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Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

DOI: 10.4236/epe.2024.161002, PP. 21-42

Keywords: Photovoltaic, PV, Global Maximum Power Point Tracking, GMPPT, Fast Varying Partial Shading Conditions, Autonomous PV Systems, GMPPT Review

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Abstract:

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

References

[1]  Markets and Markets. Photovoltaics (PV) Market Size Share Trends & Growth Drivers Forecast, 2030.
https://www.marketsandmarkets.com/Market-Reports/building-integrated-photovoltaic-market-428.html
[2]  ECONECT—Ecosystèmes connectés Sentinelles de l’Environnement.
https://econect.cnrs.fr/
[3]  The Sun Trip. Solar Bike Adventures—Since 2013.
https://www.thesuntrip.com/
[4]  Alonsogarcia, M., Ruiz, J. and Chenlo, F. (2006) Experimental Study of Mismatch and Shading Effects in the I-V Characteristic of a Photovoltaic Module. Solar Energy Materials and Solar Cells, 90, 329-340.
https://doi.org/10.1016/j.solmat.2005.04.022
[5]  Ahmed, J. and Salam, Z. (2016) A Modified P&O Maximum Power Point Tracking Method with Reduced Steady-State Oscillation and Improved Tracking Efficiency. IEEE Transactions on Sustainable Energy, 7, 1506-1515.
https://doi.org/10.1109/TSTE.2016.2568043
[6]  Killi, M. and Samanta, S. (2015) Modified Perturb and Observe MPPT Algorithm for Drift Avoidance in Photovoltaic Systems. IEEE Transactions on Industrial Electronics, 62, 5549-5559.
https://doi.org/10.1109/TIE.2015.2407854
[7]  Scarpa, V.V.R., Buso, S. and Spiazzi, G. (2009) Low-Complexity MPPT Technique Exploiting the PV Module MPP Locus Characterization. IEEE Transactions on Industrial Electronics, 56, 1531-1538.
https://doi.org/10.1109/TIE.2008.2009618
[8]  Sera, D., Teodorescu, R., Hantschel, J. and Knoll, M. (2008) Optimized Maximum Power Point Tracker for Fast-Changing Environmental Conditions. IEEE Transactions on Industrial Electronics, 55, 2629-2637.
https://doi.org/10.1109/TIE.2008.924036
[9]  Hussein, K., Muta, I., Hoshino, T. and Osakada, M. (1995) Maximum Photovoltaic Power Tracking: An Algorithm for Rapidly Changing Atmospheric Conditions. IEEE Proceedings—Generation, Transmission and Distribution, 142, 59-64.
https://doi.org/10.1049/ip-gtd:19951577
[10]  Liu, F., Duan, S., Liu, F., Liu, B. and Kang, Y. (2008) A Variable Step Size INC MPPT Method for PV Systems. IEEE Transactions on Industrial Electronics, 55, 2622-2628.
https://doi.org/10.1109/TIE.2008.920550
[11]  Hsieh, G.C., Hsieh, H.I., Tsai, C.Y. and Wang, C.H. (2013) Photovoltaic Power-Increment-Aided Incremental-Conductance MPPT With Two-Phased Tracking. IEEE Transactions on Power Electronics, 28, 2895-2911.
https://doi.org/10.1109/TPEL.2012.2227279
[12]  Villegas-Mier, C.G., Rodriguez-Resendiz, J., álvarez-Alvarado, J.M., Rodriguez-Resendiz, H., Herrera-Navarro, A.M. and Rodríguez-Abreo, O. (2021) Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review. Micromachines, 12, Article 1260.
https://doi.org/10.3390/mi12101260
[13]  Rahman, M.M. and Islam, M.S. (2019) Artificial Neural Network Based Maximum Power Point Tracking of a Photovoltaic System. 2019 3rd International Conference on Electrical, Computer Telecommunication Engineering (ICECTE), Rajshah, 26-28 December 2019, 117-120.
https://doi.org/10.1109/ICECTE48615.2019.9303531
[14]  Jyothy, L.P.N. and Sindhu, M.R. (2018) An Artificial Neural Network Based MPPT Algorithm For Solar PV System. 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, 7-9 February 2018, 375-380.
https://doi.org/10.1109/ICEES.2018.8443277
[15]  Agha, H.S., Koreshi, Z. and Khan, M.B. (2017) Artificial Neural Network Based Maximum Power Point Tracking for Solar Photovoltaics. 2017 International Conference on Information and Communication Technologies (ICICT), Karachi, 30-31 December 2017, 150-155.
