Nature-Inspired Optimization Algorithms in Solving Partial Shading Problems: A Systematic Review
Tóm tắt
Artificial intelligence based maximum power point tracking (MPPT) techniques play an essential role in improving the efficiency of photovoltaic power conversion systems. Over the past few years, researchers around the world have proposed various nature-inspired metaheuristic optimization algorithms in order to extract the highest possible power from photovoltaic (PV) systems under partial shading conditions. These approaches were developed to track for the maximum power point (MPP) efficiently with fast convergence speed and high accuracy. This paper provides a systematic review on these state-of-the-art computing mechanisms with their recent advancements, modifications and adaptations in tracking for the MPP of PV systems under partial shading conditions. The technical advantages, trade-offs, and challenges of these computation mechanisms are analysed and discussed. In-depth study found that nature-inspired swarm search mechanisms are highly suitable to be implemented as MPPT schemes in PV applications. Recent developments and improvements show enhancements in multiple different aspects, especially in the accuracy and the speed of the search algorithms. Several research gaps are identified and discussed to guide future research directions.
Tài liệu tham khảo
Pachauri RK, Thanikanti SB, Bai J, Yadav VK, Aljafari B, Ghosh S, Alhelou H (2022) Ancient Chinese magic square-based PV array reconfiguration methodology to reduce power loss under partial shading conditions. Energy Convers Manage 253:115148. https://doi.org/10.1016/j.enconman.2021.115148
Tan JD, Choe WCC, Mohammad ASB, Khairun NM, Kharudin A (2022) Advancements of wind energy conversion systems for low-wind urban environments: a review. Energy Rep 8:3406–3414. https://doi.org/10.1016/j.egyr.2022.02.153
Gaabour A, Metatla A, Kelaiaia R, Bourennani F, Kerboua A (2019) Recent bibliography on the optimization of multi-source energy systems. Arch Comput Methods Eng 26:809–830. https://doi.org/10.1007/s11831-018-9271-6
Koay YY, Tan JD, Koh SP, Chong KH, Tiong SK, Ekanayake J (2020) Optimization of wind energy conversion systems—an artificial intelligent approach. Int J Power Electron Drive Syst 11(2):1040–1046. https://doi.org/10.11591/ijpeds.v11.i2.pp1040-1046
Chamundeeswari V, Seyezhai R (2017) PSO-PID maximum power point tracking controller using modified superlift luo converter. Energy Procedia 117:87–94. https://doi.org/10.1016/j.egypro.2017.05.110
Tan JD, Koh SP, Tiong SK, Kharudin A, Koay YY (2018) An electromagnetism-like mechanism algorithm approach for photovoltaic system optimization. Indones J Electr Eng Comput Sci 12(1):333–340. https://doi.org/10.11591/ijeecs.v12.i1.pp333-340
Chai LGK, Gopal L, Juwono FH, Chiong CWR, Ling HC, Basuki TA (2021) A novel global MPPT technique using improved PS-FW algorithm for PV system under partial shading conditions. Energy Convers Manage 246:114639. https://doi.org/10.1016/j.enconman.2021.114639
Venugopal S, Aspalli A, Raveendra R (2017) Maximum power point tracking for photovoltaic systems. In: Third international conference on current trends in engineering science and technology ICCTEST-2017. https://doi.org/10.21647/icctest/2017/49002
Pal RS, Mukherjee V (2021) A novel population based maximum power point tracking algorithm to overcome partial shading issues in solar photovoltaic technology. Energy Convers Manage 244:114470. https://doi.org/10.1016/j.enconman.2021.114470
Tan JD, Mahidzal D, Koh SP, Koay YY, Issa AA (2017) Analysis of the effect of search step size on the accuracy and convergence properties of electromagnetism-like mechanism algorithm. J Mult-Valued Logic Soft Comput 28(4):429–441
Aygül K, Cikan M, Demirdelen T, Tumay M (2019) Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources A. https://doi.org/10.1080/15567036.2019.1677818
