Document Type : Research Article
Authors
- Nikita Gupta 1
- Mahajan Sagar Bhaskar 2
- Sanjay Kumar 1
- Dhafer J. Almakhles 2
- Tarun Panwar 1
- Abhinav Banyal 1
- Aanandita Sharma 1
- Akanksha Nadda 1
1 Department of Electrical Engineering, University Institute of Technology, HPU, Shimla, India.
2 Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Abstract
The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research.
Keywords
Main Subjects
- Adly, M., & Besheer, A. H. (2012, July). An optimized fuzzy maximum power point tracker for stand-alone photovoltaic systems: Ant colony approach. In 2012 7th IEEE conference on industrial electronics and applications (ICIEA)(pp. 113-119). IEEE. https://doi.org/10.1093/benz/9780199773787.article.b00000988
- Ahmad, J. (2010, October). A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays. In 2010 2nd International Conference on Software Technology and Engineering (Vol. 1, pp. V1-247). IEEE. https://doi.org/10.1109/icste.2010.5608868
- Baimel, D., Tapuchi, S., Levron, Y., & Belikov, J. (2019). Improved fractional open circuit voltage MPPT methods for PV systems. Electronics, 8(3), https://doi.org/10.3390/electronics8030321
- Batarseh, M. G., & Za'ter, M. E. (2018). Hybrid maximum power point tracking techniques: A comparative survey, suggested classification and uninvestigated combinations. Solar Energy, 169, 535-555. https://doi.org/10.1016/j.solener.2018.04.045
- Belkaid, A., Colak, U., & Kayisli, K. (2017, November). A comprehensive study of different photovoltaic peak power tracking methods. In 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 1073-1079). IEEE. https://doi.org/10.1109/icrera.2017.8191221
- Bennis Ghita, K. M., & Ahmed, L. (2018). Application and comparison between the conventional methods and PSO method for maximum power point extraction in photovoltaic systems under partial shading conditions. Int J Pow Elec & Dri Syst, 9(2), 631-640. https://doi.org/10.11591/ijpeds.v9.i2.pp631-640
- Blange, R., Mahanta, C., & Gogoi, A. K. (2015, June). MPPT of solar photovoltaic cell using perturb & observe and fuzzy logic controller algorithm for buck-boost DC-DC converter. In 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE) (pp. 1-5). IEEE. https://doi.org/10.1109/epetsg.2015.7510125
- Bollipo, R. B., Mikkili, S., & Bonthagorla, P. K. (2020). Hybrid, optimal, intelligent and classical PV MPPT techniques: A review. CSEE Journal of Power and Energy Systems, 7(1), 9-33. https://doi.org/10.17775/cseejpes.2019.02720
- Boonmee, C., & Kumsuwan, Y. (2013, May). Modified maximum power point tracking based-on ripple correlation control application for single-phase VSI grid-connected PV systems. In 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (pp. 1-6). IEEE. https://doi.org/10.1109/ecticon.2013.6559503
- Brunton, S. L., Rowley, C. W., Kulkarni, S. R., & Clarkson, C. (2010). Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control. IEEE transactions on power electronics, 25(10), 2531-2540. https://doi.org/10.1109/tpel.2010.2049747
- Casadei, D., Grandi, G., & Rossi, C. (2006). Single-phase single-stage photovoltaic generation system based on a ripple correlation control maximum power point tracking. IEEE Transactions on Energy Conversion, 21(2), 562-568. https://doi.org/10.1109/tec.2005.853784
- Catherine, T. J. (2013). A Digital MPPT Control for the Optimization of a Photo Voltaic System as a Battery Charger. International Journal of Emerging Technology and Advanced Engineering, 3(4). (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 4, April 2013). https://www.academia.edu/download/36794892/MPPT_paper_.pdf
- Cheng, H., Li, S., Fan, Z., & Liu, L. (2021, May). Intelligent MPPT Control Methods for Photovoltaic System: A review. In 2021 33rd Chinese Control and Decision Conference (CCDC) (pp. 1439-1443). https://doi.org/10.1109/ccdc52312.2021.9602802
- Datta, M., & Senjyu, T. (2013). Fuzzy control of distributed PV inverters/energy storage systems/electric vehicles for frequency regulation in a large power system. IEEE Transactions on Smart Grid, 4(1), 479-488. https://doi.org/10.1109/tsg.2012.2237044
- Dogra, R., Kumar, S., & Gupta, N. (2022). Application of Artificial Neural Network to Solar Potential Estimation in Hilly Region of India. Journal of Renewable Energy and Environment, 9(3), 10-16. https://www.jree.ir/article_149613.html.
- Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics), 26(1), 29-41. https://doi.org/10.1109/3477.484436
- Elobaid, L. M., Abdelsalam, A. K., & Zakzouk, E. E. (2012, October). Artificial neural network based maximum power point tracking technique for PV systems. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society(pp. 937-942). IEEE. https://doi.org/10.1109/iecon.2012.6389165
- Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on energy conversion, 22(2), 439-449. https://doi.org/10.1109/tec.2006.874230
- Giraud, F., & Salameh, Z. M. (1999). Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks. IEEE Transactions on Energy Conversion, 14(4), 1572-1577. https://doi.org/10.1109/60.815107
- Fahad, S., Mahdi, A. J., Tang, W. H., Huang, K., & Liu, Y. (2018, November). Particle swarm optimization-based DC-link voltage control for two-stage grid connected PV inverter. In 2018 International Conference on Power System Technology (POWERCON)(pp. 2233-2241) IEEE. https://doi.org/10.1109/powercon.2018.8602128
- Fapi, C. B. N., Wira, P., Kamta, M., Badji, A., & Tchakounte, H. (2019). Real-time experimental assessment of Hill Climbing MPPT algorithm enhanced by estimating a duty cycle for PV system. International Journal of Renewable Energy Research. https://doi.org/10.20508/ijrer.v9i3.9432.g7705
- Figueiredo, S., & e Silva, R. N. A. L. (2021). Hybrid mppt technique pso-p&o applied to photovoltaic systems under uniform and partial shading conditions. IEEE Latin America Transactions, 19(10), 1610-1617. https://doi.org/10.1109/tla.2021.9477222
- Giraud, F., & Salameh, Z. M. (1999). Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks. IEEE Transactions on Energy Conversion, 14(4), 1572-1577. https://doi.org/10.1109/60.815107
- Gupta, N., & Garg, R. (2017). Tuning of asymmetrical fuzzy logic control algorithm for SPV system connected to grid. International journal of hydrogen energy, 42(26), 16375-16385. https://doi.org/10.1016/j.ijhydene.2017.05.103
- Gupta, N., Garg, R., & Kumar, P. (2015, December). Characterization study of PV module connected to microgrid. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE. https://doi.org/10.1109/INDICON.2015.7443360.
- Gupta, N., Garg, R., & Kumar, P. (2017). Sensitivity and reliability models of a PV system connected to grid. Renewable and Sustainable Energy Reviews, 69, 188-196. https://doi.org/10.1016/j.rser.2016.11.031
- Hohm, D. P., & Ropp, M. E. (2003). Comparative study of maximum power point tracking algorithms. Progress in photovoltaics: Research and Applications, 11(1), 47-62. https://doi.org/10.1002/pip.459
- Huynh, D. C., & Dunnigan, M. W. (2016). Development and comparison of an improved incremental conductance algorithm for tracking the MPP of a solar PV panel. IEEE transactions on sustainable energy, 7(4), 1421-1429. https://doi.org/10.1109/tste.2016.2556678
- Ishaque, K., Salam, Z., Amjad, M., & Mekhilef, S. (2012). An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE transactions on Power Electronics, 27(8), 3627-3638. https://doi.org/10.1109/tpel.2012.2185713.
