TY - GEN
T1 - Model Parameter Extraction of Solar PV Cell Using Gold Rush Optimizer
AU - Khajuria, Rahul
AU - Sharma, Pankaj
AU - Lamba, Ravita
AU - Kumar, Rajesh
AU - Raju, Saravanakumar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In this article, a recently developed metaheuristic optimization algorithm, Gold Rush Optimizer (GRO) is applied in extracting five parameters of solar photovoltaic (PV) cells. The mathematical model of a PV cell is considered to be a highly complex and non-linear and extraction of model parameters is found to be the multi-modal and multivariate problem. It is difficult to solve this problem using conventional methods. Metaheuristic algorithms have the advantage of solving this problem of parameter extraction. Therefore, in this paper, GRO with Newton Raphson (NR) method has been used to extract the parameters and is compared with four state-of-art algorithms such as grey wolf optimization (GWO), harris hawk optimization (HHO), bald eagle search (BES), and whale optimization algorithm (WOA). The parameter extraction problem is characterized by root mean squared error (RMSE)-based objective function. Two case studies have been considered to evaluate GRO’s effectiveness. Results reveal that with RMSE value of 7.72E-4 and 1.59E-04 for case study 1 and 2 respectively GRO identifies solar PV parameters accurately. Also, the algorithm’s accuracy has been checked through the closeness between estimated and experimental I-V characteristics for both case studies. Moreover, convergence curves have been plotted to evaluate the convergence speed, and the effectiveness is evaluated using statistical study including min, standard deviation, and mean of RMSE value. It is observed that GRO extracted model parameters of solar PV cell accurately and statistical study reveals that GRO outperforms among other algorithms.
AB - In this article, a recently developed metaheuristic optimization algorithm, Gold Rush Optimizer (GRO) is applied in extracting five parameters of solar photovoltaic (PV) cells. The mathematical model of a PV cell is considered to be a highly complex and non-linear and extraction of model parameters is found to be the multi-modal and multivariate problem. It is difficult to solve this problem using conventional methods. Metaheuristic algorithms have the advantage of solving this problem of parameter extraction. Therefore, in this paper, GRO with Newton Raphson (NR) method has been used to extract the parameters and is compared with four state-of-art algorithms such as grey wolf optimization (GWO), harris hawk optimization (HHO), bald eagle search (BES), and whale optimization algorithm (WOA). The parameter extraction problem is characterized by root mean squared error (RMSE)-based objective function. Two case studies have been considered to evaluate GRO’s effectiveness. Results reveal that with RMSE value of 7.72E-4 and 1.59E-04 for case study 1 and 2 respectively GRO identifies solar PV parameters accurately. Also, the algorithm’s accuracy has been checked through the closeness between estimated and experimental I-V characteristics for both case studies. Moreover, convergence curves have been plotted to evaluate the convergence speed, and the effectiveness is evaluated using statistical study including min, standard deviation, and mean of RMSE value. It is observed that GRO extracted model parameters of solar PV cell accurately and statistical study reveals that GRO outperforms among other algorithms.
KW - Gold rush optimizer
KW - I-V characteristics
KW - Parameter extraction
KW - Solar PV cell
KW - Statistical study
UR - http://www.scopus.com/inward/record.url?scp=85205965570&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-5419-9_15
DO - 10.1007/978-981-97-5419-9_15
M3 - Conference contribution
AN - SCOPUS:85205965570
SN - 9789819754182
T3 - Green Energy and Technology
SP - 163
EP - 173
BT - Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research
A2 - Tatiparti, Sankara Sarma V.
A2 - Seethamraju, Srinivas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Advances in Energy Research, ICAER 2023
Y2 - 12 December 2023 through 14 December 2023
ER -