Abdesalam A, Massoud A, Ahmed A, Enjeti P (2011) High performance adaptive perturb and observe MPPT technique for photovoltaic basedmicro grids. IEEE Trans Power Electron 26(4):1010–1021. https://doi.org/10.1109/TPEL.2011.2106221
Safari A, Mekhilef S (2011) Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuck converter. IEEE Trans Ind Electron 58(4):1154–1156. https://doi.org/10.1109/TIE.2010.2048834
Koutrouli E, Kalaitzakis K, Voulgaris NC (2001) Development of microcontroller based photovoltaic maximum power point tracking control system. Power Electron IEEE Trans 16:46–54
Fathi FD, Shams IM, Mekhilef S (2021) A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions. Energy Convers Manage 230:113773. https://doi.org/10.1016/j.enconman.2020.113773
Tan JD, Mahidzal D, Koh SP, Koay YY, Issa AA (2016) An improved electromagnetism-like algorithm for numerical optimization. Theor Comput Sci 641:75–84. https://doi.org/10.1016/j.tcs.2016.05.045
Li C, Yang Y, Zhang K, Zhu C, Wei H (2021) A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays. Energy Convers Manage 234:113950. https://doi.org/10.1016/j.enconman.2021.113950
Tan JD, Mahidzal D, Koh SP, Koay YY, Issa AA (2016) A new experiential learning electromagnetism-like mechanism for numerical optimization. Expert Syst Appl 86:321–333. https://doi.org/10.1016/j.eswa.2017.06.002
Molina D, Poyatos J, Ser J, García S, Hussain A, Herrera F (2020) Comprehensive taxonomies of nature- and bio-inspired optimization: inspiration versus algorithmic behavior. Crit Anal Recomm Cognit Comput 12(5):897–939. https://doi.org/10.1007/s12559-020-09730-8
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, Piscataway, pp 1942–1948
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature & biologically inspired computing, pp 210–214
Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2012) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57. https://doi.org/10.1007/s10462-012-9328-0
Gupta S, Saurabh K (2017) Artificial mountain ape optimization algorithm for maximum power point tracking under partial shading condition. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS). https://doi.org/10.1109/icecds.2017.8389547
Yang XS, He XS (2017) Why the firefly algorithm works? Stud Comput Intell. https://doi.org/10.1007/978-3-319-67669-2_11
Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh A (2019) Moth–flame optimization algorithm: variants and applications. Neural Comput Appl 32(14):9859–9884. https://doi.org/10.1007/s00521-019-04570-6
Elattar E (2019) Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm. Energy 171:256–269. https://doi.org/10.1016/j.energy.2019.01.010
Koopialipoor M, Noorbakhsh A (2020) Applications of artificial intelligence techniques in optimizing drilling. Emerg Trends Mechatron. https://doi.org/10.5772/intechopen.85398
Mohapatra A, Nayak B, Das P, Mohanty KB (2017) A review on MPPT techniques of PV system under partial shading condition. Renew Sustain Energy Rev 80:854–867. https://doi.org/10.1016/j.rser.2017.05.083
Srinivasan V, Boopathi C, Sridhar R (2021) A new meerkat optimization algorithm based maximum power point tracking for partially shaded photovoltaic system. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2021.03.017
Ishaque K, Salam Z (2013) A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Trans Ind Electron. https://doi.org/10.1109/tie.2012.2200223
Pan L, Zhao Y, Li L (2022) Neighbourhood-based particle swarm optimization with discrete crossover for nonlinear equation systems. Swarm Evol Comput 69:101019
Hu K, Cao S, Li W, Zhu F (2019) An improved particle swarm optimization algorithm suitable for photovoltaic power tracking under partial shading conditions. IEEE Access 7:143217–143232. https://doi.org/10.1109/access.2019.2944964
