The new approach to hybrid Kalman filtering, based on the changed order of filters for state estimation of dynamical systems

• Autorzy: Michalski Jacek , Kozierski Piotr , Ziętkiewicz Joanna

Identyfikatory

DOI

10.21008/j.1897-0737.2019.97.0016

• Konferencja: Computer Applications in Electrical Engineering (15-16.04.2019 ; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

The paper presents a new approach to Hybrid Kalman filtering, composed of Extended Kalman Filter and Unscented Kalman Filter. In known algorithms, the Unscented Kalman Filter algorithm is used as first and the result of this is given as an input to the Extended Kalman Filter. The authors checked modified Hybrid Kalman Filter with changed order of filters using theoretical object, which was created on the basis of power system. Besides traditional method, the modification of Hybrid Kalman Particle Filter was evaluated too. Results were compared with Extended Kalman Filter, Unscented Kalman Filter and Bootstrap Particle Filter. For particle filters the authors compared method estimation qualities for a different number of particles. The estimation quality was evaluated by three quality indices. Based on the obtained results, one can see that the changed order of methods in Hybrid Kalman filter can improve estimation quality.

Słowa kluczowe

EN

state estimation; Kalman filtering; particle filtering; power system; quality indices

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Poznan University of Technology Academic Journals. Electrical Engineering
• Rocznik: 2019
• Tom: No. 97
• Strony: 181--190
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Michalski Jacek

• Poznan University of Technology

autor

Kozierski Piotr

• Poznan University of Technology

autor

Ziętkiewicz Joanna

• Poznan University of Technology

Bibliografia

• [1] Chang C., Ansari R., Khokhar A., Multiple Object Tracking with Kernel Particle Filter, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2005, Vol. 1, pp. 566–573.
• [2] Zhong S., Hao F., Hand tracking by particle filtering with elite particles mean shift, Proceedings on Japan-China Joint Workshop on Frontier of Computer Science and Technology, December 2008, pp. 163–167.
• [3] Yang B., Pan X., Men A., Chen X., A Robust Particle Filter for People Tracking, In Future Networks, Second International Conference on, ICFN'10, January 2010, pp. 20–23. DOI: 10.1109/ICFN.2010.34
• [4] Šmídl V., Hofman R., Adaptive Importance Sampling in Particle Filtering, In Information Fusion, 16th International Conference on (FUSION), July 2013, pp. 9–16.
• [5] Korres G.N., A Distributed Multiarea State Estimation, Power Systems, IEEE Transactions on, Vol. 26, No. 1, 2011, pp. 73–84.
• [6] Guo Y., Wu W., Zhang B., Sun H., A Distributed State Estimation Method for Power Systems incorporating Linear and Nonlinear Models, International Journal of Electrical Power & Energy Systems, Vol. 64, 2015, pp. 608–616.
• [7] Chen H., Liu X., She C., Yao C., Power System Dynamic State Estimation Based on a New Particle Filter, Procedia Environmental Sciences, Vol. 11, Part B, 2011, pp. 655–661. DOI: 10.1016/j.proenv.2011.12.102
• [8] Valverde G., Terzija V., Unscented Kalman Filter for Power System Dynamic State Estimation, IET Generation, Transmission & Distribution, Vol. 5, Iss. 1, 2011, pp. 29–37.
• [9] Shih K.R., Huang S.J., Application of a Robust Algorithm for Dynamic State Estimation of a Power System, Power Systems, IEEE Transactions on, Vol. 17, No. 1, 2002, pp. 141–147. DOI: 10.1109/59.982205
• [10] Salehfar H., Zhao R., A neural network preestimation filter for bad-data detection and identification in power system state estimation, Electric Power Systems Research, Vol. 34, No. 2, 1995, pp. 127–134.
• [11] Kalman R.E., A new approach to linear filtering and prediction problems, Journal of basic Engineering,1960, Vol. 82, No. 1, pp. 35–45.
• [12] Michalski J., Kozierski P., Ziętkiewicz J., Comparison of State Estimation Methods of Dynamical Systems, Pomiary Automatyka Robotyka, 2017, Vol. 21, No. 4, pp. 41–48 (in Polish).
• [13] Gordon N.J., Salmond D.J., Smith A.F., Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F-radar and signal processing, 1993, Vol. 140, No. 2, pp. 107–113.
• [14] Arulampalam M.S., Maskell S., Gordon N., Clapp T., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on signal processing, 2002, Vol. 50, No. 2, pp. 174–188.
• [15] Kozierski P., Lis M., Ziętkiewicz J., Resampling in particle filtering-comparison, Studia z Automatyki i Informatyki, 2013, Vol. 38, pp. 35–64.
• [16] Michalski J., Kozierski P., Ziętkiewicz J., Comparison of Auxiliary and Likelihood Particle Filters for State Estimation of Dynamical Systems, Przegląd Elektrotechniczny, 2018, Vol. 94, pp. 86–90.
• [17] Doucet A., Johansen A.M., A tutorial on particle filtering and smoothing: Fifteen years later, Handbook of nonlinear filtering, 2009, Vol. 12, No. 3, pp. 656–704.
• [18] Wang F., Lin Y., Zhang T., Liu J., Particle Filter with Hybrid Proposal Distribution for Nonlinear State Estimation, JCP, 2011, Vol. 6, No. 11, pp. 2491–2501.
• [19] Michalski J., Kozierski P., Zietkiewicz J., Giernacki W., Likelihood Particle Filter and Its Proposed Modifications, Studia z Automatyki i Informatyki, 2018, Vol. 43, pp. 81–93.
• [20] Kozierski P., Michalski J., Sadalla T., Giernacki W., Zietkiewicz J., Drgas S., New Grid for Particle Filtering of Multivariable Nonlinear Objects, In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE , 2018, pp. 1073–1077.
• [21] Florek A., Mazurkiewicz P., Dynamic Signals and Systems (in Polish), 2nd ED., Poznań 2015. ISBN: 978-83-7775360-6.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).