Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

. 2024 Oct 30 ; 14 (1) : 26047. [epub] 20241030

Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39472710
Odkazy

PubMed 39472710
PubMed Central PMC11522394
DOI 10.1038/s41598-024-77488-2
PII: 10.1038/s41598-024-77488-2
Knihovny.cz E-zdroje

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA's performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed-approaching the maximum Power Point 10-15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm's overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.

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Douifi, N. et al. A Novel MPPT based reptile search algorithm for photovoltaic system under various conditions. Appl. Sci.13(8), 4866 (2023).

Awan, M. M. A., Javed, M. Y., Asghar, A. B., Ejsmont, K. & Zia-ur-Rehman,. Economic integration of renewable and conventional power sources—a case study. Energies15, 2141. 10.3390/en15062141 (2022).

Ali, Z. M. et al. Novel hybrid improved bat algorithm and fuzzy system based MPPT for photovoltaic under variable atmospheric conditions. Sustain. Energy Technol. Assess.52, 102156 (2022).

Belmadani, H. et al. A new fast and efficient MPPT algorithm for partially shaded PV systems using a hyperbolic slime mould algorithm. Int. J. Energy Res.2024, 1–26 (2024).

Awan, M. M. A., Javed, M. Y., Asghar, A. B. & Ejsmont, K. Performance optimization of a ten check MPPT algorithm for an off-grid solar photovoltaic system. Energies15, 2104. 10.3390/en15062104 (2022).

Abbass, M. J., Lis, R. & Saleem, F. The maximum power point tracking (MPPT) of a partially shaded PV array for optimization using the antlion algorithm. Energies16 (5), 2380 (2023).

Alshareef, M. J. An effective falcon optimization algorithm based MPPT under partial shaded photovoltaic systems. IEEE Access10, 131345–131360 (2022).

Afzal Awan, M. M. & Mahmood, T. A novel ten check maximum power point tracking algorithm for a standalone solar photovoltaic system. Electronics7, 327. 10.3390/electronics7110327 (2018).

Shiau, J. K., Wei, Y. C. & Chen, B. C. A study on the fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Algorithms8 (2), 100–127 (2015).

Naser, T., Mohammed, K. K., Ab Aziz, N. F., Kamil, K. & Mekhilef, S. Improved coot optimizer algorithm-based MPPT for PV systems under complex partial shading conditions and load variation. Energy Convers. Management: X22, 100565 (2024).

Awan, M. M. A., Asghar, A. B., Javed, M. Y. & Conka, Z. Ordering technique for the maximum power point tracking of an islanded solar photovoltaic system. Sustainability15(4), 3332. 10.3390/su15043332 (2023).

Ramli, M. A., Twaha, S., Ishaque, K. & Al-Turki, Y. A. A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renew. Sustain. Energy Rev.67, 144–159 (2017).

Jately, V., Azzopardi, B., Joshi, J., Sharma, A. & Arora, S. Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels. Renew. Sustain. Energy Rev.150, 111467 (2021).

Toumi, I., Boulmaiz, A., Meghni, B. & Hachana, O. Robust variable step P&O algorithm based MPPT for PMSG wind generation system using estimated wind speed compensation technique. Sustain. Energy Technol. Assess60, 103420 (2023).

Femia, N., Petrone, G., Spagnuolo, G. & Vitelli, M. Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron.20 (4), 963–973 (2005).

Naeem, U. & Awan, M. M. A. Maximizing off-grid solar photovoltaic system efficiency through cutting-edge performance optimization technique for incremental conductance algorithm. Mehran Univ. Res. J. Eng. Technol.43 (3), 113–125 (2024).

Pal, R., Sankar, & Mukherjee, V. Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition. Energy212, 118592 (2020).

Kalaiarasi, N. et al. Performance evaluation of various Z-source inverter topologies for PV applications using AI-based MPPT techniques. Int. Trans. Electr. Energy Syst.2023, 1134633 (2023).

Deghfel, N. et al. A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems. Sci. Rep.14, 6827. 10.1038/s41598-024-57610-0 (2024). PubMed PMC

Naoussi, S. R. D. et al. Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains. Sci. Rep.14, 3334. 10.1038/s41598-024-53721-w (2024). PubMed PMC

Zaghba, L. et al. Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions. Sci. Rep.14, 8205. 10.1038/s41598-024-59024-4 (2024). PubMed PMC

Mohapatra, B. et al. Optimizing grid-connected PV systems with novel super-twisting sliding mode controllers for real-time power management. Sci. Rep.14, 4646. 10.1038/s41598-024-55380-3 (2024). PubMed PMC

Guermoui, M. et al. An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques. Sci. Rep.14, 6653. 10.1038/s41598-024-57398-z (2024). PubMed PMC

Sahoo, G. K., Choudhury, S., Rathore, R. S., Bajaj, M. & Dutta, A. K. Scaled Conjugate-Artificial Neural Network-Based novel framework for enhancing the power quality of Grid-Tied Microgrid systems. Alex. Eng. J.80, 520–541 (2023).

Bouguerra, A. et al. Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo search MPPT techniques in dynamically operating environments. Sci. Rep.14, 13946. 10.1038/s41598-024-64915-7 (2024). PubMed PMC

Harrison, A. et al. Enhanced control strategy for photovoltaic emulator operating in continuously changing environmental conditions based on shift methodology. Sci. Rep.14, 13406. 10.1038/s41598-024-64092-7 (2024). PubMed PMC

Rekioua, D. et al. Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms. Sci. Rep.14, 10267. 10.1038/s41598-024-60116-4 (2024). PubMed PMC

Al-Majidi, S. D., Abbod, M. F. & Al-Raweshidy, H. S. A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems. Int. J. Hydrog. Energy. 43 (31), 14158–14171 (2018).