https://doi.org/10.1109/ICICT.2017.8320180
[16]  Rizzo, S.A. and Scelba, G. (2015) ANN Based MPPT Method for Rapidly Variable Shading Conditions. Applied Energy, 145, 124-132.
https://doi.org/10.1016/j.apenergy.2015.01.077
[17]  Elobaid, L.M., Abdelsalam, A.K. and Zakzouk, E.E. (2012) Artificial Neural Network Based Maximum Power Point Tracking Technique for PV Systems. IECON 2012—38th Annual Conference on IEEE Industrial Electronics Society, Montreal, 25-28 October 2012, 937-942.
https://doi.org/10.1109/IECON.2012.6389165
[18]  Dzung, P.Q., Khoa, L.D., Lee, H.H., Phuong, L.M. and Vu, N.T.D. (2010) The New MPPT Algorithm Using ANN-Based PV. International Forum on Strategic Technology 2010, Ulsan, 13-15 October 2010, 402-407.
https://doi.org/10.1109/IFOST.2010.5668004
[19]  Ali, M.N., Mahmoud, K., Lehtonen, M. and Darwish, M.M.F. (2021) Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic. Sensors, 21, Article 1244.
https://doi.org/10.3390/s21041244
[20]  Li, X., Wen, H., Hu, Y. and Jiang, L. (2019) A Novel β Parameter Based Fuzzy-Logic Controller for Photovoltaic MPPT Application. Renewable Energy, 130, 416-427.
https://doi.org/10.1016/j.renene.2018.06.071
[21]  Yilmaz, U., Kircay, A. and Borekci, S. (2018) PV System Fuzzy Logic MPPT Method and PI Control as a Charge Controller. Renewable and Sustainable Energy Reviews, 81, 994-1001.
https://doi.org/10.1016/j.rser.2017.08.048
[22]  Kumar, R., Kumar, B. and Swaroop, D. (2018) Fuzzy Logic Based Improved P&O MPPT Technique for Partial Shading Conditions. 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, 28-29 September 2018, 775-779.
https://doi.org/10.1109/GUCON.2018.8674917
[23]  El Khateb, A., Rahim, N.A., Selvaraj, J. and Uddin, M.N. (2014) Fuzzy-Logic-Controller-Based SEPIC Converter for Maximum Power Point Tracking. IEEE Transactions on Industry Applications, 50, 2349-2358.
https://doi.org/10.1109/TIA.2014.2298558
[24]  Alajmi, B.N., Ahmed, K.H., Finney, S.J. and Williams, B.W. (2011) Fuzzy-Logic-Control Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System. IEEE Transactions on Power Electronics, 26, 1022-1030.
https://doi.org/10.1109/TPEL.2010.2090903
[25]  Farivar, G., Asaei, B. and Mehrnami, S. (2013) An Analytical Solution for Tracking Photovoltaic Module MPP. IEEE Journal of Photovoltaics, 3, 1053-1061.
https://doi.org/10.1109/JPHOTOV.2013.2250332
[26]  Moradi, M.H., Reza Tousi, S.M., Nemati, M., Saadat Basir, N. and Shalavi, N. (2013) A Robust Hybrid Method for Maximum Power Point Tracking in Photovoltaic Systems. Solar Energy, 94, 266-276.
https://doi.org/10.1016/j.solener.2013.05.016
[27]  Deboucha, H., Kermadi, M., Mekhilef, S. and Belaid, S.L. (2023) Ultra-Fast and Accurate MPPT Control Structure for Mobile PV System under Fast-Changing Atmospheric Conditions. IEEE Transactions on Sustainable Energy, 14, 2168-2176.
https://doi.org/10.1109/TSTE.2023.3260031
[28]  Shah, N. and Rajagopalan, C. (2016) Experimental Evaluation of a Partially Shaded Photovoltaic System with a Fuzzy Logic-Based Peak Power Tracking Control Strategy. IET Renewable Power Generation, 10, 98-107.
https://doi.org/10.1049/iet-rpg.2015.0098
[29]  Zhou, G., Bi, Q., Tian, Q., Leng, M. and Xu, G. (2022) Single Sensor Based Global Maximum Power Point Tracking Algorithm of PV System with Partial Shading Condition. IEEE Transactions on Industrial Electronics, 69, 2669-2683.
https://doi.org/10.1109/TIE.2021.3066920
[30]  Li, X., Zhu, Y., Wen, H., Du, Y. and Xiao, W. (2022) Reference-Voltage-Line-Aided Power Incremental Algorithm for Photovoltaic GMPPT and Partial Shading Detection. IEEE Transactions on Sustainable Energy, 13, 1756-1770.
https://doi.org/10.1109/TSTE.2022.3174614
