A Review of Partial Shading MPPT Algorithm on Speed, Accuracy, and Cost Embedded
Keywords:
Partial shading, MPPT algorithm, Speed, Accuracy, Cost embeddedAbstract
This paper describes several maximum power point tracking algorithms for partial shading conditions that have detrimental effects on photovoltaic systems. The method used was a literature review of articles from reputable publishers. Fifty-two articles were obtained after meeting the established criteria for selection. The literature review focused on the ability of the maximum power point tracking algorithms to overcome partial shading conditions in terms of tracking speed, tracking accuracy, efficiency and implementation complexity. Some algorithms were recommended to be applied for maximum power point tracking, including the single swam algorithm and the perturb and observe algorithm, the enhanced adaptive step size perturb and observe algorithm, the novel adaptable step incremental conductance algorithm, the improved bat algorithm and fuzzy logic controller algorithm and the particle swarm optimisation with one cycle control algorithm. In terms of implementation complexity, these five algorithms are categorised as medium-complexity algorithms, which can be characterised by low cost, high efficiency and even 100% high tracking speed and accuracy with a minimum number of sensors used.
Downloads
References
L. Bhukya and S. Nandiraju, “A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach,” Int. J. Hydrogen Energy, vol. 45, no. 16, pp. 9416–9427, 2020, doi: 10.1016/j.ijhydene.2020.01.219.
H. Karmouni, M. Chouiekh, S. Motahhir, H. Qjidaa, M. O. Ouazzani, and M. Sayyouri, “A fast and accurate sine-cosine MPPT algorithm under partial shading with implementation using arduino board,” Clean. Eng. Technol., vol. 9, no. 100535, pp. 1–13, 2022, doi: 10.1016/j.clet.2022.100535. DOI: https://doi.org/10.1016/j.clet.2022.100535
K. Itako and A. Alhabib, “A new method of detecting hot spots in PV generation system utilizing AI,” in IOP Conference Series: Earth and Environmental Science, 2020, vol. 581, no. 1, pp. 1–6. doi: 10.1088/1755-1315/581/1/012006. DOI: https://doi.org/10.1088/1755-1315/581/1/012006
J. C. Teo, R. H. G. Tan, V. H. Mok, V. K. Ramachandaramurthy, and C. Tan, “Impact of partial shading on the P-V characteristics and the maximum power of a photovoltaic string,” Energies, vol. 11, no. 7, 2018, doi: 10.3390/en11071860. DOI: https://doi.org/10.3390/en11071860
W. Li, G. Zhang, T. Pan, Z. Zhang, Y. Geng, and J. Wang, “A Lipschitz optimization-based MPPT algorithm for photovoltaic system under partial shading condition,” IEEE Access, vol. 7, pp. 126323–126333, 2019, doi: 10.1109/ACCESS.2019.2939095. DOI: https://doi.org/10.1109/ACCESS.2019.2939095
S. Padmanaban, N. Priyadarshi, M. S. Bhaskar, J. B. Holm-Nielsen, V. K. Ramachandaramurthy, and E. Hossain, “A hybrid ANFIS-ABC based MPPT controller for PV system with anti-islanding grid protection: experimental realization,” IEEE Access, vol. 7, pp. 103377–103389, 2019, doi: 10.1109/ACCESS.2019.2931547. DOI: https://doi.org/10.1109/ACCESS.2019.2931547
M. A. M. Ramli, S. Twaha, K. Ishaque, and Y. A. Al-Turki, “A review on maximum power point tracking for photovoltaic systems with and without shading conditions,” Renew. Sustain. Energy Rev., vol. 67, pp. 144–159, 2017, doi: 10.1016/j.rser.2016.09.013. DOI: https://doi.org/10.1016/j.rser.2016.09.013
