A review of artificial intelligence-based optimization techniques for the sizing of integrated renewable energy systems in smart cities

References

• Panchal HN, Patel N. ANSYS CFD and experimental comparison of various parameters of a solar still. Int J Ambient Energy. 2018;39(6):551–557. DOI:10.1080/01430750.2017.1318785.
   Web of Science ®Google Scholar
• Panchal H, Hemin T. Theoretical and experimental validation of evacuated tubes directly coupled with solar still. Therm Eng. 2016;63(11):825–831. DOI:10.1109/EnergyCon.2012.6348251.
   Google Scholar
• Panchal H, Awasthi A. Theoretical modeling and experimental analysis of solar still integrated with evacuated tubes. Heat Mass Transfer. 2017;53(6):1943–1955. DOI:10.1007/s00231-016-1953-8.
   Web of Science ®Google Scholar
• Sheela A, Suresh M, Gowri Shankar V, et al. FEA based analysis and design of PMSM for electric Vehicle applications using magnet software. Int J Ambient Energy. DOI:10.1080/01430750.2020.1762736
   Web of Science ®Google Scholar
• Bhattacharya TR, Bhattacharya A, Mclellan B, et al. Sustainable smart city development framework for developing countries. Urban Res Pract. 2020;13:180–212. DOI:10.1080/17535069.2018.1537003.
   Web of Science ®Google Scholar
• Suresh M, Meenakumari R, Panchal H, et al. An enhanced multiobjective particle swarm optimisation algorithm for optimum utilisation of hybrid renewable energy systems. Int J Ambient Energy. DOI:10.1080/01430750.2020.1737837.
   Web of Science ®Google Scholar
• Zhao X, Jiang N, Liu J, et al. Short-term average wind speed and turbulent standard deviation forecasts based on one-dimensional convolutional neural network and the integrate method for probabilistic framework. Energy Convers Manag. 2020;203:112239. DOI:10.1016/j.enconman.2019.112239.
   Web of Science ®Google Scholar
• Akella AK, Saini RP, Sharma MP. Social, economical and environmental impacts of renewable energy systems. Renew Energy. 2008;34:390–396. DOI:10.1016/j.renene.2008.05.002.
   Web of Science ®Google Scholar
• Zekić-Sušac M, Mitrović S, Has A. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. Int J Inf Manage. 2020;102074. DOI:10.1016/j.ijinfomgt.2020.102074.
   Web of Science ®Google Scholar
• Ahad MA, Paiva S, Tripathi G, et al. Enabling technologies and sustainable smart cities. Sustain Cities Soc. 2020;61:102301. DOI:10.1016/j.scs.2020.102301.
   Web of Science ®Google Scholar
• Bansal M, Khatod DK, Saini RP. Modeling and optimization of integrated renewable energy system for a rural site. ICROIT 2014 – Proceedings of the 2014 International Conference on Reliability, Optimization and Information Technology. 2014. p. 25–28. DOI:10.1109/ICROIT.2014.6798289.
   Google Scholar
• Kakran S, Chanana S. Smart operations of smart grids integrated with distributed generation: a review. Renew Sustain Energy Rev. 2018;81:524–535. DOI:10.1016/j.rser.2017.07.045.
   Web of Science ®Google Scholar
• Flores HFV, Furubayashi T, Nakata T. Decentralised electricity generation system based on local renewable energy sources in the Honduran rural residential sector. Clean Technol Environ Policy. 2016;18:883–900. DOI:10.1007/s10098-015-1067-x.
   Web of Science ®Google Scholar
• Wang X, Palazoglu A, El-Farra NH, et al. Operational optimization and demand response of hybrid renewable energy systems. Appl Energy. 2015;143:324–335. DOI:10.1016/j.apenergy.2015.01.004.
   Web of Science ®Google Scholar
• Figueiredo J, Martins J. Energy production system management – renewable energy power supply integration with building automation system. Energy Convers Manag. 2010;51:1120–1126. DOI:10.1016/j.enconman.2009.12.020.
   Web of Science ®Google Scholar
• Bansal M, Saini RP, Khatod DK. An off-grid hybrid system scheduling for a remote area. 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science: Innovation for Humanity SCEECS 2012. 2012. p. 9–12. DOI:10.1109/SCEECS.2012.6184799.
