Document Type : Research Article
Authors
1 Department of Electrical & Electronics Engineering, IIMT University, Meerut, P. O. Box: 250001, India.
2 Department of R&D, IIMT University, Meerut, P. O. Box: 250001, India.
3 Department of Electronics & Communication Engineering, IIMT College Of Engineering, Greater Noida, P. O. Box: 201306, India.
Abstract
The rapid rise in electrical energy demand and the depletion of fossil fuels have created a market for renewable energy. Among all the renewable energy resources, the most popular is solar energy, perceived as pollution-free, easily accessible, and low maintenance. In non-uniform solar irradiation or partial shading conditions (PSC), the photovoltaic characteristics (PVC) of a solar panel system (SPS) exhibit multiple minor peaks (MP) with one global peak power point (GPPP). To extract the utmost energy from the SPS, the authors proposed an efficient hybrid algorithm integrating the advantages of machine learning and the classical algorithm fractional open circuit voltage (FOVA) to track the GPPP. To follow the GPPP of SPS under unstable environmental surroundings, this study tests ML-based hybrid MPPT algorithms, specifically squared multiple variable linear regression algorithms (SMVLRA), using Matlab/Simulink. Simulation through Matlab is employed to validate the efficiency of the SMVLRA-MPPT approach compared to existing popular conventional and modern MPPT algorithms, namely the Perturb and Observation algorithm (P&OA), the variable step size incremental conductance (VINC) algorithm, and an intelligent algorithm, Decision Tree Regression Algorithm (DTRA). The simulation results demonstrate that SMVLRA offers higher peak power and mean peak power efficiency in less tracking time, with lower error and almost negligible steady-state fluctuation under PSC. The proposed algorithm achieves 99.99% efficiency under standard test conditions (1000w/m2, 25°C), 99.95% under PSC1 (1000w/m2, 800w/m2, 25°C), and 98.89% under PSC2 (1000w/m2, 800w/m2, 600w/m2, 25°C)
Keywords
- Solar Panel System (SPS)
- MPPT (Maximum Power Point Tracking)
- Squared Multivariable linear Regression Algorithm
- (SMVLRA)
- P&OA
- VINC
- DTRA
- Matlab/Simulink
Main Subjects
- Alpaydin, E. (2013). Introduction to Machine Learning. MIT press. https://dl.matlabyar.com/siavash/ML/Book/Ethem%20Alpaydin-Introduction%20to%20Machine%20Learning-The%20MIT%20Press%20(2014).pdf
- Ameur, K., Hadjaissa, A., Boutoubat, M., &Abouchabana, N. (2018). A fast MPPT control technique using PID controller in a photovoltaic system. 2018 International Conference on Applied Smart Systems (ICASS). https://doi.org/10.1109/icass.2018.8652062
- Behera, M. K., Majumder, I., & Nayak, N. (2018). Solar photovoltaic power forecasting using optimized modified extreme learning machine technique. Engineering Science and Technology, an International Journal, 21(3), 428–438. https://doi.org/10.1016/j.jestch.2018.04.013
- Bendib, B., Belmili, H., &Krim, F. (2015). A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems. Renewable & Sustainable Energy Reviews, 45, 637–648. https://doi.org/10.1016/j.rser.2015.02.009
- Du, Y., Yan, K., Ren, Z., & Xiao, W. (2018). Designing localized MPPT for PV systems using Fuzzy-Weighted Extreme Learning Machine. Energies, 11(10), 2615. https://doi.org/10.3390/en11102615
- Harrison, Ambe, &Alombah, H. (2022). Solar PV data: (A-M) piecewise segmentation of the I-V curve.https://doi.org/10.6084/m9.figshare.21677555.v
- Hayder, W., Ogliari, E., Dolara, A., Abid, A., Hamed, M. B., &Sbita, L. (2020). Improved PSO: A Comparative Study in MPPT Algorithm for PV System Control under Partial Shading Conditions. Energies, 13(8), 2035. https://doi.org/10.3390/en13082035
- Hill, J. S. (n.d.). Global solar market demand expected to reach 100 Gigawatts in 2017, says solarpower Europe’, (2017).https://cleantechnica.com/2017/10/27/global-solar-market-demand-expected-reach-100-gw-2017-solarpower-europe/
- International Energy Agency: ‘ Snapshot of Global Photovoltaic Markets 2017’, (2017). (n.d.).http://www.ieapvps.org/fileadmin/dam/public/report/statistics/IEA-PVPS-A.
