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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국전기전자재료학회 Transactions on Electrical and Electronic Materials Transactions on Electrical and Electronic Materials 제22권 제6호
- 발행연도
- 2021.12
- 수록면
- 869 - 888 (20page)
- DOI
- https://doi.org/10.1007/s42341-021-00312-5
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초록· 키워드
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The word’s demand for renewable energy has be rinsing incrementally. One of the solutions for the energy crisis is photovoltaic. However, the design and development of better performing photovoltaic cells and modules requires accurate extraction of their intrinsic parameters. Metaheuristic algorithms have been reported to be the best methods for obtaining accurate values of these intrinsic parameters. However, local convergence goes against the recently devised heuristic methods and inhibits them from producing optimal result. This paper proposes a hybrid method that is based on the Newton Raphson method and a self-adaptive algorithm called the Drone Squadron Optimisation. The latter is an artifact technique inspired by the simulation of a drone squadron from a command centre. It is proposed that this hybrid method can help extract the best intrinsic parameters of photovoltaic cell and module. This study also provides insights and clarifi cation on the reported approaches that have been recently proposed to formulate the objective function. Further, this study also computes and compares the ten best recently published heuristics algorithms in the domain of photovoltaic estimation. The study’s results obtain point to the diff erence between the two formulations and the accuracy of the best formulation. The results obtained from the six case studies covered in this study present the combined performance of the Newton Raphson method and Drone Squadron Optimisation to extract the accurate parameters of a photovoltaic module.
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참고문헌 (53)
참고문헌 신청
Zhi-Feng Liu / 2020 / Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model / Journal of Cleaner Production 248:119272
Patrick Juvet Gnetchejo / 2019 / Important notes on parameter estimation of solar photovoltaic cell / Energy Conversion and Management 197:111870
Guo-Qian Lin / 2020 / An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation / Journal of Cleaner Production 253:119966
Zhicong Chen / 2019 / Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics / Energy Conversion and Management 186:168~187
Patrick Juvet Gnetchejo / 2019 / Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System / Journal of Power and Energy Engineering 07(08):1~26
Patrick Juvet Gnetchejo / 2019 / Important notes on parameter estimation of solar photovoltaic cell / Energy Conversion and Management 197:111870
Guo-Qian Lin / 2020 / An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation / Journal of Cleaner Production 253:119966
Zhicong Chen / 2019 / Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics / Energy Conversion and Management 186:168~187
Patrick Juvet Gnetchejo / 2019 / Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System / Journal of Power and Energy Engineering 07(08):1~26
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