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Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method

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  • Pan, Jeng-Shyang
  • Tian, Ai-Qing
  • Snášel, Václav
  • Kong, Lingping
  • Chu, Shu-Chuan

Abstract

The simulation, control and optimization of photovoltaic (PV) modules require the extraction of parameters from actual data and the construction of highly accurate PV cells. Multiple PV modules supplying power to a common load is the most common form of power distribution in PV systems. In these PV systems, providing separate maximum power point tracking (MPPT) technology for each PV module would increase the cost of the entire system. Determining how to accurately identify the internal parameter information of the PV modules and control the MPPT technology is the problem solved in this paper. we proposes an improved pigeon-inspired optimization (PIO) algorithm based on Taguchi method to solve the above problems. In this paper, we use the CEC2014 test library for testing and cross-sectional comparison. Experimental results show that the PIO algorithm based on Taguchi method is more competitive than other algorithms. The proposed algorithm uses measurement data to extract the unknown parameter in the PV modules and then uses this information to optimize the MPPT of all PV systems under partially shaded conditions (PSCs). Simulation results demonstrate the fitness value of the unknown parameters extracted by TPIO is 9.7525 × 10−4, which is better than the compared algorithms.

Suggested Citation

  • Pan, Jeng-Shyang & Tian, Ai-Qing & Snášel, Václav & Kong, Lingping & Chu, Shu-Chuan, 2022. "Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007666
    DOI: 10.1016/j.energy.2022.123863
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    1. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    2. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    3. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & El-Fergany, Attia & Elsayed, Abdallah M., 2023. "Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer," Energy, Elsevier, vol. 283(C).
    4. Ragb, Ola & Bakr, Hanan, 2023. "A new technique for estimation of photovoltaic system and tracking power peaks of PV array under partial shading," Energy, Elsevier, vol. 268(C).
    5. Refaat, Ahmed & Ali, Qays Adnan & Elsakka, Mohamed Mohamed & Elhenawy, Yasser & Majozi, Thokozani & Korovkin, Nikolay V. & Elfar, Medhat Hegazy, 2024. "Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions," Renewable Energy, Elsevier, vol. 220(C).
    6. Muhammad Jamshed Abbass & Robert Lis & Faisal Saleem, 2023. "The Maximum Power Point Tracking (MPPT) of a Partially Shaded PV Array for Optimization Using the Antlion Algorithm," Energies, MDPI, vol. 16(5), pages 1-13, March.

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