Wrong transition and measurement models in power system state estimation

• Autorzy: Kozierski P. , Lis M. , Horla D.

Identyfikatory

DOI

10.1515/aee-2016-0040

• Języki publikacji: EN

Abstrakty

EN

The influence of wrong information about transition and measurement models on estimation quality has been presented in the paper. Two methods of a particle filter, with and without the Population Monte Carlo modification, and also the extended and unscented Kalman filters methods have been compared. A small 5-bus power system has been used in simulations, which have been performed based on one data set, and this data set has been chosen from among 100 different – to draw the most general conclusions. Based on the obtained results it has been found that for the particle filter methods the implementation of the slightly higher standard deviation than the true value, usually increases the estimation quality. For the Kalman filters methods it has been concluded that optimal values of variances are equal to the true values.

Słowa kluczowe

EN

particle filter; estimation quality; Population Monte Carlo

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2016
• Tom: Vol. 65, nr 3
• Strony: 559--574
• Opis fizyczny: Bibliogr. 21 poz., fig., tab., wz.

Twórcy

autor

Kozierski P.

• Faculty of Electrical Engineering, Institute of Control and Information Engineering,Poznan University of Technology Piotrowo 3a, 60-965 Poznań, Poland
• Faculty of Computing, Chair of Control and Systems Engineering Division of Signal Processing and Electronic Systems, Poznan University of Technology Piotrowo 3a, 60-965 Poznań, Poland

autor

Lis M.

• Faculty of Electrical Engineering, Institute of Electrical Engineering and Electronics, Poznan University of Technology Piotrowo 3a, 60-965 Poznań, Poland
• 4Spirvent sp. z o.o. Polna 2C, 64-600 Oborniki, Poland

autor

Horla D.

• Faculty of Electrical Engineering, Institute of Control and Information Engineering, Poznan University of Technology Piotrowo 3a, 60-965 Poznań, Poland

Bibliografia

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• [8] Šmídl V., Hofman R., Adaptive Importance Sampling in Particle Filtering, Proc. 16th Int. Conf. on Information Fusion (FUSION), pp. 9-16 (July 2013).
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• [10] Kozierski P., Lis M., Ziętkiewicz J., Resampling in Particle Filtering – Comparison, Studia z Automatyki i Informatyki 38: 35-64 (2013).
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• [12] Arulampalam S., Maskell S., Gordon N., Clapp T., A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Proceedings on Signal Processing 50(2): 174-188 (2002).
• [13] Simon D., Optimal State Estimation, WILEY-INTERSCIENCE, New Jersey (2006).
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• [17] Kremens Z., Sobierajski M., The Analysis of Power Systems, Wydawnictwa Naukowo-Techniczne, Warsaw, pp. 39-191 (1996). (in Polish)
• [18] Abur A., Exposito A. G., Power System State Estimation: Theory and Implementation, Marcel Dekker, Inc., pp. 17-49 (2004).
• [19] Andersson G., Modelling and Analysis of Electric Power Systems, EEH-Power Systems Laboratory, Swiss Federal Institute of Technology (ETH), Zürich (2008).
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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.