A New Modified Bacterial Foraging MPPT Technique with Dynamic Mutation Rates for Photovoltaic Systems under Partial Shading Conditions

Document Type : Original Article

Authors

1 Laboratory of Identification, Commande, Control and Communication (LI3CUB), University Mohamed KhiderBiskra, Biskra, Algeria

2 Electrical Engineering Department, LARHYSS Laboratory, University of Biskra, Biskra, Algeria

3 Institute of Automation and Infocommunication, University of Miskolc, Miskolc, Hungary

4 Laboratory of Applied and Automation and Industial Diagnostic (LAADI), University of Djelfa, Djelfa, Algeria

Abstract

This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from 81.31% to 89.39%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (0.4s to 0.9s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output.

Graphical Abstract

A New Modified Bacterial Foraging MPPT Technique with Dynamic Mutation Rates for Photovoltaic Systems under Partial Shading Conditions

Keywords

Main Subjects

References

  1. Xu S, Shao R, Cao B, Chang L. Single-phase grid-connected PV system with golden section search-based MPPT algorithm. Chinese Journal of Electrical Engineering. 2021;7(4):25-36. doi: 10.23919/CJEE.2021.000035.
  2. Balal AT, Jafarabadi YPT, Demir AT, Igene MT, Giesselmann MT, Bayne ST. Forecasting solar power generation utilizing machine learning models in lubbock. 2023.
  3. Fergani O, Bouzid A, Tkouti N, Mechgoug R, editors. A PSO Tuning ANN for Extracting the MPP from a DC Microgrid System under Changing Irradiance. 2023 24th International Carpathian Control Conference (ICCC); 2023: IEEE.
  4. Ayyad S, Baker MB, Handam A, Al-Smadi T. Reducing the Highway Networks Energy Bills using Renewable Energy System. Civil Engineering Journal. 2023;9(11):2914-26.
  5. Belhachat F, Larbes C. PV array reconfiguration techniques for maximum power optimization under partial shading conditions: A review. Solar Energy. 2021;230:558-82. doi.org/10.1016/j.solener.2021.09.089 
  6. Yang B, Zhu T, Wang J, Shu H, Yu T, Zhang X, et al. Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition. Journal of Cleaner Production. 2020;268:121983.
  7. Arifin Z, Khairunisa N, Kristiawan B, Prasetyo SD, Bangun WB. Performance Analysis of Nanofluid-based Photovoltaic Thermal Collector with Different Convection Cooling Flow. Civil Engineering Journal. 2023;9(8):1922-35.
  8. Chtita S, Motahhir S, El Hammoumi A, Chouder A, Benyoucef AS, El Ghzizal A, et al. A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions. Scientific Reports. 2022;12(1):10637. doi.org/10.1038/s41598-022-14733-6
  9. Bhukya L, Kedika NR, Salkuti SR. Enhanced maximum power point techniques for solar photovoltaic system under uniform insolation and partial shading conditions: a review. Algorithms. 2022;15(10):365. doi.org/10.3390/a15100365 
  10. Al-Ezzi AS, Ansari MNM. Photovoltaic solar cells: a review. Applied System Innovation. 2022;5(4):67.
  11. Abbassi A, Ben Mehrez R, Bensalem Y, Abbassi R, Kchaou M, Jemli M, et al. Improved arithmetic optimization algorithm for parameters extraction of photovoltaic solar cell single-diode model. Arabian Journal for Science and Engineering. 2022;47(8):10435-51.
  12. Senthilkumar S, Mohan V, Mangaiyarkarasi S, Karthikeyan M. Analysis of single-diode PV model and optimized MPPT model for different environmental conditions. International Transactions on Electrical Energy Systems. 2022;2022. doi.org/10.1155/2022/4980843
  13. Mechgoug R, Tkouti N, Okba F. A Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller for a 9-Level Inverter for Grid-Connected PV Systems. NeuroQuantology. 2023;21(6):1120-34. doi: 10.48047/nq.2023.21.6.NQ23117
  14. Premkumar M, Shankar N, Sowmya R, Jangir P, Kumar C, Abualigah L, et al. A reliable optimization framework for parameter identification of single‐diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm and Lambert‐W function. IET Renewable Power Generation. 2023;17(11):2711-32. doi.org/10.1049/rpg2.12792
  15. Terrang CD, Sodipo BK, Abubakar MS. PV module single–diode model, parameter extraction of polycrystalline and amorphous solar panel. Science World Journal. 2023;18(2):301-7. doi : 10.4314/swj.v18i2.20
  16. Gholami A, Ameri M, Zandi M, Ghoachani RG. Electrical, thermal and optical modeling of photovoltaic systems: Step-by-step guide and comparative review study. Sustainable Energy Technologies and Assessments. 2022;49:101711.
  17. Gevorkov L, Domínguez-García JL, Romero LT. Review on solar photovoltaic-powered pumping systems. Energies. 2022;16(1):94.