- Ishrat, Z., Nayak, S., & Gupta, A. (2023). Journal Of Renenwable Energy and Environment. A Comprehensive Review of MPPT Techniques Based on ML Applicable for Maximum Power in Solar Power Systems.https://doi.org/10.30501/jree.2023.385661.1556
- Ishrat, Z., Yadav, D. K., Sharma, D. K., & Nayak, S. (2022). Review on:Challenges Solution & Scope of IOT in Solar Energy. J East China Univ Sci Technology, 65(4), 587–594. http://hdlgdxxb.info/index.php/JE_CUST/article/view/477
- Kalaiarasi, N., Dash, S. S., Padmanaban, S., Paramasivam, S., &Morati, P. K. (2018). Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications. Applied Sciences, 8(1), 145. https://doi.org/10.3390/app8010145
- Kalogerakis, C., Koutroulis, E., &Lagoudakis, M. G. (2020). Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions. Applied Sciences, 10(2), 700. https://doi.org/10.3390/app10020700
- Kumar, N., Hussain, I., Singh, B., &Panigrahi, B. K. (2018). Framework of maximum power extraction from solar PV panel using self predictive perturb and observe algorithm. IEEE Transactions on Sustainable Energy, 9(2), 895–903. https://doi.org/10.1109/tste.2017.2764266
- Lorenzo, E. (1994). Solar Electricity: Engineering of photovoltaic systems: Vol. Chapter 3. 7.6. , Progensa, Earthscan Publications Ltd.,https://ccl.northwestern.edu/2016/Opiyo.pdf
- Mahesh, P. V., Meyyappan, S., &Alla, R. R. (2022). Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems. Clean Energy, 6(5), 762–775. https://doi.org/10.1093/ce/zkac057
- Mahesh V. (2023) Maximum Power Point Tracking with Regression Machine Learning Algorithms for Solar PV systems. (2023). International Journal of Renewable Energy Research, Vol12i3. https://doi.org/10.20508/ijrer.v12i3.13249.g8517
- Memayaa, M., &Moorthyb, C. Balakrishna. (2019). Machine learning based maximum power point tracking in solar energy conversion systems. International Journal of Smart Grid and Clean Energy. https://www.ijsgce.com/uploadfile/2019/0910/20190910030832865.pdf
- Rashid, M. H. (2016), Power Electronics: Devices, Circuits, and Applications. (n.d.).https://powerunit-ju.com/wp-content/uploads/2016/11/Book-Power_Electronics_Handbook_3rd_Edition_M_Rashid.pdf
- Motahhir, S., Hammoumi, A. E., &Ghzizal, A. E. (2020). The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm. Journal of Cleaner Production, 246, 118983. https://doi.org/10.1016/j.jclepro.2019.118983
- Nkambule, M. S., Hasan, A. N., Ali, A., Hong, J., &Geem, Z. W. (2020). Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions. Journal of Electrical Engineering & Technology, 16(1), 411–427. https://doi.org/10.1007/s42835-020-00598-0
- Nugraha, D. A., Lian, K. L., &Suwarno. (2019). A novel MPPT method based on Cuckoo search algorithm and Golden Section search algorithm for partially shaded PV system. Canadian Journal of Electrical and Computer Engineering, 42(3), 173–182. https://doi.org/10.1109/cjece.2019.2914723
- Owusu-Nyarko, I., Elgenedy, M. A., Abdelsalam, I., & Ahmed, K. (2021). Modified Variable Step-Size Incremental Conductance MPPT technique for photovoltaic systems. Electronics, 10(19), 2331. https://doi.org/10.3390/electronics10192331
- Phanden, R. K., Sharma, L. K., Chhabra, J., & Demir, H. İ. (2021). A novel modified ant colony optimization based maximum power point tracking controller for photovoltaic systems. Materials Today: Proceedings, 38, 89–93. https://doi.org/10.1016/j.matpr.2020.06.020
- 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. (2019). An adaptive Neuro-Fuzzy inference System-Based intelligent Grid-Connected photovoltaic power generation. In Advances in intelligent systems and computing (pp. 3–14). https://doi.org/10.1007/978-981-13-8222-2_1
