Modeling of permanent magnet linear generator and state estimation based on sliding mode observer: A wave energy system application

Amal Nasri; Iskander Boulaabi; Mansour Hajji; Anis Sellami; Fayçal Ben Hmida

Kybernetika (2023)

  • Volume: 59, Issue: 5, page 655-669
  • ISSN: 0023-5954

Abstract

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This paper synopsis a new solution for Permanent Magnets Linear Generator (PMLG) state estimation subject to bounded uncertainty. Therefore, a PMLG modeling method is presented based on an equivalent circuit, wherein a mathematical model of the generator adapted to wave energy conversion is established. Then, using the Linear Matrix Inequality (LMI) optimization and a Lyapunov function, this system's Sliding Mode Observer (SMO) design method is developed. Consequently, the proposed observer can give a robust state estimation. At last, numerical examples with and without uncertainty are included to exemplify the effectiveness and applicability of the suggested approaches.

How to cite

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Nasri, Amal, et al. "Modeling of permanent magnet linear generator and state estimation based on sliding mode observer: A wave energy system application." Kybernetika 59.5 (2023): 655-669. <http://eudml.org/doc/299159>.

@article{Nasri2023,
abstract = {This paper synopsis a new solution for Permanent Magnets Linear Generator (PMLG) state estimation subject to bounded uncertainty. Therefore, a PMLG modeling method is presented based on an equivalent circuit, wherein a mathematical model of the generator adapted to wave energy conversion is established. Then, using the Linear Matrix Inequality (LMI) optimization and a Lyapunov function, this system's Sliding Mode Observer (SMO) design method is developed. Consequently, the proposed observer can give a robust state estimation. At last, numerical examples with and without uncertainty are included to exemplify the effectiveness and applicability of the suggested approaches.},
author = {Nasri, Amal, Boulaabi, Iskander, Hajji, Mansour, Sellami, Anis, Ben Hmida, Fayçal},
journal = {Kybernetika},
keywords = {wave energy; modeling; permanent magnet linear generator (PMLG); state estimation; sliding mode observer (SMO); linear matrix inequality (LMI)},
language = {eng},
number = {5},
pages = {655-669},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Modeling of permanent magnet linear generator and state estimation based on sliding mode observer: A wave energy system application},
url = {http://eudml.org/doc/299159},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Nasri, Amal
AU - Boulaabi, Iskander
AU - Hajji, Mansour
AU - Sellami, Anis
AU - Ben Hmida, Fayçal
TI - Modeling of permanent magnet linear generator and state estimation based on sliding mode observer: A wave energy system application
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 5
SP - 655
EP - 669
AB - This paper synopsis a new solution for Permanent Magnets Linear Generator (PMLG) state estimation subject to bounded uncertainty. Therefore, a PMLG modeling method is presented based on an equivalent circuit, wherein a mathematical model of the generator adapted to wave energy conversion is established. Then, using the Linear Matrix Inequality (LMI) optimization and a Lyapunov function, this system's Sliding Mode Observer (SMO) design method is developed. Consequently, the proposed observer can give a robust state estimation. At last, numerical examples with and without uncertainty are included to exemplify the effectiveness and applicability of the suggested approaches.
LA - eng
KW - wave energy; modeling; permanent magnet linear generator (PMLG); state estimation; sliding mode observer (SMO); linear matrix inequality (LMI)
UR - http://eudml.org/doc/299159
ER -

References

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  1. Ackermann, T., Wind Power in Power Systems., John Wiley and Sons, 2012. 
  2. Brooke, J., Wave Energy Conversion., Elsevier, 2003. 
  3. Calabrese, D., Tricarico, G., Brescia, E., Cascella, G. L., Monopoli, V. G., Cupertino, F., , MDPI, Energies 13 (2020), 4647. DOI
  4. Chen, W., Saif, M., , Amer. Control Confer. (2006), 5. DOI
  5. Clément, A., McCullen, P., Falcão, A., Fiorentino, A., Gardner, F., Hammarlund, K., Lemonis, G., Lewis, T., Nielsen, K., Petroncini, S., others, , Renewable Sustainable Energy Rev. 6 (2002), 405-431. DOI
  6. Falcao, A. F. de, , Elsevier 14 (2010), 899-918. DOI
  7. Farrok, O., Islam, Md R., Sheikh, Md R. I., Guo, Y., Zhu, J., Jianguo, Lei, G., , IEEE Trans. Industry Appl. 54 (2018), 6005-6014. DOI
  8. Foteinis, S., , Elsevier 162 (2022), 112448. DOI
  9. Gahinet, P., Nemirovski, A., Laub, A. J., Chilali, M., LMI Control Toolbox, the MathWorks Inc, Natick., MA 1995. 
  10. Gao, Z., Liu, X., , MDPI J., Processes 9 (2021), 300. DOI
  11. Jayalakshmi, N. S., Gaonkar, D. N., Kumar, K. S. K., , IEEE Int. Confer. Power Electron. Drives Energy Systems (PEDES) (2012), 1-5. DOI
  12. Luenberger, D., , IEEE Trans. Automat. Control 16 (1971), 596-602. DOI
  13. Mouni, E., Tnani, S., Champenois, G., , Simul- Modell. Practice Theory 16 (2008), 678-689. DOI
  14. Odgaard, P. F., Stoustrup, J., , IEEE Int. Confer. Control Appl. (2010), 310-315. DOI
  15. Polinder, H., Mueller, M. A., Scuotto, M., Prado, M. G. de Sousa, Linear generator systems for wave energy conversion., In: Proc. 7th European Wave and Tidal Energy Conference, Porto 2007, pp. 11-14. 
  16. Remon, D., Cantarellas, A. M., Rodriguez, P., , IEEE Trans. Industry Appl. 52 (2016), 5029-5040. DOI
  17. Sename, O., New trends in design of observers for time-delay systems., Kybernetika 37 (2001), 427-458. MR1859095
  18. Simões, M. G., Farret, F. A., Modeling and Analysis with Induction Generators., CRC Press 2014. 
  19. Tagliafierro, B., Martínez-Estévez, I., Domínguez, J., Crespo, A. J. C., Göteman, M., Engström, J., Gómez-Gesteira, M., , Appl. Energy, Elsevier 311 (2022), 118629. DOI
  20. Trapanese, M., Boscaino, V., Cipriani, G., Curto, D., Dio, V. Di, Franzitta, V., , IEEE Trans. Industrial Electron. 66 (2018), 4934-4944. DOI
  21. Wang, Z., Shen, Y., Zhang, X., Wang, Q., , Systems Control Lett. 61 (2012), 683-687. MR2924211DOI
  22. Wang, J., Wang, F., Wang, X., Yu, L., , Kybernetika 55 (2019), 586-603. MR4016000DOI
  23. Wang, J., Wang, F., Wang, X., Yu, L., , Kybernetika 55 (2019), 586-603. MR4016000DOI
  24. Zhang, Y., Li, G., , IEEE Trans. Sustainable Energy 11 (2019), 2201-2209. DOI
  25. Zhang, Y., Li, G., Zeng, T., , IEEE Amer. Control Confer. (ACC) (2019), 4803-4808. DOI
  26. Zhang, Y., Stansby, P., Li, G., , IEEE Trans. Sustainable Energy 12 (2020), 568-577. DOI

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