  18. Fergani Okba RM, Ahmed Bouzid Afulay, NaciraTkouti. Revolutionizing PV Pumping Systems with PMSM Machine and Advanced MPPT Algorithm Integration: A Comparative Study of PSO, GWO, and CSA Techniques. NeuroQuantology. 2023;21(6):1852-62. 10.48047/nq.2023.21.6.nq23185 10.48047/nq.2023.21.6.nq23185
  19. Vega-Garita V, Alpizar-Gutierrez V, Alpízar-Castillo J. A practical method for considering shading on photovoltaics systems energy yield. Energy Conversion and Management: X. 2023;20:100412.
  20. Rezazadeh S, Moradzadeh A, Pourhossein K, Akrami M, Mohammadi-Ivatloo B, Anvari-Moghaddam A. Photovoltaic array reconfiguration under partial shading conditions for maximum power extraction: A state-of-the-art review and new solution method. Energy Conversion and Management. 2022;258:115468. doi.org/10.1016/j.enconman.2022.115468
  21. Heidari H, Tarafdar Hagh M. Optimal reconfiguration of solar photovoltaic arrays using a fast parallelized particle swarm optimization in confront of partial shading. International Journal of Engineering. 2019;32(8):1177-85. 10.5829/IJE.2019.32.08B.14
  22. Rupesh M, Vishwanath T. Intelligent controllers to extract maximum power for 10 kw photovoltaic system. International Journal of Engineering. 2022;35(4):784-93.
  23. Oufettoul H, Lamdihine N, Motahhir S, Lamrini N, Abdelmoula IA, Aniba G. Comparative performance analysis of PV module positions in a solar PV array under partial shading conditions. IEEE Access. 2023;11:12176-94.
  24. Dhimish M, Badran G, editors. Field Study of Photovoltaic Systems with Anti-Potential-Induced-Degradation Mechanism: UVF, EL, and Performance Ratio Investigations. Photonics; 2023: MDPI.
  25. Dhimish M, Tyrrell AM. Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks. IEEE Transactions on Instrumentation and Measurement. 2023;72:1-10. .doi:10.1109/TIM.2023.3244230.
  26. Vumbugwa M, Vorster F, McCleland JC, van Dyk E. Effects of changing partial cell shading on the electrical and thermal characteristics of crystalline silicon photovoltaic module. Solar Energy. 2022;240:147-56.
  27. Raina G, Sinha S. A comprehensive assessment of electrical performance and mismatch losses in bifacial PV module under different front and rear side shading scenarios. Energy Conversion and Management. 2022;261:115668.
  28. Osmani K, Haddad A, Jaber H, Lemenand T, Castanier B, Ramadan M. Mitigating the effects of partial shading on PV system’s performance through PV array reconfiguration: A review. Thermal Science and Engineering Progress. 2022;31:101280.
  29. Bronneberg D, Reinders A, Frijns A, Debije M. Effects of shading and temperature on the energy performance of curved crystalline silicon PV modules with different topologies at a sub-module level. 2022.
  30. Yadav D, Singh N. Performance Evaluation of Basic, Modified, and Advanced DC-DC Boost Converters Used with PV System. Sustainable Technology and Advanced Computing in Electrical Engineering: Proceedings of ICSTACE 2021: Springer; 2022. p. 325-49.
  31. Kumar LA, Maheswari YU. Electromagnetic Interference and Electromagnetic Compatibility: Principles, Design, Simulation, and Applications: CRC Press; 2023.
  32. Rajabi A, Rajaei A, Tehrani VM, Dehghanian P, Guerrero JM, Khan B. A non-isolated high step-up DC-DC converter using voltage lift technique: analysis, design, and implementation. IEEE Access. 2022;10:6338-47.
  33. Fİdan Ş, Sevİm D, Erkan E, editors. System Identification and Control of High Voltage Boost Converter. 2022 Global Energy Conference (GEC); 2022: IEEE.
  34. Rahmani B, Belkheiri M. Adaptive neural network output feedback control for flexible multi-link robotic manipulators. International Journal of Control. 2018;92(10):2324-38. 10.1080/00207179.2018.1436774
  35. Khan F, Zaid M, Tariq A, Khan MMA. A new non-isolated high-gain DC-DC converter for the PV application. e-Prime-Advances in Electrical Engineering, Electronics and Energy. 2023;5:100198.
  36. Aragon D, Unamuno E, Ceballos S, Barrena J. Comparative small-signal evaluation of advanced grid-forming control techniques. Electric Power Systems Research. 2022;211:108154.
  37. Belboul Z, Toual B, Kouzou A, Bensalem A, editors. Optimal sizing of hybrid PV/wind/battery/diesel microgrid system using a multi-objective grasshopper optimization algorithm: A case study in Djelfa City Algeria. 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE); 2022: IEEE.
International Journal of Engineering
Volume 37, Issue 8
TRANSACTIONS B: Applications
August 2024
Pages 1569-1579

Files

Share

How to cite

Statistics