Li H, Yang D, Su W, Lu J, Yu X (2018) An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Trans Ind Electron. https://doi.org/10.1109/tie.2018.2829668
Rajasekar N, Vysakh M, Thakur H, Azharuddin S, Muralidhar K, Paul D et al (2014) Application of modified particle swarm optimization for maximum power point tracking under partial shading condition. Energy Procedia 61:2633–2639. https://doi.org/10.1016/j.egypro.2014.12.265
Alshareef M, Lin Z, Ma M, Cao W (2019) Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions. Energies 12(4):623. https://doi.org/10.3390/en12040623
Mao M, Duan Q, Zhang L, Chen H, Hu B, Duan P (2017) Maximum power point tracking for cascaded PV-converter modules using two-stage particle swarm optimization. Sci Rep. https://doi.org/10.1038/s41598-017-08009-7
Oliveira S, Bezerra L, Stützle T, Dorigo M, Wanner E, de Souza S (2021) Computational study on ant colony optimization for the traveling salesman problem with dynamic demands. Comput Oper Res. https://doi.org/10.1016/j.cor.2021.105359
Jiang LL, Maskell DL, Patra JC (2013) A novel ant colony optimization based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy Build 58:227–236. https://doi.org/10.1016/j.enbuild.2012.12.001
Sundareswaran K, Krishnan GS, Simon S, Nayak P (2020) MPPT in PV systems using ant colony optimization with dwindling population. IET Renew Power Gener. https://doi.org/10.1049/iet-rpg.2019.0875
Stodola P, Otrisal P, Hasilova K (2022) Adaptive ant colony optimization with node clustering applied to the travelling salesman problem. Swarm Evol Comput 70:101056
Titri S, Larbes C, Toumi KY, Benatchba K (2017) A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Appl Soft Comput 58:465–479. https://doi.org/10.1016/j.asoc.2017.05.017
Chao K, Rizal M (2021) A hybrid MPPT controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions. Energies 14(10):2902. https://doi.org/10.3390/en14102902
Benyoucef A, Chouder A, Kara K, Silvestre S, Sahed OA (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 32(38):48. https://doi.org/10.1016/j.asoc.2015.03.047
Fathy A (2015) Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm. Renew Energy 81:78–88. https://doi.org/10.1016/j.renene.2015.03.017
Padmanaban S, Priyadarshi N, Sagar Bhaskar M, Holm-Nielsen J, Ramachandaramurthy V, Hossain E (2019) A hybrid ANFIS-ABC based MPPT controller for PV system with anti-islanding grid protection: experimental realization. IEEE Access 7:103377–103389. https://doi.org/10.1109/access.2019.2931547
Gao X, Ding D, Yang S, Huang M (2020) Application of a chaotic quantum bee colony and support vector regression to multipeak maximum power point tracking control method under partial shading conditions. Int J Comput Intell Appl 19(02):2050014. https://doi.org/10.1142/s1469026820500145
Goud JRK, Singh B, Kumar S (2018) Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array. IET Renew Power Gener 12(16):1915–1922. https://doi.org/10.1049/iet-rpg.2018.5116
Ahmed J, Salam Z (2014) A maximum power point tracking (MPPT) for PV system using cuckoo search with partial shading capability. Appl Energy 119(118):130. https://doi.org/10.1016/j.apenergy.2013.12.062
Ibrahim A, Aboelsaud R, Obukhov S (2019) Maximum power point tracking of partially shading PV system using cuckoo search algorithm. Int J Power Electron Drive Syst 10(2):1081–1089. https://doi.org/10.11591/ijpeds.v10.i2.pp1081-1089
Salgotra R, Singh U, Saha S, Gandomi AH (2021) Self adaptive cuckoo search: analysis and experimentation. Swarm Evol Comput 60:100751
Eltamaly A (2021) An improved cuckoo search algorithm for maximum power point tracking of photovoltaic systems under partial shading conditions. Energies 14(4):953. https://doi.org/10.3390/en14040953
