Abstract
Due to the environmental threat toward pollution and continuous depletion in fossil fuels, the world has found its alternate source of energy generation through pollution-free solar energy. Due to the scope in solar PV systems, the research interest has considerably increased. In solar PV research thrusts, the accurate modeling of solar I-V characteristics is given prior importance. In this article, the authors have proposed a new FPA-based solar PV parameter extraction. To appreciate the accuracy in the computation, two diode models are preferred. The authors have used RTC France data to experiment with the effectiveness of FPA. Further, the computed root mean square error and relative error for the designed model is compared with the existing Simulated Annealing (SA), Pattern Search (PS), Harmony Search (HS), and Artificial Bee Swarm Optimization (ABSO) techniques.
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Acknowledgements
The authors thank the VIT management for providing the necessary support to carry out the research work. Further, the authors sincerely acknowledge the article ‘https://ieeexplore.ieee.org/document/7584047’ for being the major reference to carry out this research work.
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Prasanth Ram, J., Pillai, D.S., Rajasekar, N., Kumar Chinnaiyan, V. (2020). Flower Pollination Based Solar PV Parameter Extraction for Double Diode Model. In: Kalam, A., Niazi, K., Soni, A., Siddiqui, S., Mundra, A. (eds) Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 607. Springer, Singapore. https://doi.org/10.1007/978-981-15-0214-9_34
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DOI: https://doi.org/10.1007/978-981-15-0214-9_34
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