TY - GEN
T1 - Fractional Order Differential Evolution to Solve Parameter Estimation Problem of Solar Photovoltaic Models
AU - Wang, Kaiyu
AU - Zhou, Meng Chu
AU - Yang, Jiaru
AU - Liu, Sicheng
AU - Gao, Shangce
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Parameter estimation problem (PEP) in photovoltaic (PV) systems is crucial for maximizing the utilization of solar energy in PV power systems. In this study, we employ fractional order differential evolution (FODE) to address PEP of various solar PV models. FODE operates on a bi-strategy co-deployment framework and is enhanced with fractional-order difference vectors, allowing for comprehensive utilization of historical population information. To assess the efficacy of FODE, we conduct six sets of experiments encompassing single, dual, and triple diode models, as well as PV module models. FODE is benchmarked against ten other representative algorithms. The experimental findings demonstrate that FODE outperforms all other algorithms in addressing PV system PEP.
AB - Parameter estimation problem (PEP) in photovoltaic (PV) systems is crucial for maximizing the utilization of solar energy in PV power systems. In this study, we employ fractional order differential evolution (FODE) to address PEP of various solar PV models. FODE operates on a bi-strategy co-deployment framework and is enhanced with fractional-order difference vectors, allowing for comprehensive utilization of historical population information. To assess the efficacy of FODE, we conduct six sets of experiments encompassing single, dual, and triple diode models, as well as PV module models. FODE is benchmarked against ten other representative algorithms. The experimental findings demonstrate that FODE outperforms all other algorithms in addressing PV system PEP.
KW - Differential evolution
KW - Fractional Order
KW - Parameter Estimation Problem
KW - Solar Photovoltaic Model
UR - http://www.scopus.com/inward/record.url?scp=85202641383&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7181-3_17
DO - 10.1007/978-981-97-7181-3_17
M3 - 会議への寄与
AN - SCOPUS:85202641383
SN - 9789819771806
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 211
EP - 222
BT - Advances in Swarm Intelligence - 15th International Conference on Swarm Intelligence, ICSI 2024, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Swarm Intelligence, ICSI 2024
Y2 - 23 August 2024 through 26 August 2024
ER -
