The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems
DOI:
https://doi.org/10.29019/enfoqueute.v9n4.407Keywords:
State estimation, Electric power systems, Extended Kalman filters, linear exponential smoothing of Holt, Performance indices, ; IEEE 14 bus test case, IEEE 30 bus test caseAbstract
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.
Downloads
References
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
Issue
Section
License
The authors retain all copyrights ©.
- The authors retain their trademark and patent rights, as well as rights to any process or procedure described in the article.
- The authors retain the right to share, copy, distribute, perform, and publicly communicate the article published in Enfoque UTE (for example, post it in an institutional repository or publish it in a book), provided that acknowledgment of its initial publication in Enfoque UTE is given.
- The authors retain the right to publish their work at a later date, to use the article or any part of it (for example, a compilation of their work, lecture notes, a thesis, or for a book), provided that they indicate the source of publication (authors of the work, journal, volume, issue, and date).