   Google Scholar
• MacCarty NA, Bryden KM. An integrated systems model for energy services in rural developing communities. Energy. 2016;113:536–557. DOI:10.1016/j.energy.2016.06.145.
   Web of Science ®Google Scholar
• Kaygusuz A, Keles C, Alagoz BB, et al. Renewable energy integration for smart sites. Energy Build. 2013;64:456–462. DOI:10.1016/j.enbuild.2013.05.031.
   Web of Science ®Google Scholar
• Chai DS, Wen JZ, Nathwani J. Simulation of cogeneration within the concept of smart energy networks. Energy Convers Manag. 2013;75:453–465. DOI:10.1016/j.enconman.2013.06.045.
   Web of Science ®Google Scholar
• Mattoni B, Gugliermetti F, Bisegna F. A multilevel method to assess and design the renovation and integration of smart cities. Sustain Cities Soc. 2015;15:105–119. DOI:10.1016/j.scs.2014.12.002.
   Web of Science ®Google Scholar
• Ahmed NA, Al-Othman AK, Alrashidi MR. Development of an efficient utility interactive combined wind/photovoltaic/fuel cell power system with MPPT and DC bus voltage regulation. Electr Power Syst Res. 2011;81:1096–1106. DOI:10.1016/j.epsr.2010.12.015.
   Web of Science ®Google Scholar
• Strielkowski W, Veinbender T, Tvaronavičienė M, et al. Economic efficiency and energy security of smart cities. Econ Res Istraz. 2020;33:788–803. DOI:10.1080/1331677X.2020.1734854.
   Web of Science ®Google Scholar
• Kanase-Patil AB, Saini RP, Sharma MP. Integrated renewable energy systems for off grid rural electrification of remote area. Renew Energy. 2010;35:1342–1349. DOI:10.1016/j.renene.2009.10.005.
   Web of Science ®Google Scholar
• Fadaee M, Radzi MAM. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: a review. Renew Sustain Energy Rev. 2012;16:3364–3369. DOI:10.1016/j.rser.2012.02.071.
   Web of Science ®Google Scholar
• Chávez-Ramírez AU, Vallejo-Becerra V, Cruz JC, et al. A hybrid power plant (solar-wind-hydrogen) model based in artificial intelligence for a remote-housing application in Mexico. Int J Hydrogen Energy. 2013;38:2641–2655. DOI:10.1016/j.ijhydene.2012.11.140.
   Web of Science ®Google Scholar
• Al-Alawi A, Al-Alawi SM, Islam SM. Predictive control of an integrated PV-diesel water and power supply system using an artificial neural network. Renew Energy. 2007;32:1426–1439. DOI:10.1016/j.renene.2006.05.003.
   Web of Science ®Google Scholar
• Wang H, Abdollahi E, Lahdelma R, et al. Modelling and optimization of the smart hybrid renewable energy for communities (SHREC). Renew Energy. 2015;84:114–123. DOI:10.1016/j.renene.2015.05.036.
   Web of Science ®Google Scholar
• Mahesh A, Sandhu KS. Optimal sizing of a grid-connected PV/wind/battery system using particle swarm optimization. Iran J Sci Technol Trans Electr Eng. 2019;43:107–121. DOI:10.1007/s40998-018-0083-3.
   Web of Science ®Google Scholar
• Erdinc O, Uzunoglu M. Optimum design of hybrid renewable energy systems: overview of different approaches. Renew Sustain Energy Rev. 2012;16:1412–1425. DOI:10.1016/j.rser.2011.11.011.
   Web of Science ®Google Scholar
• Shiroudi A, Taklimi SRH, Mousavifar SA, et al. Stand-alone PV-hydrogen energy system in Taleghan-Iran using HOMER software: optimization and techno-economic analysis. Environ Dev Sustain. 2013;15:1389–1402. DOI:10.1007/s10668-013-9443-3.
   Google Scholar
• Bhandari B, Lee K, Lee G, et al. Optimization of hybrid renewable energy power systems: a review. Int J Precis Eng Manuf-Green Technol. 2015;2:99–112. Available from: https://link.springer.com/article/10.1007/s40684-015-0013-z.
