TY - GEN
T1 - A Dense Explorative ElectroStatic Discharge optimization Algorithm for Photovoltaic Parameters Estimation
AU - Antoniadis, Charalampos
AU - Massoud, Yehia Mahmoud
N1 - KAUST Repository Item: Exported on 2022-09-14
PY - 2022/8/22
Y1 - 2022/8/22
N2 - Photovoltaic (PV) systems are becoming one of the most emerging systems for power generation owing to their low cost and clean operation. Due to the high demand for PVs in several small and large-scale applications, their accurate modeling is essential in power systems simulation to obtain reliable results. In this work, an improved ElectroStatic Discharge Algorithm with Dense Explorative Search (ESDADES) is proposed that estimates the parameters of the most popular PV cell models with more accuracy than previous works. More specifically, the dense explorative search proposed in this Work enhances the ESDA capability to search around the regions closer to the best solution found by ESDA more extensively, thus improving convergence accuracy. The proposed ESDADES Was compared to two recently proposed optimization algorithms, namely the Self-adaptive Ensemble-based Differential Evolution (SEDE), the Directional Permutation Differential Evolution (DPDE), and the simple ESDA. The experimental results demonstrated that the proposed algorithm arrives faster at more accurate estimates of the examined PV cell models parameters.
AB - Photovoltaic (PV) systems are becoming one of the most emerging systems for power generation owing to their low cost and clean operation. Due to the high demand for PVs in several small and large-scale applications, their accurate modeling is essential in power systems simulation to obtain reliable results. In this work, an improved ElectroStatic Discharge Algorithm with Dense Explorative Search (ESDADES) is proposed that estimates the parameters of the most popular PV cell models with more accuracy than previous works. More specifically, the dense explorative search proposed in this Work enhances the ESDA capability to search around the regions closer to the best solution found by ESDA more extensively, thus improving convergence accuracy. The proposed ESDADES Was compared to two recently proposed optimization algorithms, namely the Self-adaptive Ensemble-based Differential Evolution (SEDE), the Directional Permutation Differential Evolution (DPDE), and the simple ESDA. The experimental results demonstrated that the proposed algorithm arrives faster at more accurate estimates of the examined PV cell models parameters.
UR - http://hdl.handle.net/10754/680508
UR - https://ieeexplore.ieee.org/document/9859366/
U2 - 10.1109/MWSCAS54063.2022.9859366
DO - 10.1109/MWSCAS54063.2022.9859366
M3 - Conference contribution
SN - 978-1-6654-0280-4
BT - 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)
PB - IEEE
ER -
