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Photovoltaic array reconfiguration method based on fuzzy logic and recursive least squares: An experimental validation

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  • Bouselham, Loubna
  • Rabhi, Abdelhamid
  • Hajji, Bekkay
  • Mellit, Adel

Abstract

In this paper, an experimental analysis and validation of a simple reconfigurable photovoltaic (PV) array is carried out. An assessment of a new reconfiguration method based on fuzzy logic (FL) under partial shading conditions is introduced. Furthermore, a recursive least squares based irradiance estimator is proposed aiming to reduce the investment cost of the dynamic PV array. An experimental comparison with other estimators showed the high precision of the proposed estimator. The estimation error has decreased by an average of 10% compared to the first estimator (based on the PV current and voltage measurement) and by 4.28%compared to the second estimator(based on the PV current measurement). On the other hand, the results validated the FL Controller ability to switch to the appropriate configuration under prevailing shading conditions. The method was tested for a simple configuration, however it could be generalized for small-scale configurations as residential house (average power output equal to 5 kWh). To evaluate the performance of the FL method an extended simulation of dynamic PV array of 16 PV modules is also realized. The mismatch loss is mitigated by nearly 50% compared to fixed Total-Cross-Tied and 8% compared to basic Irradiance Equalization techniques.

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  • Bouselham, Loubna & Rabhi, Abdelhamid & Hajji, Bekkay & Mellit, Adel, 2021. "Photovoltaic array reconfiguration method based on fuzzy logic and recursive least squares: An experimental validation," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221013554
    DOI: 10.1016/j.energy.2021.121107
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    1. Aljafari, Belqasem & Satpathy, Priya Ranjan & Thanikanti, Sudhakar Babu, 2022. "Partial shading mitigation in PV arrays through dragonfly algorithm based dynamic reconfiguration," Energy, Elsevier, vol. 257(C).
    2. Zhang, Xiaoshun & Meng, Die & Cai, Jiahui & Zhang, Guiyuan & Yu, Tao & Pan, Feng & Yang, Yuyao, 2023. "A swarm based double Q-learning for optimal PV array reconfiguration with a coordinated control of hydrogen energy storage system," Energy, Elsevier, vol. 266(C).
    3. Alharbi, Abdullah G. & Fathy, Ahmed & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2023. "An efficient war strategy optimization reconfiguration method for improving the PV array generated power," Energy, Elsevier, vol. 283(C).
    4. Fathy, Ahmed & Yousri, Dalia & Babu, Thanikanti Sudhakar & Rezk, Hegazy, 2023. "Triple X Sudoku reconfiguration for alleviating shading effect on total-cross-tied PV array," Renewable Energy, Elsevier, vol. 204(C), pages 593-604.
    5. Tuyen Nguyen-Duc & Thinh Le-Viet & Duong Nguyen-Dang & Tung Dao-Quang & Minh Bui-Quang, 2022. "Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network," Energies, MDPI, vol. 15(17), pages 1-21, August.
    6. Singh, Rashmi & Sharma, Madhu & Yadav, Kamlesh, 2022. "Degradation and reliability analysis of photovoltaic modules after operating for 12 years: A case study with comparisons," Renewable Energy, Elsevier, vol. 196(C), pages 1170-1186.

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