S. Motahhir, A. El Hammoumi, and A. El Ghzizal, “The most used MPPT algorithms: review and the suitable low-cost embedded board for each algorithm,” J. Clean. Prod., vol. 246, no. 118983, pp. 1–17, Feb. 2020, doi: 10.1016/j.jclepro.2019.118983. DOI: https://doi.org/10.1016/j.jclepro.2019.118983
K. Ishaque and Z. Salam, “A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition,” Renew. Sustain. Energy Rev., vol. 19, pp. 475–488, 2013, doi: 10.1016/j.rser.2012.11.032. DOI: https://doi.org/10.1016/j.rser.2012.11.032
A. Hilali, Y. Mardoude, Y. Ben Akka, H. El Alami, and A. Rahali, “Design, modeling and simulation of perturb and observe maximum power point tracking for a photovoltaic water pumping system,” Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 3430–3439, 2022, doi: 10.11591/ijece.v12i4.pp3430-3439. DOI: https://doi.org/10.11591/ijece.v12i4.pp3430-3439
V. Jately, B. Azzopardi, J. Joshi, B. Venkateswaran V, A. Sharma, and S. Arora, “Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels,” Renew. Sustain. Energy Rev., vol. 150, no. 111467, pp. 1–16, 2021, doi: 10.1016/j.rser.2021.111467. DOI: https://doi.org/10.1016/j.rser.2021.111467
R. Alik, A. Jusoh, and T. Sutikno, “A Review on perturb and observe maximum power point tracking in photovoltaic system,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 13, no. 3, pp. 745–751, 2015, doi: 10.12928/telkomnika.v13i3.1439. DOI: https://doi.org/10.12928/telkomnika.v13i3.1439
C. H. Basha and C. Rani, “Different conventional and soft computing MPPT techniques for solar PV systems with high step-up boost converters: a comprehensive analysis,” Energies, vol. 13, no. 371, pp. 1–27, 2020, doi: 10.3390/en13020371. DOI: https://doi.org/10.3390/en13020371
M. Shixun, Y. Qintao, J. Kunping, M. Xiaofeng, and S. Gengyu, “An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm,” Energy Reports, vol. 8, pp. 141–150, 2022, doi: 10.1016/j.egyr.2022.02.160. DOI: https://doi.org/10.1016/j.egyr.2022.02.160
D. Pathak, G. Sagar, and P. Gaur, “An application of intelligent non-linear discrete-PID controller for MPPT of PV system,” in Procedia Computer Science, 2020, vol. 167, pp. 1574–1583. doi: 10.1016/j.procs.2020.03.368. DOI: https://doi.org/10.1016/j.procs.2020.03.368
S. Titri, C. Larbes, K. Y. Toumi, and K. Benatchba, “A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions,” Appl. Soft Comput. J., vol. 58, pp. 465–479, 2017, doi: 10.1016/j.asoc.2017.05.017. DOI: https://doi.org/10.1016/j.asoc.2017.05.017
R. K. Phanden, L. Sharma, J. Chhabra, and H. I. Demir, “A novel modified ant colony optimization based maximum power point tracking controller for photovoltaic systems,” Mater. Today Proc., vol. 38, pp. 1–5, 2020, doi: 10.1016/j.matpr.2020.06.020. DOI: https://doi.org/10.1016/j.matpr.2020.06.020
L. Mohammad, E. Prasetyono, and F. D. Murdianto, “Performance Evaluation of ACO-MPPT and constant voltage method for street lighting charging system,” in nternational Seminar on Application for Technology of Information and Communication: Industry 4.0: Retrospect, Prospect, and Challenges, iSemantic 2019, 2019, pp. 411–416. doi: 10.1109/ISEMANTIC.2019.8884303. DOI: https://doi.org/10.1109/ISEMANTIC.2019.8884303
S. K. Sahoo, M. Balamurugan, S. Anurag, R. Kumar, and V. Priya, “Maximum power point tracking for PV panels using ant colony optimization,” in International Conference on Innovations in Power and Advanced Computing Technologies [i-PACT2017], 2017, pp. 1–4. doi: 10.1109/IPACT.2017.8245004. DOI: https://doi.org/10.1109/IPACT.2017.8245004