Seyedmahmoudian M, Kok Soon T, Jamei E, Thirunavukkarasu G, Horan B, Mekhilef S, Stojcevski A (2018) Maximum power point tracking for photovoltaic systems under partial shading conditions using bat algorithm. Sustainability 10(5):1347. https://doi.org/10.3390/su10051347
Xu Y, Pi D (2019) A hybrid enhanced bat algorithm for the generalized redundancy allocation problem. Swarm Evol Comput 50:100562
Santana C, Oliveira M, Filho CB, Menezes R (2022) Beyond exploitation: measuring the impact of local search in swarm-based memetic algorithms through the interactions of individuals in the population. Swarm Evol Comput 70:101040
Amalo K, Birninkudu S, Bukata B, Salawudeen A, Ahmad A (2020) Cultured bat algorithm for optimized MPPT tracking under different shading conditions. In: 2020 international conference in mathematics, computer engineering and computer science (ICMCECS). https://doi.org/10.1109/icmcecs47690.2020.246985
Pan Z, Quynh N, Ali Z, Dadfar S, Kashiwagi T (2020) Enhancement of maximum power point tracking technique based on PV-Battery system using hybrid BAT algorithm and fuzzy controller. J Clean Prod 274:123719. https://doi.org/10.1016/j.jclepro.2020.123719
Sarkar R, Kumar JR, Sridhar R et al (2021) A New hybrid BAT-ANFIS-based power tracking technique for partial shaded photovoltaic systems. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-020-01037-y
Wu Z, Yu D (2018) Application of improved bat algorithm for solar PV maximum power point tracking under partially shaded condition. Appl Soft Comput 62:101–109. https://doi.org/10.1016/j.asoc.2017.10.039
Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734
Shams I, Mekhilef DS, Kok ST (2020) Maximum power point tracking using modified butterfly optimization algorithm for partial shading, uniform shading and fast varying load conditions. IEEE Trans Power Electron. https://doi.org/10.1109/tpel.2020.3029607
Zhi Y, Wang W, Wang H, Khodaei H (2020) Improved butterfly optimization algorithm for CCHP driven by PEMFC. Appl Therm Eng 173:114766. https://doi.org/10.1016/j.applthermaleng.2019.114766
Mortazavi A, Moloodpoor M (2021) Enhanced butterfly optimization algorithm with a new fuzzy regulator strategy and virtual butterfly concept. Knowl-Based Syst 228:107291. https://doi.org/10.1016/j.knosys.2021.107291
Fan Y, Shao J, Sun G, Shao X (2020) A self-adaptation butterfly optimization algorithm for numerical optimization problems. IEEE Access 8:88026–88041. https://doi.org/10.1109/ACCESS.2020.2993148
Dhanya KM, Kanmani S (2019) Mutated butterfly optimization algorithm. Int J Eng Adv Technol 8(3):375–381
Sharma TK (2021) Enhanced butterfly optimization algorithm for reliability optimization problems. J Ambient Intell Humaniz Comput 12:7595–7619. https://doi.org/10.1007/s12652-020-02481-2
Tubishat M, Alswaitti M, Mirjalili S, Al-Garadi MA, Alrashdan MT, Rana TA (2020) Dynamic butterfly optimization algorithm for feature selection. IEEE Access 8:194303–194314. https://doi.org/10.1109/ACCESS.2020.3033757
Long W, Wu T, Xu M, Tang M, Cai S (2021) Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm. Energy 229:120750. https://doi.org/10.1016/j.energy.2021.120750
Sharma S, Saha AK, Majumder A, Nama S (2021) MPBOA—a novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimed Tools Appl 80:12035–12076. https://doi.org/10.1007/s11042-020-10053-x
Zhang M, Wang D, Yang J (2022) Hybrid-flash butterfly optimization algorithm with logistics mapping for solving the engineering constrained optimization problems. Entropy 24(4):525. https://doi.org/10.3390/e24040525
Sharma S, Chakraborty S, Saha AK, Nama S, Sahoo SK (2022) mLBOA: a modified butterfly optimization algorithm with Lagrange interpolation for global optimization. J Bionic Eng. https://doi.org/10.1007/s42235-022-00175-3
Teshome DF, Lee CH, Lin YW, Lian KL (2017) A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading. IEEE J Emerg Sel Top Power Electron 5(2):661–671. https://doi.org/10.1109/jestpe.2016.2581858
