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Wpływ kolejności dyskretyzacji na rozszerzoną estymację filtra Kalmana dla generatora indukcyjnego z podwójnym zasilaniem
Języki publikacji
Abstrakty
The main objective of this paper is to analyze the influence of the discretization step on the estimated states of the Doubly-Fed Induction Generator (DFIG). Although the Extended Kalman Filter (EKF) has been widely used for such systems, the discretization process is conventionally ensured by the first-order Forward Euler method. Therefore, the effects of the discretization order of the discrete state-space representation on the Extended Kalman Filter estimation have not been studied before. In this paper, we combine the Extended Kalman Filter with two second-order discretization methods: Central Difference and Adams-Bashforth methods, to estimate the states of a Doubly-Fed Induction Generator and improve the estimation precision of the rotor speed and the Flux of the generator. A comparative study has been conducted to analyze the qualitative and quantitative responses of the estimator for different cases. The obtained results have demonstrated the significance of the discretization order on the estimation process of the two states of the DFIG.
Głównym celem tej pracy jest analiza wpływu kroku dyskretyzacji na oszacowane stany Dwubiegowego Generatora Indukcyjnego (DFIG). Chociaż Rozszerzony Filtr Kalmana (EKF) jest szeroko stosowany w tego typu systemach, proces dyskretyzacji jest zazwyczaj zapewniany przez metodę pierwszego rzędu Forward Euler. Dlatego też wpływ rzędu dyskretyzacji na oszacowanie za pomocą Rozszerzonego Filtru Kalmana nie był wcześniej badany. W niniejszej pracy łączymy Rozszerzony Filtr Kalmana z dwiema metodami dyskretyzacji drugiego rzędu: różnicą centralną i metodą Adamsa-Bashfortha, aby oszacować stany Dwubiegowego Generatora Indukcyjnego oraz poprawić precyzję oszacowania prędkości wirnika i strumienia generatora. Przeprowadzono badanie porównawcze w celu analizy odpowiedzi jakościowych i ilościowych estymatora dla różnych przypadków. Uzyskane wyniki wykazały znaczenie rzędu dyskretyzacji w procesie oszacowania dwóch stanów DFIG.
Wydawca
Czasopismo
Rocznik
Tom
Strony
102--108
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
- Applied Automation Laboratory, Faculty of Hydrocarbons, University of M’hamed Bougara of Boumerdes, 35000, Algeria
autor
- Applied Automation Laboratory, Faculty of Hydrocarbons, University of M’hamed Bougara of Boumerdes, 35000, Algeria
autor
- Applied Automation Laboratory, Faculty of Hydrocarbons, University of M’hamed Bougara of Boumerdes, 35000, Algeria
- Applied Automation Laboratory, Faculty of Hydrocarbons, University of M’hamed Bougara of Boumerdes, 35000, Algeria
- Exploration Division, Sonatrach
Bibliografia
- [1] P. Hippe, “Regular design equations for the discrete reduced-order kalman filter,” Archives of Control Sciences 22(2):175– 189, 2012. DOI: 10.2478/v10170-011-0019-x
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- [3] R. Riane, M. Kidouche, R. Illoul, M. Z. Doghmane, “Unknown resistive torque estimation of a rotary drilling system based on kalman filter,” IETE Journal of Research pp. 1–12, 2020. https://doi.org/10.1080/03772063.2020.1724834
- [4] Laamari, Yahia, et al. "Highly nonlinear systems estimation using extended and unscented kalman filters." PRZEGLĄD ELEKTROTECHNICZN journal, pp.111-115, 2021. doi:10.15199/48.2021.05.20
- [5] J. Havlík, O. Straka, “Performance evaluation of iterated extended kalman filter with variable step-length,” In Journal of Physics: Conference Series, vol. 659, p. 012022. IOP Publishing, 2015. DOI: 10.1088/1742-6596/659/1/012022
- [6] M. Z. Doghmane, M. Kidouche, “Decentralized controller robustness improvement using longitudinal overlapping Decomposition-Application to web winding system,” Elektronika ir Elektronika, vol. 24, pp. 10-18, 2018. DOI: https://doi.org/10.5755/j01.eie.24.5.21837
- [7] B. D. Anderson, J. B. Moore, “Optimal filtering,” Prentice-Hall Information and System Science Series, Prentice-Hall, INC., Englewood Cliffs, New Jersey 07632, 2012.
- [8] M. Moujahid, H. Ben Azza, M. Jemli, M. Boussak, “Speed Estimation by Using EKF Techniques for Sensor-Less DTC of PMSM with Load Torque Observer,” International Review of Electrical Engineering 9(2), 35-43 (2014).