S. Mohanty, B. Subudhi, and P. K. Ray, “A grey wolf-assisted perturb & observe MPPT algorithm for a PV system,” IEEE Trans. Energy Convers., vol. 32, no. 1, pp. 340–347, 2017, doi: 10.1109/TEC.2016.2633722. DOI: https://doi.org/10.1109/TEC.2016.2633722
K. Guo, L. Cui, M. Mao, L. Zhou, and Q. Zhang, “An improved gray wolf optimizer MPPT algorithm for PV system with BFBIC converter under partial shading,” IEEE Access, vol. 8, pp. 103476–103490, 2020, doi: 10.1109/ACCESS.2020.2999311. DOI: https://doi.org/10.1109/ACCESS.2020.2999311
C. S. Kumar and R. S. Rao, “Enhanced grey wolf optimizer based MPPT algorithm of PV system under partial shaded condition,” Int. J. Renew. Energy Dev., vol. 6, no. 3, pp. 203–212, 2017, doi: 10.14710/ijred.6.3.203-212. DOI: https://doi.org/10.14710/ijred.6.3.203-212
S. Mohanty, B. Subudhi, and P. K. Ray, “A new MPPT design using grey Wolf optimization technique for photovoltaic system under partial shading conditions,” IEEE Trans. Sustain. Energy, vol. 7, no. 1, pp. 181–188, 2016, doi: 10.1109/TSTE.2015.2482120. DOI: https://doi.org/10.1109/TSTE.2015.2482120
K. Atici, I. Sefa, and N. Altin, “Grey wolf pptimization based MPPT algorithm for solar PV system with SEPIC converter,” in 4th International Conference on Power Electronics and their Applications, ICPEA 2019, 2019, pp. 1–6. doi: 10.1109/ICPEA1.2019.8911159. DOI: https://doi.org/10.1109/ICPEA1.2019.8911159
C. Gonzalez-Castano, C. Restrepo, S. Kouro, and J. Rodriguez, “MPPT algorithm based on artificial bee colony for PV system,” IEEE Access, vol. 9, pp. 43121–43133, 2021, doi: 10.1109/ACCESS.2021.3066281. DOI: https://doi.org/10.1109/ACCESS.2021.3066281
L. Fan and X. Ma, “Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control,” Sci. Rep., vol. 12, no. 4316, pp. 1–12, 2022, doi: 10.1038/s41598-022-08327-5. DOI: https://doi.org/10.1038/s41598-022-08327-5
H. Salmi, A. Badri, and M. Zegrari, “Maximum Power Point Tracking (MPPT) using artificial bee colony based algorithm for photovoltaic system,” Int. J. Intell. Inf. Syst., vol. 5, no. 1, pp. 1–4, 2016, doi: 10.11648/j.ijiis.20160501.11. DOI: https://doi.org/10.11648/j.ijiis.20160501.11
D. Pilakkat and S. Kanthalakshmi, “Single phase PV system operating under partially shaded conditions with ABC-PO as MPPT algorithm for grid connected applications,” Energy Reports, vol. 6, pp. 1910–1921, 2020, doi: 10.1016/j.egyr.2020.07.019. DOI: https://doi.org/10.1016/j.egyr.2020.07.019
J. S. Goud, R. Kalpana, B. Singh, and S. Kumar, “Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array,” IET Renew. Power Gener., vol. 12, no. 16, pp. 1915–1922, 2018, doi: 10.1049/iet-rpg.2018.5116. DOI: https://doi.org/10.1049/iet-rpg.2018.5116
B. Nagarani and J. Nesa Mony, “Performance enhancement of photovoltaic system using genetic algorithm- based maximum power point tracking,” Turkish J. Electr. Eng. Comput. Sci., vol. 27, no. 4, pp. 3015–3025, 2019, doi: 10.3906/elk-1801-189. DOI: https://doi.org/10.3906/elk-1801-189
C. T. Lee, H. I. Tsou, T. H. Chou, and K. W. Weng, “Application of the hybrid taguchi genetic algorithm to maximum power point tracking of photovoltaic system,” in Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, 2018, pp. 231–234. doi: 10.1109/ICASI.2018.8394575. DOI: https://doi.org/10.1109/ICASI.2018.8394575
S. Hadji, J. P. Gaubert, and F. Krim, “Real-time genetic algorithms-based MPPT: study and comparison (theoretical an experimental) with conventional methods,” Energies, vol. 11, no. 2, pp. 1–17, 2018, doi: 10.3390/en11020459. DOI: https://doi.org/10.3390/en11020459
