Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators
DOI:
https://doi.org/10.20998/2074-272X.2022.3.08Keywords:
small-scale wind generator, maximum power point tracking, fuzzy system, fuzzy model based multivariable predictive control, linear matrix inequalities approachAbstract
Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.
References
Babes B., Rahmani L., Chaoui A., Hamouda N. Design and Experimental Validation of a Digital Predictive Controller for Variable-Speed Wind Turbine Systems. Journal of Power Electronics, 2017, vol. 17, no. 1, pp. 232-241. doi: https://doi.org/10.6113/JPE.2017.17.1.232.
Amrane F., Chaiba A., Francois B., Babes B. Experimental design of stand-alone field oriented control for WECS in variable speed DFIG-based on hysteresis current controller. 2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA), 2017, pp. 304-308. doi: https://doi.org/10.1109/ELMA.2017.7955453.
Lee J., Kim Y. Sensorless fuzzy‐logic‐based maximum power point tracking control for a small‐scale wind power generation systems with a switched‐mode rectifier. IET Renewable Power Generation, 2016, vol. 10, no. 2, pp. 194-202. doi: https://doi.org/10.1049/iet-rpg.2015.0250.
Hamouda N., Babes B., Kahla S., Soufi Y. Real time implementation of grid connected wind energy systems: predictive current controller. 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), 2019. pp. 1-6. doi: https://doi.org/10.1109/ICSRESA49121.2019.9182526.
Kesraoui M., Korichi N., Belkadi A. Maximum power point tracker of wind energy conversion system. Renewable Energy, 2011, vol. 36, no. 10, pp. 2655-2662. doi: https://doi.org/10.1016/j.renene.2010.04.028.
Zhu Y., Cheng M., Hua W., Wang W. A novel maximum power point tracking control for permanent magnet direct drive wind energy conversion systems. Energies, 2012, vol. 5, no. 5, pp. 1398-1412. doi: https://doi.org/10.3390/en5051398.
Kazmi S.M.R., Goto H., Guo H., Ichinokura O. A Novel Algorithm for Fast and Efficient Speed-Sensorless Maximum Power Point Tracking in Wind Energy Conversion Systems. IEEE Transactions on Industrial Electronics, 2011, vol. 58, no. 1, pp. 29-36. doi: https://doi.org/10.1109/TIE.2010.2044732.
Xia Y., Ahmed K.H., Williams B.W. A New Maximum Power Point Tracking Technique for Permanent Magnet Synchronous Generator Based Wind Energy Conversion System. IEEE Transactions on Power Electronics, 2011, vol. 26, no. 12, pp. 3609-3620. doi: https://doi.org/10.1109/TPEL.2011.2162251.
Ching-Tsai Pan, Yu-Ling Juan. A Novel Sensorless MPPT Controller for a High-Efficiency Microscale Wind Power Generation System. IEEE Transactions on Energy Conversion, 2010, vol. 25, no. 1, pp. 207-216. doi: https://doi.org/10.1109/TEC.2009.2032604.
Agarwal V., Aggarwal R.K., Patidar P., Patki C. A Novel Scheme for Rapid Tracking of Maximum Power Point in Wind Energy Generation Systems. IEEE Transactions on Energy Conversion, 2010, vol. 25, no. 1, pp. 228-236. doi: https://doi.org/10.1109/TEC.2009.2032613.
Lin W.-M., Hong C.-M. Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system. Energy, 2010, vol. 35, no. 6, pp. 2440-2447. doi: https://doi.org/10.1016/j.energy.2010.02.033.
Kazmi S.M.R., Goto H., Guo H.-J., Ichinokura O. A Novel Algorithm for Fast and Efficient Speed-Sensorless Maximum Power Point Tracking in Wind Energy Conversion Systems. IEEE Transactions on Industrial Electronics, 2011, vol. 58, no. 1, pp. 29-36. doi: https://doi.org/10.1109/TIE.2010.2044732.
Galdi V., Piccolo A., Siano P. Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction. IEEE Transactions on Energy Conversion, 2008, vol. 23, no. 2, pp. 559-569. doi: https://doi.org/10.1109/TEC.2007.914164.
Pucci M., Cirrincione M. Neural MPPT Control of Wind Generators With Induction Machines Without Speed Sensors. IEEE Transactions on Industrial Electronics, 2011, vol. 58, no. 1, pp. 37-47. doi: https://doi.org/10.1109/TIE.2010.2043043.
Cardenas R., Pena R. Sensorless Vector Control of Induction Machines for Variable-Speed Wind Energy Applications. IEEE Transactions on Energy Conversion, 2004, vol. 19, no. 1, pp. 196-205. doi: https://doi.org/10.1109/TEC.2003.821863.
Chedid R., Mrad F., Basma M. Intelligent control of a class of wind energy conversion systems. IEEE Transactions on Energy Conversion, 1999, vol. 14, no. 4, pp. 1597-1604. doi: https://doi.org/10.1109/60.815111.
Chiang H., Tsai H. Design and implementation of a grid‐tied wind power micro‐inverter. IET Renewable Power Generation, 2013, vol. 7, no. 5, pp. 493-503. doi: https://doi.org/10.1049/iet-rpg.2012.0342.
Hamouda N., Benalla H., Hemsas K., Babes B., Petzoldt J., Ellinger T., Hamouda C. Type-2 Fuzzy Logic Predictive Control of a Grid Connected Wind Power Systems with Integrated Active Power Filter Capabilities. Journal of Power Electronics, 2017, vol. 17, no. 6, pp. 1587-1599. doi: https://doi.org/10.6113/JPE.2017.17.6.1587.
Beddar A., Bouzekri H., Babes B., Afghoul H. Real time implementation of improved fractional order proportional-integral controller for grid connected wind energy conversion system. Revue Roumaine Des Sciences Techniques Serie Electrotechnique et Energetique, 2016, vol. 61, no. 4, pp. 402-407. Available at: http://revue.elth.pub.ro/upload/89285817_ABeddar_RRST_4_2016_pp_402-407.pdf (Accessed 12 June March 2021).
Kahla S., Bechouat M., Amieur T., Sedraoui M., Babes B., Hamouda N. Maximum power extraction framework using robust fractional-order feedback linearization control and GM-CPSO for PMSG-based WECS. Wind Engineering, 2021, vol. 45, no. 4, pp. 1040-1054. doi: https://doi.org/10.1177/0309524X20948263.
Bose B.K. Expert system, fuzzy logic, and neural network applications in power electronics and motion control. Proceedings of the IEEE, 1994, vol. 82, no. 8, pp. 1303-1323. doi: https://doi.org/10.1109/5.301690.
Mamdani E.H., Assilian S. An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Human-Computer Studies, 1999, vol. 51, no. 2, pp. 135-147. doi: https://doi.org/10.1006/ijhc.1973.0303.
Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, vol. SMC-15, no. 1, pp. 116-132. doi: https://doi.org/10.1109/TSMC.1985.6313399.
Wang L. Model predictive control system design and implementation using MATLAB. Springer London, 2009. doi: https://doi.org/10.1007/978-1-84882-331-0.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 B. Babes, N. Hamouda, S. Kahla, H. Amar, S.S.M. Ghoneim
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.