   Web of Science ®Google Scholar
• Eltamaly AM, Mohamed MA. A novel design and optimization software for autonomous PV/wind/battery hybrid power systems. Math Probl Eng. 2014;2014. DOI:10.1155/2014/637174.
   Web of Science ®Google Scholar
• Gül T. Integrated analysis of hybrid systems for rural electrification in developing countries. 2000. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.689.1306&rep=rep1&type=pdf.
   Google Scholar
• Kumar H, Cheshta V. A novel hybrid statistical particle swarm optimization for multimodal functions and frequency control of hybrid wind-solar system. J Inst Eng Ser B. 2015. DOI:10.1007/s40031-015-0213-5.
   Google Scholar
• Krishna KS, Kumar KS. A review on hybrid renewable energy systems. Renew Sustain Energy Rev. 2015;52:907–916. DOI:10.1016/j.rser.2015.07.187.
   Web of Science ®Google Scholar
• Rozali NEM, Wan Alwi SR, Manan ZA, et al. A process integration approach for design of hybrid power systems with energy storage. Clean Technol Environ Policy. 2015: 1–18. DOI:10.1007/s10098-015-0934-9.
   Web of Science ®Google Scholar
• Allam Z, Dhunny ZA. On big data, artificial intelligence and smart cities. Cities. 2019;89:80–91. DOI:10.1016/j.cities.2019.01.032.
   Web of Science ®Google Scholar
• Nehrir MH, Wang C, Strunz K, et al. A review of hybrid renewable / alternative energy systems for electric power generation. IEEE Trans Sustain Energy. 2011;2:392–403. DOI:10.1109/TSTE.2011.2157540.
   Web of Science ®Google Scholar
• Hakimi SM, Hasankhani A, Shafie-Khah M, et al. Demand response method for smart microgrids considering high renewable energies penetration. Sustain Energy Grids Netw. 2020;21:100325. DOI:10.1016/j.segan.2020.100325.
   Web of Science ®Google Scholar
• Ricalde LJ, Ordonez E, Gamez M, et al. Design of a smart grid management system with renewable energy generation. 2011. 4 pp. DOI:10.1109/CIASG.2011.5953346.
   Google Scholar
• Bhoyar RR, Bharatkar SS. Renewable energy integration in to microgrid: powering rural Maharashtra State of India. 2013 Annual IEEE India Conference INDICON 2013; 2013. DOI:10.1109/INDCON.2013.6725877
   Google Scholar
• Sarker MaR, Nagasaka K. Web enabled smart microgrid model with renewable energy resources in Bangladesh power system. International Conference on Advanced Mechatronic Systems ICAMechS 2012. 2012:345–350. Available from: https://ieeexplore.ieee.org/document/6329601/.
   Google Scholar
• Strasser T, Andren F, Kathan J, et al. A review of architectures and concepts for intelligence in future electric energy systems. IEEE Trans Ind Electron. 2015;62:2424–2438. DOI:10.1109/TIE.2014.2361486.
   Web of Science ®Google Scholar
• Kang D, Park JH, Member SYE. Intelligent decision-making system with green pervasive computing for renewable energy business in electricity markets on smart grid. 2009. DOI:10.1155/2009/247483
   Google Scholar
• Zahurul S, Mariun N, Grozescu IV, et al. Future strategic plan analysis for integrating distributed renewable generation to smart grid through wireless sensor network: Malaysia prospect. Renew Sustain Energy Rev. 2016;53:978–992. DOI:10.1016/j.rser.2015.09.020.
   Web of Science ®Google Scholar
• Saraiva FdO, Bernardes WMS, Asada EN. A framework for classification of non-linear loads in smart grids using artificial neural networks and multi-agent systems. Neurocomputing. 2015;170:328–338. DOI:10.1016/j.neucom.2015.02.090.
   Web of Science ®Google Scholar
• Mote TP, Lokhande SD. Temperature control system using ANFIS. Int J Comput Eng. 2012;2:156–161. Available from: https://pdfs.semanticscholar.org/fe7d/671033d3d9965642961dce35f175dea11eed.pdf.