S. Obukhov, A. Ibrahim, A. A. Zaki Diab, A. S. Al-Sumaiti, and R. Aboelsaud, “Optimal performance of dynamic particle swarm optimization dased maximum power trackers for stand-alone PV system under partial shading conditions,” IEEE Access, vol. 8, pp. 20770–20785, 2020, doi: 10.1109/ACCESS.2020.2966430. DOI: https://doi.org/10.1109/ACCESS.2020.2966430
G. Dileep and S. N. Singh, “An improved particle swarm optimization based maximum power point tracking algorithm for PV system operating under partial shading conditions,” Sol. Energy, vol. 158, no. October, pp. 1006–1015, 2017, doi: 10.1016/j.solener.2017.10.027. DOI: https://doi.org/10.1016/j.solener.2017.10.027
R. Venugopalan, N. Krishnakumar, T. Sudhakarbabu, K. Sangeetha, and N. Rajasekar, “Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition,” Appl. Soft Comput. J., vol. 34, pp. 613–624, 2015, doi: 10.1016/j.asoc.2015.05.029. DOI: https://doi.org/10.1016/j.asoc.2015.05.029
Y. Soufi, M. Bechouat, and S. Kahla, “Particle swarm optimization based maximum power point tracking algorithm for photovoltaic energy conversion system,” in 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018, 2018, pp. 773–779. doi: 10.1109/SSD.2018.8570633. DOI: https://doi.org/10.1109/SSD.2018.8570633
H. Li, D. Yang, W. Su, J. Lu, and X. Yu, “An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading,” IEEE Trans. Ind. Electron., vol. 66, no. 1, pp. 265–275, 2019, doi: 10.1109/TIE.2018.2829668. DOI: https://doi.org/10.1109/TIE.2018.2829668
K. Y. Yap, C. R. Sarimuthu, and J. M. Y. Lim, “Artificial intelligence based MPPT techniques for solar power system: a review,” J. Mod. Power Syst. Clean Energy, vol. 8, no. 6, pp. 1043–1059, 2020, doi: 10.35833/MPCE.2020.000159. DOI: https://doi.org/10.35833/MPCE.2020.000159
E. Kandemir, S. Borekci, and N. S. Cetin, “Comparative analysis of reduced-rule compressed fuzzy logic control and incremental conductance MPPT methods,” J. Electron. Mater., vol. 47, no. 8, pp. 4463–4474, 2018, doi: 10.1007/s11664-018-6273-y. DOI: https://doi.org/10.1007/s11664-018-6273-y
N. Bouarroudj et al., “Fuzzy logic controller based maximum power point tracking and its optimal tuning in photovoltaic systems,” Serbian J. Electr. Eng., vol. 18, no. 3, pp. 351–384, 2021, doi: 10.2298/SJEE2103351B. DOI: https://doi.org/10.2298/SJEE2103351B
S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, “Design of an intelligent MPPT based on ANN using a real photovoltaic system data,” in 2019 54th International Universities Power Engineering Conference, UPEC 2019 - Proceedings, 2019, pp. 1–6. doi: 10.1109/UPEC.2019.8893638. DOI: https://doi.org/10.1109/UPEC.2019.8893638
C. G. Villegas-Mier, J. Rodriguez-Resendiz, J. M. Álvarez-Alvarado, H. Rodriguez-Resendiz, A. M. Herrera-Navarro, and O. Rodríguez-Abreo, “Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: a review,” Micromachines, vol. 12, pp. 1–19, 2021, doi: 10.3390/mi12101260. DOI: https://doi.org/10.3390/mi12101260
S. Messalti, A. Harrag, and A. Loukriz, “A new variable step size neural networks MPPT controller: review, simulation and hardware implementation,” Renew. Sustain. Energy Rev., vol. 68, pp. 221–233, 2017, doi: 10.1016/j.rser.2016.09.131. DOI: https://doi.org/10.1016/j.rser.2016.09.131
R. Divyasharon, N. R. Banu, and D. Devaraj, “Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions,” in 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2019, pp. 1–6. doi: 10.1109/INCOS45849.2019.8951321. DOI: https://doi.org/10.1109/INCOS45849.2019.8951321
