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

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

Kybernetika, Tome 59 (2023) no. 5, pp. 655-669

Abstract (VO)

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.

Détail

DOI : 10.14736/kyb-2023-5-0655

Classification : 49M30, 93B07
Keywords: wave energy; modeling; permanent magnet linear generator (PMLG); state estimation; sliding mode observer (SMO); linear matrix inequality (LMI)

Bibliographie

[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.: Variable structure control of a small ducted wind turbine in the whole wind speed range using a Luenberger observer. MDPI, Energies 13 (2020), 4647. | DOI

[4] Chen, W., Saif, M.: Unknown input observer design for a class of nonlinear systems: an LMI approach. 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: Wave energy in Europe: current status and perspectives. Renewable Sustainable Energy Rev. 6 (2002), 405-431. | DOI

[6] Falcao, A. F. de: Wave energy utilization: A review of the technologies. Elsevier 14 (2010), 899-918. | DOI

[7] Farrok, O., Islam, Md R., Sheikh, Md R. I., Guo, Y., Zhu, J., Jianguo, Lei, G.: Oceanic wave energy conversion by a novel permanent magnet linear generator capable of preventing demagnetization. IEEE Trans. Industry Appl. 54 (2018), 6005-6014. | DOI

[8] Foteinis, S.: Wave energy converters in low energy seas: Current state and opportunities. 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.: An overview on fault diagnosis, prognosis and resilient control for wind turbine systems. MDPI J., Processes 9 (2021), 300. | DOI

[11] Jayalakshmi, N. S., Gaonkar, D. N., Kumar, K. S. K.: Dynamic modeling and performance analysis of grid connected PMSG based variable speed wind turbines with simple power conditioning system. IEEE Int. Confer. Power Electron. Drives Energy Systems (PEDES) (2012), 1-5. | DOI

[12] Luenberger, D.: An introduction to observers. IEEE Trans. Automat. Control 16 (1971), 596-602. | DOI

[13] Mouni, E., Tnani, S., Champenois, G.: Synchronous generator modelling and parameters estimation using least squares method. Simul- Modell. Practice Theory 16 (2008), 678-689. | DOI

[14] Odgaard, P. F., Stoustrup, J.: Unknown input observer based detection of sensor faults in a wind turbine. 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.: Equivalent model of large-scale synchronous photovoltaic power plants. 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. | MR

[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.: A numerical study of a taut-moored point-absorber wave energy converter with a linear power take-off system under extreme wave conditions. Appl. Energy, Elsevier 311 (2022), 118629. | DOI

[20] Trapanese, M., Boscaino, V., Cipriani, G., Curto, D., Dio, V. Di, Franzitta, V.: A permanent magnet linear generator for the enhancement of the reliability of a wave energy conversion system. IEEE Trans. Industrial Electron. 66 (2018), 4934-4944. | DOI

[21] Wang, Z., Shen, Y., Zhang, X., Wang, Q.: Observer design for discrete-time descriptor systems: An LMI approach. Systems Control Lett. 61 (2012), 683-687. | DOI | MR

[22] Wang, J., Wang, F., Wang, X., Yu, L.: Disturbance observer based integral terminal sliding mode control for permanent magnet synchronous motor system. Kybernetika 55 (2019), 586-603. | DOI | MR

[23] Wang, J., Wang, F., Wang, X., Yu, L.: Disturbance observer based integral terminal sliding mode control for permanent magnet synchronous motor system. Kybernetika 55 (2019), 586-603. | DOI | MR

[24] Zhang, Y., Li, G.: Non-causal linear optimal control of wave energy converters with enhanced robustness by sliding mode control. IEEE Trans. Sustainable Energy 11 (2019), 2201-2209. | DOI

[25] Zhang, Y., Li, G., Zeng, T.: Wave excitation force estimation for wave energy converters using adaptive sliding mode observer. IEEE Amer. Control Confer. (ACC) (2019), 4803-4808. | DOI

[26] Zhang, Y., Stansby, P., Li, G.: Non-causal linear optimal control with adaptive sliding mode observer for multi-body wave energy converters. IEEE Trans. Sustainable Energy 12 (2020), 568-577. | DOI