- Jiang, L. L., Maskell, D. L., & Patra, J. C. (2013). A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58, 227-236. https://doi.org/10.1016/j.enbuild.2012.12.001
- Bennis Ghita, K. M., & Ahmed, L. (2018). Application and comparison between the conventional methods and PSO method for maximum power point extraction in photovoltaic systems under partial shading conditions. Int J Pow Elec & Dri Syst, 9(2), 631-640. https://doi.org/10.11591/ijpeds.v9.i2.pp631-640
- Karami, N., Moubayed, N., & Outbib, R. (2017). General review and classification of different MPPT Techniques. Renewable and Sustainable Energy Reviews, 68, 1-18. https://doi.org/10.1016/j.rser.2016.09.132
- Kottas, T. L., Boutalis, Y. S., & Karlis, A. D. (2006). New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks. IEEE Transactions on Energy conversion, 21(3), 793-803. https://doi.org/10.1109/tec.2006.875430
- Krishnan G, S., Kinattingal, S., Simon, S. P., & Nayak, P. S. R. (2020). MPPT in PV systems using ant colony optimisation with dwindling population. IET Renewable Power Generation, 14(7), 1105-1112. https://doi.org/10.1049/iet-rpg.2019.0875
- Kumar, A., Kumar, D., & Jarial, S. K. (2017). A review on artificial bee colony algorithms and their applications to data clustering. Cybernetics and Information Technologies, 17(3), 3-28. https://doi.org/10.1515/cait-2017-0027
- Kumar, M., Panda, K. P., Rosas-Caro, J. C., Valderrabano-Gonzalez, A., & Panda, G. (2023). Comprehensive Review of Conventional and Emerging Maximum Power Point Tracking Algorithms for Uniformly and Partially Shaded Solar Photovoltaic Systems. IEEE Access (11). https://doi.org/10.1109/access.2023.3262502
- Kumar, S., & Kaur, T. (2020). Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 234(6), 862-873. https://doi.org/10.1177/0957650919878318
- Kumar, S., Sharma, S., Sood, Y. R., Upadhyay, S., & Kumar, V. (2022). A review on different parametric aspects and sizing methodologies of hybrid renewable energy system. Journal of The Institution of Engineers (India): Series B, 103(4), 1345-1354. https://doi.org/10.1007/s40031-022-00738-2
- Li, N., Mingxuan, M., Yihao, W., Lichuang, C., Lin, Z., & Qianjin, Z. (2019, July). Maximum power point tracking control based on modified ABC algorithm for shaded PV system. In 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) (pp. 1-5). IEEE. https://doi.org/10.23919/eeta.2019.8804525
- Li, X., Wen, H., Hu, Y., & Jiang, L. (2019). A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application. Renewable energy, 130, 416-427. https://doi.org/10.1016/j.renene.2018.06.071
- Lian, K. L., Jhang, J. H., & 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(2), 626-633. https://doi.org/10.1109/jphotov.2013.2297513
- Loukriz, A., Haddadi, M., & Messalti, S. (2016). Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems. ISA transactions, 62, 30-38. https://doi.org/10.1016/j.isatra.2015.08.006
- Reatti, A., & Balzani, M. (2005). Neural network-based model of a PV array for the optimum performance of PV system. In Proceedings of IEEE Research in Microelectronics and Electronics, (2), 123-126). IEEE. https://doi.org/10.1109/rme.2005.1542952
- Mahdi, A. S., Mahamad, A. K., Saon, S., Tuwoso, T., Elmunsyah, H., & Mudjanarko, S. W. (2020). Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS. SN Applied Sciences, (2), 1-9. https://doi.org/10.1007/s42452-019-1886-1
- Mahdi, A. S., Mahamad, A. K., Saon, S., Tuwoso, T., Elmunsyah, H., & Mudjanarko, S. W. (2020). Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS. SN Applied Sciences, (2), 1-9. https://doi.org/10.1007/s42452-019-1886-1
- Masoum, M. A., Dehbonei, H., & Fuchs, E. F. (2002). Theoretical and experimental analyses of photovoltaic systems with voltageand current-based maximum power-point tracking. IEEE Transactions on energy conversion, 17(4), 514-522. https://doi.org/10.1109/tec.2002.805205
- Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, (69), 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
- Miyatake, M., Toriumi, F., Endo, T., & Fujii, N. (2007, September). A Novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique. In 2007 European conference on power electronics and applications (pp. 1-10). IEEE. https://doi.org/10.1109/epe.2007.4417640
- Mohanty, S., Subudhi, B., & Ray, P. K. (2015). A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181-188. https://doi.org/10.1109/tste.2015.2482120
- Mohanty, S., Subudhi, B., & Ray, P. K. (2016). A grey wolf-assisted perturb & observe MPPT algorithm for a PV system. IEEE Transactions on Energy Conversion, 32(1), 340-347. https://doi.org/10.1109/tec.2016.2633722
- Mohapatra, A., Nayak, B., Das, P., & Mohanty, K. B. (2017). A review on MPPT techniques of PV system under partial shading condition. Renewable and Sustainable Energy Reviews, (80), 854-867. https://doi.org/10.1016/j.rser.2017.05.083
- Moo, C. S., & Wu, G. B. (2014). Maximum power point tracking with ripple current orientation for photovoltaic applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(4), 842-848. https://doi.org/10.1109/jestpe.2014.2328577
- Mosavi, S. K., Jalalian, E., Soleimenian, F., & Branch, U. (2018). A comprehensive survey of grey wolf optimizer algorithm and its application. Int. Adv. Robot. Expert Syst., 1(6), 23-45. https://doi.org/10.1016/j.eswa.2022.118267
- Karami, N., Moubayed, N., & Outbib, R. (2017). General review and classification of different MPPT Techniques. Renewable and Sustainable Energy Reviews, (68), 1-18. https://doi.org/10.1016/j.rser.2016.09.132
- Nnadi, D. B. N. (2012). Environmental/climatic effect on stand-alone solar energy supply performance for sustainable energy.Nigerian Journal of Technology, 31(1), 79-88. https://doi.org/10.4314/njt.v36i2.34
- Pandey, A., & Srivastava, S. (2019). Perturb & observe MPPT technique used for PV system under different environmental conditions. Int. Res. J. Eng. Technol, 6, 2829-2835. https://www.irjet.net/archives/V6/i4/IRJET-V6I4602.pdf
- Podder, A. K., Roy, N. K., & Pota, H. R. (2019). MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renewable Power Generation, 13(10), 1615-1632. https://doi.org/10.1049/iet-rpg.2018.5946
- Priyadarshi, N., Azam, F., Sharma, A. K., & Vardia, M. (2020). An adaptive neuro-fuzzy inference system-based intelligent grid-connected photovoltaic power generation. In Advances in Computational Intelligence: Proceedings of Second International Conference on Computational Intelligence 2018 (pp. 3-14). Springer https://doi.org/10.1007/978-981-13-8222-2_1
- Priyadarshi, N., Padmanaban, S., Maroti, P. K., & Sharma, A. (2018). An extensive practical investigation of FPSO-based MPPT for grid integrated PV system under variable operating conditions with anti-islanding protection. IEEE Systems Journal, 13(2), 1861-1871. https://doi.org/10.1109/jsyst.2018.2817584
- Priyadarshi, N., Padmanaban, S., Sagar Bhaskar, M., Blaabjerg, F., & Sharma, A. (2018). Fuzzy SVPWM‐based inverter control realisation of grid integrated photovoltaic‐wind system with fuzzy particle swarm optimisation maximum power point tracking algorithm for a grid‐connected PV/wind power generation system: hardware implementation. IET Electric Power Applications, 12(7), 962-971. https://doi.org/10.1049/iet-epa.2017.0804
- Kundu, S., Gupta, N., & Kumar, P. (2016, November). Review of solar photovoltaic maximum power point tracking techniques. In 2016 7th India International Conference on Power Electronics (IICPE) (pp. 1-6). IEEE. https://doi.org/10.1109/iicpe.2016.8079494
- Sa-ngawong, N., & Ngamroo, I. (2015). Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control. Renewable Energy, 74, 555-567. https://doi.org/10.1016/j.renene.2014.08.057