F. Belhachat and C. Larbes, “Comprehensive review on global maximum power point tracking techniques for PV systems subjected to partial shading conditions,” Sol. Energy, vol. 183, pp. 476–500, 2019, doi: 10.1016/j.solener.2019.03.045. DOI: https://doi.org/10.1016/j.solener.2019.03.045
M. Mansoor, A. F. Mirza, Q. Ling, and M. Y. Javed, “Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions,” Sol. Energy, vol. 198, no. October 2019, pp. 499–518, 2020, doi: 10.1016/j.solener.2020.01.070. DOI: https://doi.org/10.1016/j.solener.2020.01.070
M. G. Batarseh and M. E. Za’ter, “Hybrid maximum power point tracking techniques: A comparative survey, suggested classification and uninvestigated combinations,” Sol. Energy, vol. 169, no. December 2017, pp. 535–555, 2018, doi: 10.1016/j.solener.2018.04.045. DOI: https://doi.org/10.1016/j.solener.2018.04.045
M. C. Cavalcanti, F. Bradaschia, A. J. Do Nascimento, G. M. S. Azevedo, and E. J. Barbosa, “Hybrid maximum power point tracking technique for PV modules based on a double-diode model,” IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8169–8181, 2020, doi: 10.1109/TIE.2020.3009592. DOI: https://doi.org/10.1109/TIE.2020.3009592
D. Pilakkat and S. Kanthalakshmi, “An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions,” Sol. Energy, vol. 178, pp. 37–47, 2019, doi: 10.1016/j.solener.2018.12.008. DOI: https://doi.org/10.1016/j.solener.2018.12.008
V. Balaji and A. Peer Fathima, “Hybrid algorithm for MPPT tracking using a single current sensor for partially shaded PV systems,” Sustain. Energy Technol. Assessments, vol. 53, pp. 1–15, 2022, doi: 10.1016/j.seta.2022.102415. DOI: https://doi.org/10.1016/j.seta.2022.102415
M. Premkumar, C. Kumar, R. Sowmya, and J. Pradeep, “A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems,” Automatika, vol. 62, no. 1, pp. 1–20, 2020, doi: 10.1080/00051144.2020.1834062. DOI: https://doi.org/10.1080/00051144.2020.1834062
S. Jana, N. Kumar, R. Mishra, D. Sen, and T. K. Saha, “Development and implementation of modified MPPT algorithm for boost converter‐based PV system under input and load deviation,” Int. Trans. Electr. Energy Syst., vol. 30, no. 2, pp. 1–15, 2019, doi: 10.1002/2050-7038.12190. DOI: https://doi.org/10.1002/2050-7038.12190
N. M. M. Altwallbah, M. A. M. Radzi, N. Azis, S. Shafie, and M. A. A. M. Zainuri, “New perturb and observe algorithm based on trapezoidal rule: Uniform and partial shading conditions,” Energy Convers. Manag., vol. 264, pp. 1–23, 2022, doi: 10.1016/j.enconman.2022.115738. DOI: https://doi.org/10.1016/j.enconman.2022.115738
A. N. M. Mohammad, M. A. M. Radzi, N. Azis, S. Shafie, and M. A. A. M. Zainuri, “An enhanced adaptive perturb and observe technique for effcient maximum power point tracking under partial shading conditions,” Appl. Sci., vol. 10, pp. 1–29, 2020, doi: 10.3390/app10113912. DOI: https://doi.org/10.3390/app10113912
J. Ahmed and Z. Salam, “An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions,” IEEE Trans. Sustain. Energy, vol. 9, no. 3, pp. 1487–1496, 2018, doi: 10.1109/TSTE.2018.2791968. DOI: https://doi.org/10.1109/TSTE.2018.2791968
R. Alik and A. Jusoh, “An enhanced P&O checking algorithm MPPT for high tracking efficiency of partially shaded PV module,” Sol. Energy, vol. 163, pp. 570–580, 2018, doi: 10.1016/j.solener.2017.12.050. DOI: https://doi.org/10.1016/j.solener.2017.12.050
A. I. M. Ali and H. R. A. Mohamed, “Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation,” Int. J. Electr. Power Energy Syst., vol. 137, pp. 1–12, 2022, doi: 10.1016/j.ijepes.2021.107805. DOI: https://doi.org/10.1016/j.ijepes.2021.107805