Trachanatzi D, Rigakis M, Marinaki M, Marinakis Y (2020) A firefly algorithm for the environmental prize-collecting vehicle routing problem. Swarm Evol Comput 57:100712
Farzaneh J, Keypour R, Khanesar M (2018) A new maximum power point tracking based on modified firefly algorithm for PV system under partial shading conditions. Technol Econ Smart Grids Sustain Energy. https://doi.org/10.1007/s40866-018-0048-7
Huang Y, Chen X, Ye C (2018) A hybrid maximum power point tracking approach for photovoltaic systems under partial shading conditions using a modified genetic algorithm and the firefly algorithm. Int J Photoenergy 2018:1–13. https://doi.org/10.1155/2018/7598653
Abo-Khalil A, Alharbi W, Al-Qawasmi A, Alobaid M, Alarifi I (2021) Maximum power point tracking of PV systems under partial shading conditions based on opposition-based learning firefly algorithm. Sustainability 13(5):2656. https://doi.org/10.3390/su13052656
Zhang M, Chen Z, Wei L (2019) An immune firefly algorithm for tracking the maximum power point of PV array under partial shading conditions. Energies 12(16):3083. https://doi.org/10.3390/en12163083
Safarudin Y, Priyadi A, Purnomo M, Pujiantara M (2015) Combining Simplified Firefly and modified P& O algorithm for maximum power point tracking of photovoltaic system under partial shading condition. In: 2015 international seminar on intelligent technology and its applications (ISITIA). https://doi.org/10.1109/isitia.2015.7219976
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
Shi JY, Zhang DY, Xue F, Li YJ, Qiao W, Yang T (2019) Moth-Flame optimization-based maximum power point tracking for photovoltaic systems under partial shading conditions. J Power Electron 19(5):1248–1258
Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Sources Plan Manage 129(3):210–225
Zhang X, Kang Q, Wang X (2019) Hybrid biogeography-based optimization with shuffled frog leaping algorithm and its application to minimum spanning tree problems. Swarm Evol Comput 49:245–265
Sridhar R, Jeevananthan S, Dash SS, Vishnuram P (2016) A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm. J Exp Theor Artif Intell 29(3):481–493. https://doi.org/10.1080/0952813x.2016.1186750
Guo S, Abbassi R, Jerbi H, Rezvani A, Suzuki K (2021) Efficient maximum power point tracking for a photovoltaic using hybrid shuffled frog-leaping and pattern search algorithm under changing environmental conditions. J Clean Prod 297:126573. https://doi.org/10.1016/j.jclepro.2021.126573
Mohammadinodoushan M, Abbassi R, Jerbi H, Waly Ahmed F, Abdalqadir KH, Rezvani A (2021) A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm-SMC controller. Sustain Energy Technol Assess 45:101056. https://doi.org/10.1016/j.seta.2021.101056
Nie X, Nie H (2017) MPPT control strategy of PV based on improved shuffled frog leaping algorithm under complex environments. J Control Sci Eng 2017:1–11. https://doi.org/10.1155/2017/2186420
Aldosary A, Ali Z, Alhaider M, Ghahremani M, Dadfar S, Suzuki K (2021) A modified shuffled frog algorithm to improve MPPT controller in PV system with storage batteries under variable atmospheric conditions. Control Eng Pract 112:104831. https://doi.org/10.1016/j.conengprac.2021.104831
Krishnan GS, Sundareswaran K, Simon SP (2022) Increased energy harvesting from shaded PV power plant using a fast converging fruit fly algorithm. J Inst Eng (India) B. https://doi.org/10.1007/s40031-022-00725-7
Emad D, El-Hameed MA, Yousef MT, El-Fergany AA (2018) Computational methods for optimal planning of hybrid renewable microgrids: a comprehensive review and challenges. Arch Comput Methods Eng 27:1297–1319. https://doi.org/10.1007/s11831-019-09353-9
Gupta A, Tiwari D, Kumar V, Rana KPS, Mirjalili S (2022) A chaos-infused moth-flame optimizer. Arab J Sci Eng. https://doi.org/10.1007/s13369-022-06689-6