[31]  Barbosa, E.J., Cavalcanti, M.C., Azevedo, G.M.S., Neto, R.C., Barbosa, E.A.O. and Bradaschia, F. (2023) Hybrid GMPPT Technique for Photovoltaic Series Based on Fractional Characteristic Curve. IEEE Journal of Photovoltaics, 14, 170-177.
https://doi.org/10.1109/JPHOTOV.2023.3323774
[32]  Rahman, M.M. and Islam, M. (2020) PSO and ANN Based Hybrid MPPT Algorithm for Photovoltaic Array under Partial Shading Condition. Engineering International, 8, 9-24.
https://doi.org/10.18034/ei.v8i1.481
[33]  Ahmad, W., Khan, Z.A., Khan, U.H., Alam, Z., Qasuria, H.T. and Mustafa, E. (2020) Neural Network Based Robust Nonlinear GMPPT Control Approach for Partially Shadow Conditions of Solar Energy System. 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, 26-27 March 2020, 1–7.
https://doi.org/10.1109/ICETST49965.2020.9080701
[34]  Ye, S.P., Liu, Y.H., Pai, H.Y., Sangwongwanich, A. and Blaabjerg, F. (2024) A Novel ANN-Based GMPPT Method for PV Systems under Complex Partial Shading Conditions. IEEE Transactions on Sustainable Energy, 15, 328-338.
https://doi.org/10.1109/TSTE.2023.3284866
[35]  Miyatake, M., Toriumi, F., Endo, T. and Fujii, N. (2007) A Novel Maximum Power Point Tracker Controlling Several Converters Connected to Photovoltaic Arrays with Particle Swarm Optimization Technique. 2007 European Conference on Power Electronics and Applications, Aalborg, 2-5 September 2007, 1-10.
https://doi.org/10.1109/EPE.2007.4417640
[36]  Ishaque, K. and Salam, Z. (2013) A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition. IEEE Transactions on Industrial Electronics, 60, 3195-3206.
https://doi.org/10.1109/TIE.2012.2200223
[37]  Leong, J.Y., Gopal, L., Chiong, C.W.R., Juwono, F.H. and Basuki, T.A. (2023) Hybrid Gravitational Search Particle Swarm Optimization Algorithm for GMPPT under Partial Shading Conditions. Green Technologies and Sustainability, 1, Article ID: 100034.
https://doi.org/10.1016/j.grets.2023.100034
[38]  Motamarri, R., Bhookya, N. and Chitti Babu, B. (2021) Modified Grey Wolf Optimization for Global Maximum Power Point Tracking under Partial Shading Conditions in Photovoltaic System. International Journal of Circuit Theory and Applications, 49, 1884-1901.
https://doi.org/10.1002/cta.3018
[39]  Farayola, A.M., Sun, Y. and Ali, A. (2022) Global Maximum Power Point Tracking and Cell Parameter Extraction in Photovoltaic Systems Using Improved Firefly Algorithm. Energy Reports, 8, 162-186.
https://doi.org/10.1016/j.egyr.2022.09.130
[40]  Motahhir, S., Chouder, A., Hammoumi, A.E., Benyoucef, A.S., Ghzizal, A.E., Kichou, S., Kara, K., Sanjeevikumar, P. and Silvestre, S. (2021) Optimal Energy Harvesting from a Multistrings PV Generator Based on Artificial Bee Colony Algorithm. IEEE Systems Journal, 15, 4137-4144.
https://doi.org/10.1109/JSYST.2020.2997744
[41]  Lodhi, E., Yang, P., Wang, L., Khan, M.A., Lodhi, Z., Javed, U. and Saleem, Q. (2021) Dragonfly Optimization-Based MPPT Algorithm for Standalone PV System under Partial Shading. 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT), Chongqing, 22-24 November 2021, 277-283.
https://doi.org/10.1109/ICESIT53460.2021.9697000
[42]  Sridhar, R., Subramani, C. and Pathy, S. (2021) A Grasshopper Optimization Algorithm Aided Maximum Power Point Tracking for Partially Shaded Photovoltaic Systems. Computers & Electrical Engineering, 92, Article ID: 107124.
https://doi.org/10.1016/j.compeleceng.2021.107124
[43]  Prasanth Ram, J. and Rajasekar, N. (2017) A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking. IEEE Transactions on Power Electronics, 32, 8486-8499.
https://doi.org/10.1109/TPEL.2016.2645449
[44]  Titri, S., Larbes, C., Toumi, K.Y. and Benatchba, K. (2017) A New MPPT Controller Based on the Ant Colony Optimization Algorithm for Photovoltaic Systems under Partial Shading Conditions. Applied Soft Computing, 58, 465-479.
https://doi.org/10.1016/j.asoc.2017.05.017