K. Bataineh, “Improved hybrid algorithms-based MPPT algorithm for PV system operating under severe weather conditions,” IET Power Electron., vol. 12, no. 4, pp. 703–711, 2019. DOI: https://doi.org/10.1049/iet-pel.2018.5651
A. Yasmine, B. Rafik, B. Rachid, and M. Adel, “Grid connected photovoltaic system efficiency and quality improvement using fuzzy-inCond MPPT,” Int. J. Power Electron. Drive Syst., vol. 11, no. 3, pp. 1536–1546, 2020, doi: 10.11591/ijpeds.v11.i3.pp1536-1546. DOI: https://doi.org/10.11591/ijpeds.v11.i3.pp1536-1546
M. Bouksaim, M. Mekhfioui, and M. N. Srifi, “Design and implementation of modified INC, conventional INC, and Fuzzy Logic Controllers applied to a pv system under variable weather conditions,” Designs, vol. 5, no. 71, pp. 1–26, 2021, doi: 10.3390/designs5040071. DOI: https://doi.org/10.3390/designs5040071
S. Motahhir, A. El Ghzizal, S. Sebti, and A. Derouich, “Modeling of photovoltaic system with modified Incremental Conductance Algorithm for fast changes of irradiance,” Int. J. Photoenergy, vol. 2018, pp. 1–14, 2018, doi: 10.1155/2018/3286479. DOI: https://doi.org/10.1155/2018/3286479
S. Motahhir, A. El Hammoumi, and A. El Ghzizal, “Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation,” Energy Reports, vol. 4, pp. 341–350, 2018, doi: 10.1016/j.egyr.2018.04.003. DOI: https://doi.org/10.1016/j.egyr.2018.04.003
L. Xu, R. Cheng, and J. Yang, “A new MPPT technique for fast and efficient tracking under fast varying solar irradiation and load resistance,” Int. J. Photoenergy, vol. 2020, pp. 1–18, 2020, doi: 10.1155/2020/6535372. DOI: https://doi.org/10.1155/2020/6535372
S. J. Zand, K. Hsia, N. Eskandarian, and S. Mobayen, “Improvement of self-predictive Incremental Conductance,” Energies, vol. 14, no. 1234, pp. 1–14, 2021. DOI: https://doi.org/10.3390/en14051234
B. H. Wijaya, R. K. Subroto, K. L. Lian, and N. Hariyanto, “A maximum power point tracking method based on a modified grasshopper algorithm combined with incremental conductance,” Energies, vol. 13, no. 4329, pp. 1–18, 2020, doi: 10.3390/en13174329. DOI: https://doi.org/10.3390/en13174329
S. Sarwar et al., “A Novel hybrid MPPT technique to maximize power harvesting from PV system under partial and complex partial shading,” Appl. Sci., vol. 12, no. 587, pp. 1–26, 2022, doi: 10.3390/app12020587. DOI: https://doi.org/10.3390/app12020587
A. Chellakhi, S. El Beid, Y. Abouelmahjoub, and Y. Mchaouar, “Optimization of power extracting from photovoltaic systems based on a novel adaptable step INC MPPT approach,” ScienceDirect, vol. 55, no. 12, pp. 508–513, 2022, doi: 10.1016/j.ifacol.2022.07.362. DOI: https://doi.org/10.1016/j.ifacol.2022.07.362
M. H. Parvaneh and P. G. Khorasani, “A new hybrid method based on Fuzzy Logic for maximum power point tracking of photovoltaic systems,” Energy Reports, vol. 6, pp. 1619–1632, 2020, doi: 10.1016/j.egyr.2020.06.010. DOI: https://doi.org/10.1016/j.egyr.2020.06.010
Z. M. Ali, T. Alquthami, S. Alkhalaf, H. Norouzi, S. Dadfar, and K. Suzuki, “Novel hybrid improved bat algorithm and fuzzy system based MPPT for photovoltaic under variable atmospheric conditions,” Sustain. Energy Technol. Assessments, vol. 52, pp. 1–13, 2022, doi: 10.1016/j.seta.2022.102156. DOI: https://doi.org/10.1016/j.seta.2022.102156
B. Laxman, A. Annamraju, and N. V. Srikanth, “A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids,” Int. J. Hydrogen Energy, vol. 46, pp. 10653–10665, 2021, doi: 10.1016/j.ijhydene.2020.12.158. DOI: https://doi.org/10.1016/j.ijhydene.2020.12.158
