TY - GEN
T1 - Parameters identification of photovoltaic cell and module using LSHADE
AU - El-Abd, Mohammed
AU - Yu, Kunjie
AU - Ge, Shilei
N1 - El-Abd M., Yu K., and Ge S. (2020). Parameters Identification of Photovoltaic Cell and Module using LSHADE. The 12th International Conference on Advanced Computational Intelligence, 189-193.
PY - 2020/8
Y1 - 2020/8
N2 - Identifying circuit model parameters for photovoltaic cell and module is a challenging issue that often translates into an optimization problem. Recently, the most mainstream solution to such problems is based on metaheuristic optimization algorithms. Although there are many metaheuristic algorithms for the problem, the obtained parameters are often not very accurate and reliable. Therefore, a linear population reduction success-history based parameter adaptation for differential evolution (LSHADE) is applied to accurately and reliably identify the parameters of photovoltaic models. In LSHADE, the population size is continually decreased according to a linear function. The effectiveness of LSHADE is evaluated by identifying the parameters of the single diode model, the double diode model and the photovoltaic module model. The experimental results show that LSHADE outperforms other well-established parameters identification algorithms with respect to accuracy, stability, and rapidity.
AB - Identifying circuit model parameters for photovoltaic cell and module is a challenging issue that often translates into an optimization problem. Recently, the most mainstream solution to such problems is based on metaheuristic optimization algorithms. Although there are many metaheuristic algorithms for the problem, the obtained parameters are often not very accurate and reliable. Therefore, a linear population reduction success-history based parameter adaptation for differential evolution (LSHADE) is applied to accurately and reliably identify the parameters of photovoltaic models. In LSHADE, the population size is continually decreased according to a linear function. The effectiveness of LSHADE is evaluated by identifying the parameters of the single diode model, the double diode model and the photovoltaic module model. The experimental results show that LSHADE outperforms other well-established parameters identification algorithms with respect to accuracy, stability, and rapidity.
KW - Differential evolution
KW - Optimization method
KW - Parameters identification
KW - Photovoltaic model
UR - http://www.scopus.com/inward/record.url?scp=85092164200&partnerID=8YFLogxK
U2 - 10.1109/ICACI49185.2020.9177843
DO - 10.1109/ICACI49185.2020.9177843
M3 - Conference contribution
T3 - 12th International Conference on Advanced Computational Intelligence, ICACI 2020
SP - 189
EP - 193
BT - 12th International Conference on Advanced Computational Intelligence, ICACI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Advanced Computational Intelligence, ICACI 2020
Y2 - 14 August 2020 through 16 August 2020
ER -