- Sera, D., Kerekes, T., Teodorescu, R., & Blaabjerg, F. (2006, August). Improved MPPT algorithms for rapidly changing environmental conditions. In 2006 12th International Power Electronics and Motion Control Conference (pp. 1614-1619). IEEE. https://doi.org/10.1109/epepemc.2006.283440
- Sera, D., Mathe, L., Kerekes, T., Spataru, S. V., & Teodorescu, R. (2013). On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE journal of photovoltaics, 3(3), 1070-1078. https://doi.org/10.1109/jphotov.2013.2261118
- Sharma, P., & Agarwal, V. (2013). Exact maximum power point tracking of grid-connected partially shaded PV source using current compensation concept. IEEE Transactions on Power Electronics, 29(9), 4684-4692. https://doi.org/10.1109/tpel.2013.2285075
- Sher, H. A., Murtaza, A. F., Noman, A., Addoweesh, K. E., & Chiaberge, M. (2015). An intelligent control strategy of fractional short circuit current maximum power point tracking technique for photovoltaic applications. Journal of renewable and sustainable Energy, 7(1). https://doi.org/10.1063/1.4906982
- Sher, H. A., Murtaza, A. F., Noman, A., Addoweesh, K. E., Al-Haddad, K., & Chiaberge, M. (2015). A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT. IEEE Transactions on sustainable energy, 6(4), 1426-1434. https://doi.org/10.1109/tste.2015.2438781
- Shinde, U. K., Kadwane, S. G., Gawande, S. P., & Keshri, R. (2016, December). Solar PV emulator for realizing PV characteristics under rapidly varying environmental conditions. In 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-5). IEEE. https://doi.org/10.1109/pedes.2016.7914286
- Spiazzi, G., Buso, S., & Mattavelli, P. (2009, September). Analysis of MPPT algorithms for photovoltaic panels based on ripple correlation techniques in presence of parasitic components. In 2009 Brazilian Power Electronics Conference (pp. 88-95). IEEE. https://doi.org/10.1109/cobep.2009.5347738
- Srinivas, C. L., & Sreeraj, E. S. (2016). A maximum power point tracking technique based on ripple correlation control for single phase photovoltaic system with fuzzy logic controller. Energy Procedia, 90, 69-77. https://doi.org/10.1016/j.egypro.2016.11.171
- Sumathi, S., Kumar, L. A., & Surekha, P. (2015). Solar PV and wind energy conversion systems: an introduction to theory, modeling with MATLAB/SIMULINK, and the role of soft computing techniques (Vol. 1). Switzerland: Springer. https://doi.org/10.1007/978-3-319-14941-7_2
- Sundareswaran, K., & Palani, S. (2015). Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renewable Energy, 75, 308-317. https://doi.org/10.1016/j.renene.2014.09.044
- Sundareswaran, K., Sankar, P., Nayak, P. S. R., Simon, S. P., & Palani, S. (2014). Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE transactions on sustainable energy, 6(1), 198-209. https://doi.org/10.1109/tste.2014.2363521
- Tajjour, S., & Chandel, S. S. (2023). A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids. Sustainable Energy Technologies and Assessments, 58, https://doi.org/10.1016/j.seta.2023.103377
- Uddin, M., Mo, H., Dong, D., Elsawah, S., Zhu, J., & Guerrero, J. M. (2023). Microgrids: A review, outstanding issues and future trends. Energy Strategy Reviews, 49, https://doi.org/10.1016/j.esr.2023.101127
- Ramana, V. V., & Jena, D. (2015, February). Maximum power point tracking of PV array under non-uniform irradiance using artificial neural network. In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) (pp. 1-5). IEEE. https://doi.org/10.1109/spices.2015.7091514
- Xu, Q., Lin, P., & Blaabjerg, F. (2021). Power electronics converters for distributed generation. Smart Grid and Enabling Technologies, 81-112. https://doi.org/10.1002/9781119422464.ch3
- Zainuri, M. A. A. M., Radzi, M. A. M., Che Soh, A., & Rahim, N. A. (2014). Development of adaptive perturb and observe‐fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter. IET Renewable Power Generation, 8(2), 183-194. https://doi.org/10.1049/iet-rpg.2012.0362