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

  • 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
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.

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References

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Published
2018-12-21
How to Cite
Cevallos Ulloa, H., 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/https://doi.org/10.29019/enfoqueute.v9n4.407
Section
Automation and Control, Mechatronics, Electromechanics, Automotive