Z. Hu, H. Norouzi, M. Jiang, S. Dadfar, and T. Kashiwagi, “Novel hybrid modified krill herd algorithm and fuzzy controller based MPPT to optimally tune the member functions for PV system in the three-phase grid-connected mode,” ISA Trans., vol. 129, pp. 214–229, 2022, doi: 10.1016/j.isatra.2022.02.009. DOI: https://doi.org/10.1016/j.isatra.2022.02.009
A. S. Mahdi, A. K. Mahamad, S. Saon, T. Tuwoso, H. Elmunsyah, and S. W. Mudjanarko, “Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS,” SN Appl. Sci., vol. 2, no. 1, pp. 1–9, 2020, doi: 10.1007/s42452-019-1886-1. DOI: https://doi.org/10.1007/s42452-019-1886-1
U. Yilmaz, O. Turksoy, and A. Teke, “Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions,” Int. J. Electr. Power Energy Syst., vol. 113, pp. 634–651, 2019, doi: 10.1016/j.ijepes.2019.05.074. DOI: https://doi.org/10.1016/j.ijepes.2019.05.074
B. M. Kiran Kumar, M. S. Indira, and S. Nagaraja Rao, “Performance analysis of multiple gain boost converter with hybrid maximum power point tracker for solar PV connected to grid,” Clean Energy, vol. 5, no. 4, pp. 655–672, 2021, doi: 10.1093/ce/zkab037. DOI: https://doi.org/10.1093/ce/zkab037
K. Bataineh and N. Eid, “A hybrid maximum power point tracking method for photovoltaic systems for dynamic weather conditions,” Resources, vol. 7, no. 68, pp. 1–16, 2018, doi: 10.3390/resources7040068. DOI: https://doi.org/10.3390/resources7040068
A. M. Eltamaly and H. M. H. Farh, “Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC,” Sol. Energy, vol. 177, pp. 306–316, 2019, doi: 10.1016/j.solener.2018.11.028. DOI: https://doi.org/10.1016/j.solener.2018.11.028
L. Bhukya and S. Nandiraju, “A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach,” Int. J. Hydrogen Energy, vol. 45, pp. 9416–9427, 2020, doi: 10.1016/j.ijhydene.2020.01.219. DOI: https://doi.org/10.1016/j.ijhydene.2020.01.219
C. Charin, D. Ishak, M. A. A. Mohd Zainuri, B. Ismail, and M. K. Mohd Jamil, “A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions,” Sol. Energy, vol. 217, pp. 1–14, 2021, doi: 10.1016/j.solener.2021.01.049. DOI: https://doi.org/10.1016/j.solener.2021.01.049
A. Sharma, A. Sharma, V. Jately, M. Averbukh, S. Rajput, and B. Azzopardi, “A novel TSA-PSO based hybrid algorithm for GMPP tracking under partial shading conditions,” Energies, vol. 15, no. 3164, pp. 1–21, 2022, doi: 10.3390/en15093164. DOI: https://doi.org/10.3390/en15093164
D. M. Djanssou, A. Dadjé, A. Tom, and N. Djongyang, “Improvement of the dynamic response of robust sliding mode MPPT controller-based PSO algorithm for PV systems under fast-changing atmospheric conditions,” Int. J. Photoenergy, vol. 2021, pp. 1–13, 2021, doi: 10.1155/2021/6671133. DOI: https://doi.org/10.1155/2021/6671133
H. M. H. Farh, A. M. Eltamaly, and M. F. Othman, “Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system,” PLoS One, vol. 13, no. 11, pp. 1–16, 2018, doi: 10.1371/journal.pone.0206171. DOI: https://doi.org/10.1371/journal.pone.0206171
I. Dagal, B. Akın, and E. Akboy, “MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink,” Sci. Rep., vol. 12, pp. 1–17, 2022, doi: 10.1038/s41598-022-06609-6. DOI: https://doi.org/10.1038/s41598-022-06609-6
S. Chtita et al., “A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions,” Sci. Rep., vol. 12, pp. 1–15, 2022, doi: 10.1038/s41598-022-14733-6. DOI: https://doi.org/10.1038/s41598-022-14733-6
