Politécnica del Litoral - ESPOL
  • Gabriel Intriago Escuela Superior Politécnica del Litoral - ESPOL
  • Douglas Plaza Escuela Superior Politécnica del Litoral - ESPOL
  • Roger Idrovo Escuela Superior Politécnica del Litoral - ESPOL

DOI:

https://doi.org/10.29019/enfoqueute.v9n4.407

Keywords:

State estimation, Electric power systems, Extended Kalman filters, linear exponential smoothing of Holt, Performance indices, ; IEEE 14 bus test case, IEEE 30 bus test case

Abstract

The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.

Metrics

Downloads

Download data is not yet available.

References

Alhalali, S.M.O., and Elshatshat, R.A. (2015). “State Estimator for Electrical Distribution Systems Based on a Particle Filter”. 2015 IEEE Power & Energy Society General Meeting, p. 1-5. http://dx.doi.org/10.1109/PESGM.2015.7286398
Debs, A.S., and Larson, R.E. (1970). “A Dynamic Estimator for Tracking the State of a Power System”. IEEE Transactions on Power Apparatus and Systems, PAS-89(7), p. 1670-1678. http://dx.doi.org/10.1109/TPAS.1970.292822
Huang, Y.F., Werner, S., Huang J., Kashyap N., and Gupta V. (2012). “State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid”. IEEE Signal Processing Magazine, 29(5), p. 33-43. http://dx.doi.org/10.1109/MSP.2012.2187037
Leite da Silva, A.M., Do Coutto Filho, M.B., and De Queiroz, J.F. (1983). “State forecasting in electric power systems”. IEEE Proceedings C – Generation, Transmission and Distribution, 130(5), p. 237-244. http://dx.doi.org/10.1049/ip-c:19830046
Nguyen, H., Venayagamoorthy, G., Kling, W. and Ribeiro, P. (2013). Dynamic state estimation and prediction for real-time control and operation. Power Systems Conference (PS13). Technische Universiteit Eindhoven.
Schweppe, F., and Wildes, J. (1970). “Power System Static-State Estimation, Part I: Exact Model”. IEEE Transactions on Power Apparatus and Systems, PAS-89(1), p. 120-125. http://dx.doi.org/10.1109/TPAS.1970.292678. Recuperado de htttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4074022 (accedido el 20/02/2018).
Sharma, A., Srivastava, S.C., and Chakrabarti, S. (2017). “A Cubature Kalman Filter Based Power System Dynamic State Estimator”. IEEE Transactions on Instrumentation and Measurement, 66(8), p. 2036-2045. http://dx.doi.org/10.1109/TIM.2017.2677698
University of Washington. (1993). “IEEE 14 bus test case”. Recuperado de http://www2.ee.washington.edu/research/pstca/pf14/ieee14cdf.txt (accedido el 20/03/2018).
Valverde, G., and Terzija, V. (2011). “Unscented kalman filter for power system dynamic state estimation”. Generation, Transmission & Distribution IET, 5(1), p. 29-37. http://dx.doi.org/10.1049/iet-gtd.2010.0210.
Zanni. L., Le Boudec, J.Y., Cherkaoui, R., and Paolone, M. (2017). “A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation”. IEEE Transactions on Control Systems Technology, 25(5). http://dx.doi.org/10.1109/TCST.2016.2628716.

Published

2018-12-21

How to Cite

Cevallos Ulloa, H. I., Intriago, G., Plaza, D., & Idrovo, R. (2018). The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems. Enfoque UTE, 9(4), pp. 120 - 130. https://doi.org/10.29019/enfoqueute.v9n4.407

Issue

Vol. 9 No. 4 (2018)

Section

Automation and Control, Mechatronics, Electromechanics, Automotive

The articles and research published by the UTE University are carried out under the Open Access regime in electronic format. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.

The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.

 

This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).

 

In addition, the journal Enfoque UTE guarantees and declares that authors always retain all copyrights to the original published works without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.