PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The algorithm of adaptive determination of amplification of the PD filter estimating object state on the basis of signal measurable on-line

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents the algorithm that enables adaptive determination of the amplification coefficient in the filter equation provided by Kalman. The method makes use of an estimation error, which was defined for this purpose, and its derivative to determine the direction of correction changes of the gain vector. This eliminates the necessity to solve Riccati equation, which causes reduction of the method computational complexity. The experimental studies carried out using the proposed approach relate to the estimation of state coordinates describing river pollution using the BOD (biochemical oxygen demand) and DO (dissolved oxygen) indicators).The acquired results indicate that the suggested method does better estimations than the Kalman filter. Two indicators were used to measure the quality of estimates: the Root Mean Squared Error (RMSE) and the Mean Percentage Error (MPE).
Rocznik
Strony
129--143
Opis fizyczny
Bibliogr. 26 poz., rys., wykr., wzory
Twórcy
  • Institute of Technical Engineering, The State University of Technology and Economics in Jaroslaw, Czarnieckiego 16, 37-500 Jaroslaw, Poland
  • Institute of Technical Engineering, The State University of Technology and Economics in Jaroslaw, Czarnieckiego 16, 37-500 Jaroslaw, Poland
  • Faculty of Natural Sciences, University of Rzeszow, Pigonia 1, 35-959 Rzeszów, Poland
autor
  • Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Pola 2, Poland
Bibliografia
  • [1] F. Adinolfi, F. D’agostino, A. Morini, M. Saviozzi, and F. Silvestro: Pseudo-measurements modeling using neural network and fourier decomposition for distribution state estimation, IEEE PES Innovative Smart Grid Techn. Europe, (2014), 1-6, DOI: 10.1109/ISGTEurope.2014.7028770.
  • [2] S. Akhlaghi, N. Zhou, and Z. Huang: Adaptive adjustment of noise Cc-variance in Kalman filter for dynamic state estimation, IEEE Power and Energy Conference (PES) General Meeting, Chicago, IL, (2017), 1-5.
  • [3] D. Applebaum and S. Blackwood: The Kalman-Bucy filter for integrable Lévy processes with infinite second moment, Journal of Applied Probability, 52(3), (2015), 636-648.
  • [4] S. Bordignon and M. Scagliarini: Monitoring algorithms for detecting changes in the ozone concentrations environmetrics, Environmetrics, 11(2), (2000), 125-137.
  • [5] Z. Duda: Fusion Kalman filtration for distributed multisensor systems, Archives of Control Sciences, 24(1), (2014), 53-65, DOI: 10.2478/acsc2014-0004.
  • [6] H. Fang, N. Tian, Y. Wang, M. Zhou, and M. A. Haile: Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon, IEEE/CAA Journal of Automatica Sinica, 5(2), (2018), 401-417.
  • [7] Z. Gomolka, B. Twarog, E. Zeslawska, A. Lewicki, and T. Kwater: Using artificial neural networks to solve the problem represented by BOD and DO indicators, Water, 10(1), (2017).
  • [8] P. Hawro and T. Kwater: Concentration monitoring in continuous stirredtank reactor based on temperature measurement using a gain change algorithm, Elektronika, 10, (2018), DOI: 10.15199/13.2018.10.8 (in Polish).
  • [9] P. Hawro, T., Kwater, R. Pękala, and B. Twaróg: Soft sensor with adaptive algorithm for filter gain correction in the online monitoring system of a polluted river, Applied Sciences, 9(9), (2019), 1883, DOI: 10.3390/app9091883.
  • [10] P. Hawro, T. Kwater, and D. Strzęciwilk: The monitoring system based on lookup algorithm for objects described by ordinary differential equations, ITM Web Conferences, 21, (2018), 00006, DOI: 10.1051/itmconf/ 20182100006.
  • [11] K. Ito and K. Xiong: Gaussian filters for nonlinear filtering problems, IEEE Transactions on Automatic Control, 45(5), (2000), 910-927.
  • [12] S. J. Julier and J. K. Uhlmann: A new extension of the Kalman filter to nonlinear systems, Defense, Security, and Sensing (1997), DOI: 10.1117/12.280797.
  • [13] R. E. Kalman: A new approach to linear filtering and prediction problems, Transactions of the ASME – Journal of Basic Engineering, 82 (Series D), (1960), 35-45.
  • [14] P. Kozierski, M. Lis, and D. Horla: Wrong transition and measurement models in power system state estimation, Archives of Electrical Engineering, 65(3), (2016), 559-574.
  • [15] Z. Kowalewski, E. Neverova-Dziopak, and M. Preisner: An attempt to develop a regression model to estimate the BOD5 value of municipal wastewater, Ochrona Środowiska, 40(1), (2018), 21–27, (in Polish).
  • [16] P. Marantos, Y. Koveos, and K. J. Kyriakopoulos: UAV state estimation using adaptive complementary filters, IEEE Transaction Control Systems Technology, 24(4), (2016), 1214-1226.
  • [17] K. R. Mestav, J. Luengo-Rozas, and L. Tong: Bayesian state estimation for unobservable distribution systems via deep learning, IEEE Transactions on Power Systems, 34(6), (2019), 4910-4920.
  • [18] J. Michalski, P. Kozierski, and J. Zienkiewicz: Comparison of methods for estimating the state of dynamic systems [Porównanie metod estymacji stanu systemów dynamicznych], Pomiary, Automatyka, Robotyka, 4 (2017), 41-47, (in Polish).
  • [19] M. Nørgaard, N. Poulsen, and O. Ravn: Advances in Derivative-Free State Estimation for Nonlinear Systems, Technical Report IMM-REP-1998- 15, Department of Mathematical Modelling, DTU (1998).
  • [20] M. Nørgaard, N. Poulsen, and O. Ravn: New developments in state estimation for nonlinear systems, Automatica, 36(11), (2000), 1627-1638.
  • [21] F. Pan, W. Wang, A. K. H. Tung, and J. Yang: Finding representative set from massive data, Fifth IEEE International Conference on Data Mining (ICDM’05), Houston, TX, (2005), DOI: 10.1109/ICDM.2005.69.
  • [22] P. A. Pegoraro et al.: Bayesian approach for distribution system state estimation with non-Gaussian uncertainty models, IEEE Transactions on Instrumentation and Measurement, 66(11), (2017), 2957-2966.
  • [23] D. Sornette and K. Ide: The Kalman-Lévy filter, Physica D: Nonlinear Phenomena, 151(2–4), (2001), 142-174.
  • [24] X. Sun, J. Duan, X. Li, and X. Wang: State estimation under non-Gaussian Lévy noise: A modified Kalman filtering method, arXiv:1303.2395 (2013).
  • [25] G. Welch and G. Bishop: An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Chapel Hill, NC, (2006).
  • [26] G. Zhou, G. Biswas, W. Zhang, Q. Zhao, and W. Feng: Comparison of state estimation techniques for nonlinear hybrid systems, Simulation, 92(4), (2016), 357-376.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-da40de6c-824e-42e9-9127-e003ec51a450
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.