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A Novel Hybrid Slime Mould MPPT Technique for BL-HC Configured Solar PV System Under PSCs

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Abstract

Solar photovoltaic (SPV) systems are becoming key emerging technologies for both stand-alone and grid-connected power system applications. However, uncertainty in environmental conditions such as temperature and irradiation dominates the performance of the SPV system. Due to uncertainty in environmental conditions, there should be drastic reduction in output power, efficiency and higher mismatch power loss in the SPV system. This adverse phenomenon can be overawed by the inclusion of bypass diode. But the inclusion will result in multiple peaks that are developed in the P–V curve. This will necessitate a robust MPPT controller to track the global maximum power (GMP) under PSCs. Various conventional (P&O, InC etc.) and optimization algorithms are described in the literature, but they are unsuccessful to identify GMP among multiple peaks. This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) MPPT approach (HSMO) with hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power under PSCs. The proposed work is carried out in MATLAB/Simulink environment. Also, the performance of proposed HSMO technique is compared with the latest adaptive coefficient PSO (ACPSO), Flower Pollination algorithm (FPA), and slime mould optimization (SMO) MPPT techniques in terms of tracked GMP, tracking efficiency, and convergence time/tracking speed. The results of the proposed HSMO technique also demonstrate its supremacy in terms of tracked GMP, convergence time, and efficiency.

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References

  • Alajmi, B. N., Ahmed, K. H., Finney, S. J., & Williams, B. W. (2010). 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(4), 1022–1030.

    Article  Google Scholar 

  • Bollipo, R., Mikkili, S., & Bonthagorla, P. (2019). Critical review on PV MPPT techniques: Classical, intelligent and optimization. IET Renewable Power Generation, 14(9), 1433–1452.

    Article  Google Scholar 

  • Bollipo, R., Mikkili, S., & Bonthagorla, P. K. (2021). Application of radial basis neural network in MPPT technique for stand-alone PV system under partial shading conditions. IETE Journal of Research. https://doi.org/10.1080/03772063.2021.1988874

    Article  Google Scholar 

  • Bonthagorla, P. K., & Mikkili, S. (2021). A novel fixed PV array configuration for harvesting maximum power from shaded modules by reducing the number of cross-ties. IEEE Journal of Emerging and Selected Topics in Power Electronics, 9(2), 2109–2121. https://doi.org/10.1109/JESTPE.2020.2979632

    Article  Google Scholar 

  • Eltamaly, A. M., & Farh, H. M. (2019). Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Solar Energy, 177, 306–316.

    Article  Google Scholar 

  • Houam, Y., Terki, A., & Bouarroudj, N. (2021). An efficient metaheuristic technique to control the maximum power point of a partially shaded photovoltaic system using crow search algorithm (CSA). Journal of Electrical Engineering & Technology, 16, 381–402.

    Article  Google Scholar 

  • Howard, F. L. (1931). the life history of Physarum polycephalum. American Journal of Botany, 18(2), 116–133. https://doi.org/10.1002/j.1537-2197.1931.tb09577

    Article  Google Scholar 

  • Jiang, Y., Xu, J., Leng, X., & Eghbalian, N. (2022). A novel hybrid maximum power point tracking method based on improving the effectiveness of different configuration partial shadow. Sustainable Energy Technologies and Assessment, 50, 101835.

    Article  Google Scholar 

  • Kamarzaman, N. A., & Tan, C. W. (2014). A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renewable and Sustainable Energy Reviews, 37, 585–598.

    Article  Google Scholar 

  • Khare, A., & Rangnekar, S. (2013). A review of particle swarm optimization and its applications in solar photovoltaic system. Applied Soft Computing, 13(5), 2997–3006.

    Article  Google Scholar 

  • Kota, V. R., & Bhukya, M. N. (2019). A novel global mpp tracking scheme based on shading pattern identification using artificial neural networks for photovoltaic power generation during partial shaded condition. IET Renewable Power Generation, 13(10), 1647–1659.

    Article  Google Scholar 

  • Kumar, J. C. R., & Majid, M. A. (2020). Renewable energy for sustainable development in India: Current status, future prospects, challenges, employment, and investment opportunities. Energy, Sustainability and Society, 10, 1–36.

    Article  Google Scholar 

  • Kumar, N., Hussain, I., Singh, B., & Panigrahi, B. K. (2017). Maximum power peak detection of partially shaded PV panel by using intelligent monkey king evolution algorithm. IEEE Transactions on Industry Applications, 53(6), 5734–5743.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Laxman, B., Annamraju, A., & Srikanth, N. V. (2021). A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. International Journal of Hydrogen Energy, 46, 10653–10665.