Villegas-Mier, C. G. et al. Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: A review. Micromachines12 (10), 1260 (2021). PubMed PMC

Chtita, S., Derouich, A., Motahhir, S. & Ghzizal, A. E. A new MPPT design using arithmetic optimization algorithm for PV energy storage systems operating under partial shading conditions. Energy. Conv. Manag.289, 117197 (2023).

Pal, R., Sankar & Mukherjee, V. A novel population based maximum point tracking algorithm to overcome partial shading issues in solar photovoltaic technology. Energy. Conv. Manag.244, 114470 (2021).

Mukti, E. W., Risdiyanto, A., Kristi, A. A. & Darussalam, R. Particle swarm optimization (PSO) based photovoltaic MPPT algorithm under the partial shading condition. Jurnal Elektronika dan. Telekomunikasi23(2), 99–107 (2023).

Regaya, C. B. et al. Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems. ISA Trans.146, 496–510 (2024). PubMed

Mohanty, S., Subudhi, B. & Ray, P. K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans. Sustain. Energy. 7 (1), 181–188 (2015).

Gundogdu, H., Demirci, A., Tercan, S. M. & Cali, U. A novel improved grey wolf algorithm based global maximum power point tracker method considering partial shading. IEEE Access (2024). Accessed 25 Jun 2024.

Yang, B. et al. Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions. Energy. Conv. Manag.292, 117410 (2023).

Qi, P. et al. Novel global MPPT technique based on hybrid cuckoo search and artificial bee colony under partial-shading conditions. Electronics13(7), 1337 (2024).

Xia, K., Li, Y. & Zhu, B. Improved photovoltaic MPPT algorithm based on ant colony optimization and fuzzy logic under conditions of partial shading. IEEE Access (2024). Accessed: Jun. 25, 2024.

Kumar, B. & Kumar, A. A novel adaptive flower pollination algorithm for maximum power tracking of photovoltaic systems under dynamic shading conditions. Iran J. Sci. Technol. Trans. Electr. Eng.48(2), 859–875 (2024).

Basalamah, et al. Comparing MPPT algorithms for improved partial-shaded PV power generations. Jurnal Nasional Teknik Elektro 73–81 (2023).

Aouchiche, N., Aitcheikh, M. S., Becherif, M. & Ebrahim, M. A. AI-based global MPPT for partial shaded grid connected PV plant via MFO approach. Sol. Energy. 171, 593–603 (2018).

Seyedmahmoudian, M. et al. Maximum power point tracking for photovoltaic systems under partial shading conditions using bat algorithm. Sustainability. 10 (5), 1347 (2018).

Khan, M. K., Zafar, M. H., Riaz, T., Mansoor, M. & Akhtar, N. Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm. Energy Convers. Management: X. 21, 100509 (2024).

Sugavanam, K. R. et al. MPPT in partially shaded PV system with the use of WODE technique, in Advances in Greener Energy Technologies (eds. Bhoi, A. K., Sherpa, K. S., Kalam, A. & G.-S. Chae) 795–806  (Springer Singapore, 2020).

Mariprasath, T., Basha, C. H., Khan, B. & Ali, A. A novel on high voltage gain boost converter with cuckoo search optimization based MPPT Controller for solar PV system. Sci. Rep.14 (1), 8545 (2024). PubMed PMC

Li, Y. et al. A novel hybrid maximum power point tracking technique for PV system under complex partial shading conditions in campus microgrid. Appl. Sci.13(8), 4998 (2023).

Kumar, N., Hussain, I., Singh, B. & Panigrahi, B. K. MPPT in dynamic condition of partially shaded PV system by using WODE technique. IEEE Trans. Sustain. Energy. 8 (3), 1204–1214 (2017).

Abdelmalek, F. et al. Comparison between MPPTs for PV systems using P&O and Grey Wolf controllers, in 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) 1–5 (IEEE, 2023). Accessed: Sep. 19, 2024.

Dhimish, M. Assessing MPPT techniques on hot-spotted and partially shaded photovoltaic modules: Comprehensive review based on experimental data. IEEE Trans. Electron. Devices. 66 (3), 1132–1144 (2019).

Chen, Q., Wang, L., Xie, Y. S. S. & Wang, R. Adaptive integral sliding mode MPPT control for wind turbines with fixed-time convergence. IET Renew. Power Gener., (2024).

Dhimish, M. & Tyrrell, A. M. Power loss and hotspot analysis for photovoltaic modules affected by potential induced degradation. npj Mater. Degrad.6 (1), 11 (2022).

Ha, H. L. D., Gopal, L., Chiong, C. W. R., Juwono, F. H. & Law, K. H. A novel artificial location selection optimization for global maximum power point tracking under partial shading conditions. Energy. Conv. Manag.304, 118218 (2024).

Refaat, A. E. et al. A novel metaheuristic MPPT technique based on enhanced autonomous group particle swarm optimization algorithm to track the GMPP under partial shading conditions-experimental validation. Energy. Conv. Manag.287, 117124 (2023).

Bisht, R. & Sikander, A. A novel hybrid architecture for MPPT of PV array under partial shading conditions. Soft. Comput.28 (2), 1351–1365 (2024).

Ilyas, M. R., Khan & Ayyub, M. Lookup table based modeling and simulation of solar photovoltaic system, in Annual IEEE India Conference (INDICON) 1–6 (IEEE, 2015) Accessed: Jun. 25, 2024.

Trojovská, E., Dehghani, M. & Trojovskỳ, P. Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access10, 49445–49473 (2022).

Jiedeerbieke, M., Li, T., Chao, Y., Qi, H. & Lin, C. Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM. IEEE Access, (2024). Accessed: Jun. 25, 2024.

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