   Google Scholar
• Ilakkia T, Vijayagowri G. Hybrid Pv/wind system for reduction of harmonics using artificial intelligence technique. 2012. p. 303–308. Available from: https://ieeexplore.ieee.org/document/6216133/.
   Google Scholar
• Bhandari B, Poudel SR, Lee K-T, et al. Mathematical modeling of hybrid renewable energy system: A review on small hydro-solar-wind power generation. Int J Precis Eng Manuf Technol. 2014;1:157–173. DOI:10.1007/s40684-014-0021-4.
   Web of Science ®Google Scholar
• Wang H, Li H, Tang C, et al. Modeling, metrics, and optimal design for solar energy-powered base station system. EURASIP J Wirel Commun Netw. 2015;39. DOI:10.1186/s13638-015-0270-0.
   Google Scholar
• Alfonso D, Ariza HE, Carcel J, et al. Experimental verification of hybrid renewable systems as feasible energy sources. Renew Energy. 2016;86:384–391. DOI:10.1016/j.renene.2015.08.030.
   Web of Science ®Google Scholar
• Hooshmand R-A, Amooshahi H, Parastegari M. A hybrid intelligent algorithm based short-term load forecasting approach. Int J Electr Power Energy Syst. 2013;45:313–324. DOI:10.1016/j.ijepes.2012.09.002.
   Web of Science ®Google Scholar
• Eltamaly AM, Addoweesh KE, Bawa U, et al. Economic modeling of hybrid renewable energy system: a case study in Saudi Arabia. Arab J Sci Eng. 2014. DOI:10.1007/s13369-014-0945-6
   Web of Science ®Google Scholar
• Katsigiannis YA, Kanellos FD, Papaefthimiou S. A software tool for capacity optimization of hybrid power systems including renewable energy technologies based on a hybrid genetic algorithm – Tabu search optimization methodology. Energy Syst. 2014: 33–48. DOI:10.1007/s12667-014-0138-0.
   Google Scholar
• Sinha S, Chandel S. Review of software tools for hybrid renewable energy systems. Renew Sustain Energy Rev. 2014;32:192–205. DOI:10.1016/j.rser.2014.01.035.
   Web of Science ®Google Scholar
• Jadhav HT, Roy R. Gbest guided artificial Bee Colony algorithm for environmental/economic dispatch considering wind power. Expert Syst Appl. 2013;40(16):6385–6399. DOI:10.1016/j.eswa.2013.05.048
   Web of Science ®Google Scholar
• Wang J, Li L, Niu D, et al. An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy. 2012;94:65–70. DOI:10.1016/j.apenergy.2012.01.010.
   Web of Science ®Google Scholar
• Kanase-Patil AB, Saini RP, Sharma MP. Development of IREOM model based on seasonally varying load profile for hilly remote areas of Uttarakhand state in India. Energy. 2011;36:5690–5702. DOI:10.1016/j.energy.2011.06.057.
   Web of Science ®Google Scholar
• Calvillo CF, Sánchez-Miralles A, Villar J. Energy management and planning in smart cities. Renew Sustain Energy Rev. 2016;55:273–287. DOI:10.1016/j.rser.2015.10.133.
   Web of Science ®Google Scholar
• Kalogirou SA. Application of artificial neural-networks for energy systems. Appl Energy. 2000;67:17–35. DOI:10.1016/S0306-2619(00)00005-2.
   Web of Science ®Google Scholar
• Ata R. Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev. 2015;49:534–562. DOI:10.1016/j.rser.2015.04.166.
   Web of Science ®Google Scholar
• Simoglou CK, Kardakos EG, Bakirtzis EA, et al. An advanced model for the efficient and reliable short-term operation of insular electricity networks with high renewable energy sources penetration. Renew Sustain Energy Rev. 2014;38:415–427. DOI:10.1016/j.rser.2014.06.015.
   Web of Science ®Google Scholar
• Zhang Z-Y, Wang K-S. Wind turbine fault detection based on SCADA data analysis using ANN. Adv Manuf. 2014;2(1):70–78. DOI:10.1007/s40436-014-0061-6.
   Web of Science ®Google Scholar
• Yezioro A, Dong B, Leite F. An applied artificial intelligence approach towards assessing building performance simulation tools. Energy Build. 2008;40:612–620. DOI:10.1016/j.enbuild.2007.04.014.