    Article  Google Scholar 

  • Mirza, A. F., Mansoor, M., & Ling, Q. (2020). A novel MPPT technique based on Henry gas solubility optimization. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2020.113409

    Article  Google Scholar 

  • Mirza, A. F., Mansoor, M., Ling, Q., Baoqun Yin, M., & Javed, Y. (2020). A salp-swarm optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2020.112625

    Article  Google Scholar 

  • Mohammadinodoushan, M., Abbassi, R., Jerbi, H., Waly Ahmed, F., Abdalqadir khahmed, H., & 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. Sustainable Energy Technologies and Assessment, 45, 101056.

    Article  Google Scholar 

  • Mohanty, S., Subudhi, B., & Ray, P. K. (2015). A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IIEEE Transactions on Sustainable Energy, 7, 181–188.

    Article  Google Scholar 

  • Mohanty, S., Subudhi, B., & Ray, P. K. (2016). 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.

    Article  Google Scholar 

  • Molleti, V. P. L., Kasibhatla, R., & Rajamahanthi, V., (2022). Modeling and implementation of statechart for MPPT control of photovoltaic system in FPGA. In Machine Learning, Advances in Computing, Renewable Energy and Communication, (pp.293–304). Springer: Singapore

  • Nakagaki, T., Yamada, H., & Ueda, T. (2000). Interaction between cell shape and contraction pattern in the Physarum plasmodium. Biophysical Chemistry, 84(3), 195–204.

    Article  Google Scholar 

  • Onyshchenko, V. F., et al. (2020). Photoconductivity relaxation time in macroporous silicon. Emerging Science Journal, 4(3), 192–204. https://doi.org/10.28991/esj-2020-01223

    Article  MathSciNet  Google Scholar 

  • Pillai, D. S., Ram, J. P., Ghias, A. M., Mahmud, M. A., & Rajasekar, N. (2019). An accurate, shade detection-based hybrid maximum power point tracking approach for PV systems. IEEE Transactions on Power Electronics, 35, 6594–6608.

    Article  Google Scholar 

  • Qerimi, D., et al. (2020). Modeling of the solar thermal energy use in urban areas. Civil Engineering Journal, 6(7), 1349–1367. https://doi.org/10.28991/cej-2020-03091553

    Article  Google Scholar 

  • Rakhshan, M., Vafamand, N., Khooban, M.-H., & Blaabjerg, F. (2018). Maximum power point tracking control of photovoltaic systems: A polynomial fuzzy model-based approach. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6(1), 292–299.

    Article  Google Scholar 

  • Renewables 2020. Available online: https://www.iea.org/reports/renewables-2020 (accessed on 1 March 2022).

  • Seyedmahmoudian, M., Kok Soon, T., Jamei, E., Thirunavukkarasu, G. S., 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.

    Article  Google Scholar 

  • Shaw, S. et al., (2021). The global circular economy for the electric power industry and opportunities for solar photovoltaics. In: 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), Fort Lauderdale, (pp.1594–1599). https://doi.org/10.1109/PVSC43889.2021.9518899.

  • Singh, N., Gupta, K. K., Jain, S. K., Dewangan, N. K., & Bhatnagar, P. (2020). A flying squirrel search optimization for MPPT under partial shaded photovoltaic system. IIEEE Journal of Emerging and Selected Topics in Power Electronics 9, 4963–4978.

    Article  Google Scholar 

  • Singh, S. (2021). Environmental energy harvesting techniques to power standalone IoT-equipped sensor and its application in 5G communication. Emerging Science Journal, 4, 116–126. https://doi.org/10.28991/esj-2021-SP1-08

    Article  Google Scholar 

  • Subramanian, A., & Jayaparvathy, R. (2022). Performance comparison of modified elephant herding optimization tuned MPPT for PV based solar energy systems. Circuit World, 48(3), 309–321.

    Article  Google Scholar 

  • Subudhi, B., & Pradhan, R. (2013). A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 4(1), 89–98.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhao, Z., Cheng, R., Yan, B., Zhang, J., Zhang, Z., Zhang, M., & Lai, L. L. (2020). A dynamic particles MPPT method for photovoltaic systems under partial shading conditions. Energy CConversion and Management 220, 113070.

    Article  Google Scholar 

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Funding

The authors wish to thank the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, for financial assistance provided under Grant number: EEQ/2021/000294.

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Correspondence to Suresh Mikkili.

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Table 2 PV module specifications

2

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Bonthagorla, P.K., Mikkili, S. A Novel Hybrid Slime Mould MPPT Technique for BL-HC Configured Solar PV System Under PSCs. J Control Autom Electr Syst 34, 782–795 (2023). https://doi.org/10.1007/s40313-023-00996-5

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  • DOI: https://doi.org/10.1007/s40313-023-00996-5

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