Improvement of the energy production of a photovoltaic-wind hybrid system using NF-PSO MPPT
Main Article Content
Abstract
This manuscript gives a contribution to the optimization of a hybrid Photovoltaic-Wind Turbine system with a storage system. In order to capture the maximum power that can be produced by each source, while maintaining the rotor speed of the wind turbine at its maximum values according to wind variations, the Neuro-Fuzzy-Particle Swarm Optimization (NF-PSO) controller is proposed. The Neuro-Fuzzy method is used here because it allows an automatic generation of fuzzy rules, and the Particle Swarm Optimization to find an optimal gain allowing to readjust the dynamics of the fuzzy rules by reducing the power losses (oscillations). For the proper functioning of such a system, we have developed a fuzzy supervisor in order to have an optimal control of the system according to the variations of the requested load and the produced power by considering the storage system and the load shedding. The simulation results of the system confirmed the better performance of this method in terms of speed with a response time of 0.2s on the wind side and 0.025s on the side photovoltaic, of efficiency with 99.87% on the photovoltaic side and 99.6% on the wind side, and above all in term of oscillation reduction with practically a negligible oscillation rate compared to the NF and the Cuckoo algorithm.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Krishna, K.S., and Kumar, K.S. A review on hybrid energy systems. Renewable and Sustainable Energy,Reviews, 2015; vol (52), 907 – 916. doi.org/10.1016/j.rser.2015.070187.
Madan, A.S, S. Balasubramanian, and G. Arunkumar. Current Status of Research on Hybrid Power Generation Systems. Res. 1. Appl. Sei. Eng. Technol, 2014; vol. 8, no.14, pp. 1684-1690. Doi: 10.19026/rjaset.8.1150.
Diaf. S, Diaf. D, M. Belhamed, M. Haddadi and A. Louche. A methodology for optimal sizing of autonomous hybrid PV/wind system. Energy Policy, 2007; vol 35(11), 5708–5718. doi: 10.1016/j.enpol.2007.06.020.
M. Egido and E. Lorenzo. The Sizing of Stand-Alone PV-Systems: A Review and a Proposed New Method. Solar Energy Materials & Solar Cells, 1992; 26, pp. 51-69. doi.org/10.1016/0927-0248(92)90125-9.
Hans Beyer and Christian Langer. A Method for the Identification of Configurations of PV/Wind Hybrid Systems for the Reliable Supply of Small Loads. Solar Energy, 1996; Vol. 57, pp. 381-391. doi:S0038-092X(96)00118-1.
Bouthaina. M, Rachid. C and Kamel E.H. Energy Management of the Stand-alone Hybrid Power System by Fuzzy Logic. 6th Eur. Conf. Ren. Energy Sys. 25-27 june 2018. https://www.researchgate.net/publication/326711741.
Courtecuisse. V, Srooten. J, and Robyns. B. A methodology to design a fuzzy logic based supervision of hybrid renewable energy systems. Mathematics and Computers in Simulation, 2010; 81: 208-224. doi: 0.1016/j.matcom.2010.03.003.
Ozgur Celik and Ahmet Teke. A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions. Electric Power Systems Research, 2017; 152, 194–210. doi.org/10.1016/j.epsr.2017.07.011.
Alireza. R, Ali. E, Hasan. E and Mohammad. M. Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for Wind turbine in the grid connected mode. Front. Energy, 2017; DOI: 10.1007/s11708-017-0446-x.
Ahmad. R, Hossein. K and Mahdi. M. A Comprehensive Method for Optimum Sizing of Hybrid Energy Systems using Intelligence Evolutionary Algorithms. Indian Journal of Science and Technology, 2013; Vol 6 (6), ISSN: 0974-6846.
Malek Zaibi, Gerard. C, Xavier. R, Jamel. B and Bruno. S. Smart power management of a hybrid photovoltaic/wind stand-alone system coupling battery storage and hydraulic network. Mathematics and Computers in Simulation, 2016; vol 146.pp. 210-228. ISSN 0378-4754. doi.org/10.1016/j.matcom.2016.08.009.
Sasan Moghaddam, Mehdi. B, Majid. M and Pierluigi. S. Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index. International Journal of Energy and Environmental Engineering, 2019; 10:429–449. doi.org/10.1007/s40095-019-00319-y.
Paul Abena, Philippe Djondine, P.N. Eloundou and H. Abena. A Novel Hybrid MPPT for Wind Energy Conversion Systems Operating under Low Variations in Wind Speed. Energy and Power Engineering, 2020; 12, 716-728. doi.org/10.4236/epe.2020.1212042.
Claude Bertin Nzoundja Fapi, Patrice Wira, Martin Kamta and Bruno Colicchio. Voltage Regulation Control with Adaptive Fuzzy Logic for a Stand-Alone Photovoltaic System. European Journal of Electrical Engineering, 2020; Vol. 22, No. 2, April, pp. 145-152. https://www.researchgate.net/publication/341622219.
Mostefa Kermadi and El Madjid. B. Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study. Renewable and Sustainable Energy Reviews, 2017; vol. 69, p. 369–386. doi.org/10.1016/j.rser.2016.11.125.
M. Premkumar and R. Sowmya. An effective maximum power point tracker for partially shaded solar photovoltaic systems. Energy Reports, 2019; 5, 1445–1462. doi.org/10.1016/j.egyr.2019.10.006.
Ramji Tiwari and Babu, N. R. fuzzy logic based MPPT for permanent magnet synchronous generator in wind energy conversion system. IFAC-PapersOnLine, 2016; 49-1.462–467. doi:10.1016/j.ifacol.2016.03.097.
Huynh, Q., Nollet, F., Essounbouli, N., and Hamzaoui, A. Fuzzy control of variable speed wind turbine using permanent magnet synchronous machine for stand-alone system. In Sustainability in Energy and Buildings. Proceedings of the 3rd International Conference in Sustainability in Energy and Buildings (SEB 11), 2012; vol 12, page 31-44. Springer Verlag.
Jyh-Shing Roger Jang. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on systems, Man, and Cybernetics, 1993; Vol 23, N°3, 665-685. doi: 0018-9472/93$03.00.
Chiou, J.S. and Liu, M.T. Numerical simulation for Fuzzy-PID controllers and helping EP Reproduction with PSO hybrid algorithm, Simulation. Modelling Practice and Theory, 2009; Vol. 17, pp.1555–1565. doi: 10.1016/j.simpat2009.05.006.
Bouarroudj, N., Boukhetala, D. and Boudjema, F. Tuning Fuzzy PDa sliding mode controller using PSO algorithm for trajectory tracking of a chaotic system. Journal of Electrical Engineering, 2014; Vol. 14, N° 2, pp.378–385.
Bouarroudj, N., Boukhetala, D. and Boudjema, F. A hybrid fuzzy fractional order PID Sliding-Mode controller design using PSO algorithm for interconnected Nonlinear Systems. Journal of Control Engineering and Applied Informatics, 2015; Vol. 17, No. 1, pp.41–51.
Abdelhalim Borni, Noureddine Bouarroudj, Abdelhak Bouchakour and Layachi Zaghba. P&O-PI and fuzzy-PI MPPT Controllers and their time domain optimization using PSO and GA for grid-connected photovoltaic system: a comparative study. International Journal of Power Electronics, 2017; 8(4), 300-322.