- Rabaia, M. K. H., Abdelkareem, M. A., Sayed, E. T., Elsaid, K., Chae, K., Wilberforce, T., &Olabi, A. (2021). Environmental impacts of solar energy systems: A review. Science of the Total Environment, 754, 141989. https://doi.org/10.1016/j.scitotenv.2020.141989
- Radjai, T., Rahmani, L., Mekhilef, S., &Gaubert, J. (2014a). Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE. Solar Energy, 110, 325–337. https://doi.org/10.1016/j.solener.2014.09.014
- Reisi, A. R., Moradi, M. H., &Jamasb, S. (2013). Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewable & Sustainable Energy Reviews, 19, 433–443. https://doi.org/10.1016/j.rser.2012.11.052
- Rezk, H., & Fathy, A. (2016). Simulation of global MPPT based on teaching–learning-based optimization technique for partially shaded PV system. Electrical Engineering, 99(3), 847–859. https://doi.org/10.1007/s00202-016-0449-3
- Riaz, A., Murtaza, A. F., & Sher, H. A. (2019). Power tracking techniques for efficient operation of photovoltaic array in solar applications – A review. Renewable & Sustainable Energy Reviews, 101, 82–102. https://doi.org/10.1016/j.rser.2018.10.015
- Sakthivel, S. S., & Arunachalam, V. (2022). Artificial Neural Network Assisted P &O-Based MPPT Controller for a Partially Shaded Grid-Connected Solar PV Panel. Arabian Journal for Science and Engineering, 48(11), 14333–14344. https://doi.org/10.1007/s13369-022-07566-y
- Shaiek MB, Smida AS, Mimouni MF. (2012). Comparison between conventional methods and {GA} approach for maximum power point tracking of shaded solar{PV}generators. Solar Energy. https://www.sciencedirect.com/science/article/PII/S0038092X1300009.
- Sharmin, R., Chowdhury, S. S., Abedin, F., & Rahman, K. (2022). Implementation of an MPPT technique of a solar module with supervised machine learning. Frontiers in Energy Research, 10. https://doi.org/10.3389/fenrg.2022.932653
- Takruri, M., Farhat, M., Barambones, Ó., Ramos-Hernanz, J. A., Turkieh, M. J., Badawi, M., AlZoubi, H., &Sakur, M. A. (2020). Maximum power point tracking of PV system based on machine learning. Energies, 13(3), 692. https://doi.org/10.3390/en13030692
- Verma, D., Nema, S., Shandilya, A., & Dash, S. K. (2016). Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems. Renewable & Sustainable Energy Reviews, 54, 1018–1034. https://doi.org/10.1016/j.rser.2015.10.068
- Vincheh, M. R., Kargar, A., &Markadeh, G. A. (2014). A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems. Arabian Journal for Science and Engineering, 39(6), 4715–4725. https://doi.org/10.1007/s13369-014-1056-0
- Vinod, Kumar, R., & Singh, S. (2018). Solar photovoltaic modeling and simulation: As a renewable energy solution. Energy Reports, 4, 701–712. https://doi.org/10.1016/j.egyr.2018.09.008
- Yap, K. Y., Sarimuthu, C. R., & Lim, J. M. (2020). Artificial Intelligence Based MPPT Techniques for Solar Power System: A review. Journal of Modern Power Systems and Clean Energy, 8(6), 1043–1059. https://doi.org/10.35833/mpce.2020.000159
- Ishrat, S. Vats, K. B. Ali, O. Ahmad Shah and T. Ahmed, (2023).A Comprehensive Study on Conventional HPPT Techniques for Solar PV System, International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India, 2023, pp. 315-321, https://doi.org/10.1109/CICTN57981.2023.10140264
- Ishrat., Babar Ali, K., Vats, S., & Kumar, S. (2023). Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions. InternationalJournal of Robotics and Control Systems, 3(4), 1007-1020. https://doi.org/10.31763/ijrcs.v3i4.1176