   Web of Science ®Google Scholar
• Elgendy MA, Zahawi B, Atkinson DJ. Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans Sustain Energy. 2012;3:21–33. DOI:10.1109/TSTE.2011.2168245.
   Web of Science ®Google Scholar
• Sandeep SR, Nandihalli R. Optimal sizing in hybrid renewable energy system with the aid of opposition based social spider optimization. J Electr Eng Technol. 2020;15:433–440. DOI:10.1007/s42835-019-00184-z.
   Web of Science ®Google Scholar
• Kanase-Patil AB, Saini RP, Sharma MP. Sizing of integrated renewable energy system based on load profiles and reliability index for the state of Uttarakhand in India. Renew Energy. 2011;36:2809–2821. DOI:10.1016/j.renene.2011.04.022.
   Web of Science ®Google Scholar
• Khosravi A, Malekan M, Pabon JJG, et al. Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm. J Clean Prod. 2020;244. DOI:10.1016/j.jclepro.2019.118904.
   PubMed Web of Science ®Google Scholar
• Javed F, Arshad N, Wallin F, et al. Forecasting for demand response in smartgrids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting. Appl Energy. 2012;96:150–160. DOI:10.1016/j.apenergy.2012.02.027.
   Web of Science ®Google Scholar
• Hemeida AM, El-Ahmar MH, El-Sayed AM, et al. Optimum design of hybrid wind/PV energy system for remote area. Ain Shams Eng J. 2020;11(1):11–23. DOI:10.1016/j.asej.2019.08.005.
   Web of Science ®Google Scholar
• Ullah S, Qureshi IM, Zohaib MM. Design of an artificial-intelligence based controller for doubly fed induction generator based wind energy conversion system. 2016. DOI:10.1109/INTELSE.2016.7475141.
   Google Scholar
• Motahar S, Bagheri-Esfeh H. Artificial neural network based assessment of grid-connected photovoltaic thermal systems in heating dominated regions of Iran. Sustain Energy Technol Assessments. 2020;39:100694. DOI:10.1016/j.seta.2020.100694.
   Web of Science ®Google Scholar
• Quan H, Srinivasan D, Khambadkone AM, et al. A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources. Appl Energy. 2015;152:71–82. DOI:10.1016/j.apenergy.2015.04.103.
   Web of Science ®Google Scholar
• Mellit A, Pavan AM. A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol Energy. 2010;84:807–821. DOI:10.1016/j.solener.2010.02.006.
   Web of Science ®Google Scholar
• Dorvlo ASS, Jervase JA, Al-Lawati A. Solar radiation estimation using artificial neural networks. Appl Energy. 2002;71:307–319. DOI:10.1016/S0306-2619(02)00016-8.
   Web of Science ®Google Scholar
• Natsheh EM, Albarbar A. Hybrid power systems energy controller based on neural network and fuzzy logic. smart grid. Renew Energy. 2013;4:187–197. DOI:10.4236/sgre.2013.42023.
   Google Scholar
• Ranganayaki V, Deepa SN, Ranganayaki V, et al. An intelligent ensemble neural network model for wind speed prediction in renewable energy systems. Sci World J. 2016;2016:1–14. DOI:10.1155/2016/9293529.
   Google Scholar
• Salcedo-Sanz S, Ángel MPB, Ortiz-García EG, et al. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction. Renew Energy. 2009;34:1451–1457. DOI:10.1016/j.renene.2008.10.017.
   Web of Science ®Google Scholar
• Khatibi R, Naghipour L, Ghorbani MA, et al. Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations. Neural Comput Appl. 2013;23:2241–2252. DOI:10.1007/s00521-012-1175-z.
   Web of Science ®Google Scholar
• Aravind CK, Saravana Ilango G, Nagamani C. A smooth co-ordination control for a hybrid autonomous power system (HAPS) with battery energy storage (BES). Front Energy. 2015;9:31–42. DOI:10.1007/s11708-015-0347-9.
   Web of Science ®Google Scholar
• Vinay P, Mathews MA. Modelling and analysis of artificial intelligence based MPPT techniques for PV applications. 2014. p. 56–65. Available from: https://ieeexplore.ieee.org/document/7050144/.