I. Dagal, B. Akın, and E. Akboy, “A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications,” Ain Shams Eng. J., vol. 13, no. 5, pp. 1–13, 2022, doi: 10.1016/j.asej.2022.101747. DOI: https://doi.org/10.1016/j.asej.2022.101747
D. K. Mathi and R. Chinthamalla, “A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions,” Int. Trans. Electr. Energy Syst., vol. 30, no. 10, pp. 1–25, 2020, doi: 10.1002/2050-7038.12543. DOI: https://doi.org/10.1002/2050-7038.12543
W. Zhang, G. Zhou, H. Ni, and Y. Sun, “A modified hybrid maximum power point tracking method for photovoltaic arrays under partially shading condition,” IEEE Access, vol. 7, pp. 160091–160100, 2019, doi: 10.1109/ACCESS.2019.2950375. DOI: https://doi.org/10.1109/ACCESS.2019.2950375
Y. E. A. Idrissi, K. Assalaou, L. Elmahni, and E. Aitiaz, “New improved MPPT based on artificial neural network and PI controller for photovoltaic applications,” Int. J. Power Electron. Drive Syst., vol. 13, no. 3, pp. 1791–1801, 2022, doi: 10.11591/ijpeds.v13.i3.pp1791-1801. DOI: https://doi.org/10.11591/ijpeds.v13.i3.pp1791-1801
K. Fatima, M. A. Alam, and A. F. Minai, “Optimization of solar energy using ANN techniques,” in 2019 2nd International Conference on Power Energy Environment and Intelligent Control, PEEIC 2019, 2019, pp. 174–179. doi: 10.1109/PEEIC47157.2019.8976854. DOI: https://doi.org/10.1109/PEEIC47157.2019.8976854
M. Jiang, M. Ghahremani, S. Dadfar, H. Chi, Y. N. Abdallah, and N. Furukawa, “A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system,” Control Eng. Pract., vol. 114, pp. 1–18, 2021, doi: 10.1016/j.conengprac.2021.104880. DOI: https://doi.org/10.1016/j.conengprac.2021.104880
M. T. Mitsuya and A. A. de Moura Meneses, “Efficiency of hybrid MPPT techniques based on ANN and PSO for photovoltaic systems under partially shading conditions,” Am. J. Eng. Appl. Sci., vol. 12, no. 4, pp. 460–471, 2019, doi: 10.3844/ajeassp.2019.460.471. DOI: https://doi.org/10.3844/ajeassp.2019.460.471
N. Priyadarshi, S. Padmanaban, J. B. Holm-Nielsen, F. Blaabjerg, and M. S. Bhaskar, “An experimental estimation of hybrid ANFIS-PSO-based MPPT for PV grid integration under fluctuating sun irradiance,” IEEE Syst. J., vol. 14, no. 1, pp. 1–12, 2020, doi: 10.1109/JSYST.2019.2949083. DOI: https://doi.org/10.1109/JSYST.2019.2949083
E. M. Ahmed, H. Norouzi, S. Alkhalaf, Z. M. Ali, S. Dadfar, and N. Furukawa, “Enhancement of MPPT controller in PV-BES system using incremental conductance along with hybrid crow-pattern search approach based ANFIS under different environmental conditions,” Sustain. Energy Technol. Assessments, vol. 50, pp. 1–16, 2022, doi: 10.1016/j.seta.2021.101812. DOI: https://doi.org/10.1016/j.seta.2021.101812
S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, “Design of an efficient maximum power point tracker based on ANFIS using an experimental photovoltaic system data,” Electronics, vol. 8, no. 858, pp. 1–20, 2019, doi: 10.3390/electronics8080858. DOI: https://doi.org/10.3390/electronics8080858
A. A. Koochaksaraei and H. Izadfar, “High-Efficiency MPPT ccontroller using ANFIS-reference model for solar systems,” in 2019 IEEE 5th Conference on Knowledge Based Engineering and Innovation, KBEI 2019, 2019, pp. 770–775. doi: 10.1109/KBEI.2019.8734965. DOI: https://doi.org/10.1109/KBEI.2019.8734965
Published
How to Cite
Issue
Section
Copyright (c) 2023 Asnil Asnil, Refdinal Nazir, Krismadinata Krismadinata, Muhammad Nasir Sonni
This work is licensed under a Creative Commons Attribution 4.0 International License.