[45]  Pal, R.S. and Mukherjee, V. (2021) A Novel Population Based Maximum Point Tracking Algorithm to Overcome Partial Shading Issues in Solar Photovoltaic Technology. Energy Conversion and Management, 244, Article ID: 114470.
https://doi.org/10.1016/j.enconman.2021.114470
[46]  Pervez, I., Shams, I., Mekhilef, S., Sarwar, A., Tariq, M. and Alamri, B. (2021) Most Valuable Player Algorithm Based Maximum Power Point Tracking for a Partially Shaded PV Generation System. IEEE Transactions on Sustainable Energy, 12, 1876-1890.
https://doi.org/10.1109/TSTE.2021.3069262
[47]  Rezk, H. and Fathy, A. (2017) Simulation of Global MPPT Based on Teaching—Learning-Based Optimization Technique for Partially Shaded PV System. Electrical Engineering, 99, 847-859.
https://doi.org/10.1007/s00202-016-0449-3
[48]  Lyden, S. and Haque, M.E. (2016) A Simulated Annealing Global Maximum Power Point Tracking Approach for PV Modules under Partial Shading Conditions. IEEE Transactions on Power Electronics, 31, 4171-4181.
https://doi.org/10.1109/TPEL.2015.2468592
[49]  Mirza, A.F., Mansoor, M. and Ling, Q. (2020) A Novel MPPT Technique Based on Henry Gas Solubility Optimization. Energy Conversion and Management, 225, Article ID: 113409.
https://doi.org/10.1016/j.enconman.2020.113409
[50]  Liu, L., Zhang, R. and Chen, Q. (2022) High-Performance Global Peak Tracking Technique for PV Arrays Subject to Rapidly Changing PSC. Chaos, Solitons & Fractals, 160, Article ID: 112214.
https://doi.org/10.1016/j.chaos.2022.112214
[51]  Motamarri, R. and Nagu, B. (2020) GMPPT by Using PSO Based on Lévy Flight for Photovoltaic System under Partial Shading Conditions. IET Renewable Power Generation, 14, 1143-1155.
https://doi.org/10.1049/iet-rpg.2019.0959
[52]  Mathi, D.K. and Chinthamalla, R. (2020) 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. International Transactions on Electrical Energy Systems, 30, e12543.
https://doi.org/10.1002/2050-7038.12543
[53]  Pilakkat, D. and Kanthalakshmi, S. (2019) An Improved P&O Algorithm Integrated with Artificial Bee Colony for Photovoltaic Systems under Partial Shading Conditions. Solar Energy, 178, 37-47.
https://doi.org/10.1016/j.solener.2018.12.008
[54]  Liu, Y.H., Huang, S.C., Huang, J.W. and Liang, W.C. (2012) A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. IEEE Transactions on Energy Conversion, 27, 1027-1035.
https://doi.org/10.1109/TEC.2012.2219533
[55]  Lian, K.L., Jhang, J.H. and Tian, I.S. (2014) A Maximum Power Point Tracking Method Based on Perturb-and-Observe Combined with Particle Swarm Optimization. IEEE Journal of Photovoltaics, 4, 626-633.
https://doi.org/10.1109/JPHOTOV.2013.2297513
[56]  Villalva, M.G., Gazoli, J.R. and Filho, E.R. (2009) Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. IEEE Transactions on Power Electronics, 24, 1198-1208.
https://doi.org/10.1109/TPEL.2009.2013862
[57]  Nguyen, N.B. (2014) Modeling and Simulation of Photovoltaic Generator.
https://dumas.ccsd.cnrs.fr/dumas-01220257
[58]  Miyatake, M., Veerachary, M., Toriumi, F., Fujii, N. and Ko, H. (2011) Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach. IEEE Transactions on Aerospace and Electronic Systems, 47, 367-380.
https://doi.org/10.1109/TAES.2011.5705681
[59]  Mohanty, S., Subudhi, B. and Ray, P.K. (2017) A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System. IEEE Transactions on Energy Conversion, 32, 340-347.
https://doi.org/10.1109/TEC.2016.2633722
[60]  Nguyen, X.H. and Nguyen, M.P. (2015) Mathematical Modeling of Photovoltaic Cell/Module/Arrays with Tags in Matlab/Simulink. Environmental Systems Research, 4, Article No. 24.
https://doi.org/10.1186/s40068-015-0047-9
[61]  Gragger, J.V., Haumer, A. and Einhorn, M. (2010) Averaged Model of a Buck Converter for Efficiency Analysis. Engineering Letters, 18.

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