- [9] H. Dai, L. Zou, et al., “Two second-order nonlinear extended kalman particle filter algorithms,” Open Journal of Statistics 5(04):254, 2015. DOI: 10.4236/ojs.2015.54027
- [10] S. Udomsuk, K. Areerak, T. Areerak, Kongpan Arrerak, “Speed Estimation of Three-Phase Induction Motor Using Kalman Filter,” International Review of Electrical Engineering 16(6), 15-27. DOI: https://doi.org/10.15866/iree.v13i4.13451
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- [12] M. Kidouche, et al., “Combining second order central difference discretization with extended kalman filter for rotor speed and flux estimation of a doubly-fed induction generator,” In 2018 International Conference on Communications and Electrical Engineering (ICCEE), pp. 1–6. IEEE, 2018. DOI: 10.1109/CCEE.2018.8634518
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- [14] I. Arasaratnam, S. Haykin, “Cubature kalman filters,” IEEE Transactions on automatic control 54(6):1254–1269, 2009. DOI: 10.1109/TAC.2009.2019800
- [15] S. A. S. Ari Aluthge, R. Estep, “Filtered leapfrog time integration with enhanced stability properties,” Journal of Applied Mathematics and Physics 4:1354–1370, 2016. DOI: 10.4236/jamp.2016.47145
- [16] S. Muller, M. Deicke, R. W. De Doncker. Doubly fed induction generator systems for wind turbines. IEEE Industry applications magazine 8(3):26–33, 2002. DOI: 10.1109/2943.999610
- [17] G. Abad, J. Lopez, M. Rodriguez, et al., “Doubly fed induction machine: modeling and control for wind energy generation,” vol. 85. John Wiley & Sons, 2011.
- [18] Akroum, H., Kidouche, M., Grouni, S., & Zelmat, M. (2010). A Perfectly Symmetrical Configuration in Dual-Bridge Inverter Topology for Maximum Mitigation of EMI, Common-Mode Voltages and Common-Mode Currents. Elektronika Ir Elektrotechnika, 103(7), 51-56. https://eejournal.ktu.lt/index.php/elt/article/view/9275
- [19] J. Slootweg, H. Polinder, W. L. Kling, “Dynamic modelling of a wind turbine with doubly fed induction generator,” In 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No. 01CH37262), vol. 1, pp. 644–649. IEEE, 2001. DOI: 10.1109/PESS.2001.970114
- [20] A. Petersson, “Analysis, modeling and control of doubly-fed induction generators for wind turbines,” PhD thesis, Chalmers University of Technology, 2005.
- [21] D. Simon, “Optimal state estimation: Kalman, H infinity, and nonlinear approaches,” John Wiley & Sons, 2006.
- [22] Aibech, A., Akroum, H., Boudouda, A., Kidouche, M., & Doghmane, M. Z. (2021). Real-Time Reduction of Rotor Position Estimation Error Based on the Stator Flux Estimation-Combined Method for Sensorless Control of PMSMs Drives. International Review of Electrical Engineering 16(6), 15-27. DOI: https://doi.org/10.15866/iree.v16i6.20801
- [23] M. Abdelrahem, C. Hackl, R. Kennel, “Application of extended kalman filter to parameter estimation of doubly fed induction generators in variable-speed wind turbine systems,” In 2015 International Conference on Clean Electrical Power (ICCEP), pp. 226–233. IEEE, 2015. DOI: 10.1109/ICCEP.2015.7177628
- [24] I. R. Pérez, J. C. Silva, E. J. Yuz, R. G. Carrasco, “Experimental sensorless vector control performance of a dfig based on an extended kalman filter,” In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 1786–1792. IEEE, 2012. DOI: 10.1109/IECON.2012.6388930
- [25] M. K. Malakar, P. Tripathy, S. Krishnaswamy, “State estimation of dfig using an extended kalman filter with an augmented state model,” In Power Systems Conference (NPSC), 2014 Eighteenth National, pp. 1–6. IEEE, 2014. DOI: 10.1109/NPSC.2014.7103891
- [26] S. Yu, T. Fernando, H. H.-C. Iu, K. Emami, “Realization of state-estimation-based dfig wind turbine control design in hybrid power systems using stochastic filtering approaches,” IEEE Transactions on Industrial Informatics 12(3):1084–1092, 2016. DOI: 10.1109/TII.2016.2549940
- [27] A. Boussoufa, M. Kidouche, A. Ahriche, “Rotor speed and flux estimation of a doubly-fed induction machine using extended kalman filter,” Algerian Journal of Signals and Systems 2(4):266–273, 2017. DOI: https://doi.org/10.51485/ajss.v2i4.52
- [28] D. J. Herzfeld, P. A. Vaswani, M. K. Marko, R. Shadmehr, “A memory of errors in sensorimotor learning,” Science 345(6202):1349–1353, 2014. DOI: 10.1126/science.1253138
- [29] Mendil C., Kidouche M., Doghmane M.Z. (2021) A Study of the Parametric Variations Influences on Stick-Slip Vibrations in Smart Rotary Drilling Systems. In: Hatti M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030- 63846-7_67
- [30] Mendil C., Kidouche M., Doghmane M.Z. (2021) Modeling of Hydrocarbons Rotary Drilling Systems Under Torsional Vibrations: A Survey. In: Hatti M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846- 7_24
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9bf513c7-400b-4167-bc28-ba90037c1a58
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