   Google Scholar
• Qamar M, Khosravi A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev. 2015;50:1352–1372. DOI:10.1016/j.rser.2015.04.065.
   Web of Science ®Google Scholar
• Geng L. A kind of web-based remote supervisory and control system for power plants in WAN 2 architecture, operational pattern and features of web services. Power. 2007. DOI:10.1109/ICEMI.2007.4350881.
   Google Scholar
• Pawar P, TarunKumar M, Vittal KP. An IoT based intelligent smart energy management system with accurate forecasting and load strategy for renewable generation. Meas J Int Meas Confed. 2020;152:107187. DOI:10.1016/j.measurement.2019.107187.
   Web of Science ®Google Scholar
• Ruiz-Romero S, Colmenar-Santos A. Integration of distributed generation in the power distribution network: the need for smart grid control systems, communication and equipment for a smart city – use cases. Renew Sustain Energy Rev. 2014;38:223–234. DOI:10.1016/j.rser.2014.05.082.
   Web of Science ®Google Scholar
• Baharudin Z, Kamel N. Autoregressive method in short term load forecast. PECon 2008 – 2008 IEEE 2nd International Power and Energy Conference. 2008. p. 1603–1608. DOI:10.1109/PECON.2008.4762735.
   Google Scholar
• Prado F, Minutolo MC, Kristjanpoller W. Forecasting based on an ensemble autoregressive moving average – adaptive neuro – fuzzy Inference system – neural network – genetic algorithm framework. Energy. 2020;197. DOI:10.1016/j.energy.2020.117159.
   Web of Science ®Google Scholar
• Borges CE, Penya YK, Ferńandez I, et al. Assessing tolerance-based robust short-term load forecasting in buildings. Energies. 2013;6:2110–2129. DOI:10.3390/en6042110.
   Web of Science ®Google Scholar
• Suganthi L, Iniyan S, Samuel AA. Applications of fuzzy logic in renewable energy systems – a review. Renew Sustain Energy Rev. 2015;48:585–607. DOI:10.1016/j.rser.2015.04.037.
   Web of Science ®Google Scholar
• Ghugare SB, Tiwary S, Elangovan V, et al. Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms. Bioenergy Res. 2014;7:681–692. DOI:10.1007/s12155-013-9393-5.
   Web of Science ®Google Scholar
• Mirakyan A, De Guio R. Integrated energy planning in cities and territories: a review of methods and tools. Renew Sustain Energy Rev. 2013;22:289–297. DOI:10.1016/j.rser.2013.01.033.
   Web of Science ®Google Scholar
• Chui KT, Lytras MD, Visvizi A. Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies. 2018;11:1–20. DOI:10.3390/en11112869.
   Web of Science ®Google Scholar
• Soufi Y, Bechouat M, Kahla S. Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system. Int J Hydrogen Energy. 2017;42:8680–8688. DOI:10.1016/j.ijhydene.2016.07.212.
   Web of Science ®Google Scholar
• Faquir S, Yahyaouy A, Tairi H, et al. A type-1 fuzzy logic algorithm to manage the flow of energy in a stand-alone PV/wind/battery hybrid system. 2015 Intelligent Systems and Computer Vision. 2015. p. 1–6. DOI:10.1109/ISACV.2015.7106186.
   Google Scholar
• Meghni B, Dib D, Azar AT. A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage. Neural Comput Appl. 2016:1–18. DOI:10.1007/s00521-015-2161-z.
   Web of Science ®Google Scholar
• Wang CC, Wu MC, Ou SY, et al. Analysis and research on maximum power point tracking of photovoltaic array with fuzzy logic control and three-point weight comparison method. Sci China-Technol Sci. 2010;53:2183–2189. DOI:10.1007/s11431-010-4024-2.
   Web of Science ®Google Scholar
• Khaehintung N, Kunakorn A, Sirisuk P. A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control. Int J Control Autom Syst. 2010;8:289–300. DOI:10.1007/s12555-010-0215-7.
   Web of Science ®Google Scholar
• Vincheh MR, Kargar A, Markadeh GA. A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems. Arab J Sci Eng. 2014;39:4715–4725. DOI:10.1007/s13369-014-1056-0.
   Web of Science ®Google Scholar