# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 5 Conclusions
**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{1}{c}{**SD**} \\ \hline \multirow{5}{*}{SDM} & TFWO & 0.000636657 & 0.000776307 & 0.000643757 & 2.76367 \(\times\)\(10^{-5}\) \\  & MRFO & 0.003374264 & 0.015283988 & 0.01143509 & 0.003320893 \\  & BSDE & 0.003467884 & 0.014320685 & 0.010188693 & 0.002289554 \\  & MPA & 0.0148696 & 0.048448767 & 0.039118106 & 0.010156791 \\  & EO & 0.002888472 & 0.01320854 & 0.009771334 & 0.002376063 \\  & CSO & 0.015480743 & 0.023739498 & 0.019620651 & 0.002077078 \\  & GWO & 0.023476598 & 0.468609138 & 0.164042826 & 0.180451636 \\  & BSA & 0.009775873 & 0.020577736 & 0.015024514 & 0.002330391 \\ \hline \multirow{5}{*}{DDM} & TFWO & 0.000464919 & 0.003991719 & 0.000784157 & 0.000677807 \\  & MRFO & 0.008229492 & 0.017508428 & 0.013106997 & 0.002099435 \\  & BSDE & 0.009849963 & 0.029252608 & 0.016694172 & 0.004448939 \\  & MPA & 0.01025436 & 0.049871846 & 0.035790405 & 0.012557606 \\  & EO & 0.002599915 & 0.013710246 & 0.009972209 & 0.002673846 \\  & CSO & 0.004212906 & 0.025007328 & 0.017745851 & 0.004339108 \\  & GWO & 0.009625309 & 0.467884375 & 0.149515185 & 0.179605814 \\  & BSA & 0.009625725 & 0.026600837 & 0.017452567 & 0.003984149 \\ \hline \multirow{5}{*}{TDM} & TFWO & 0.000379678 & 0.026665602 & 0.001706197 & 0.004771068 \\  & MRFO & 0.008878327 & 0.023401173 & 0.014709966 & 0.003787592 \\ \cline{1-1}  & BSDE & 0.006771142 & 0.032728388 & 0.019233922 & 0.006086967 \\ \cline{1-1}  & MPA & 0.013504282 & 0.051722136 & 0.039748254 & 0.0128107333 \\ \cline{1-1}  & EO & 0.004224359 & 0.015285328 & 0.011790041 & 0.0010841909 \\ \cline{1-1}  & CSO & 0.013060563 & 0.025146228 & 0.017635053 & 0.003343599 \\ \cline{1-1}  & GWO & 0.013924443 & 0.4172306 & 0.2264585866 &the experimental data as well as the competitive optimization algorithms. Added to that, the proposed method has a robust performance as well as good convergence rates for all tested cases. In future work, various environmental impacts, such as temperature, moisture, and noise, as well as the unidentifiability of parameters concept presented in [70; 71; 72; 73], are suggested to be considered for different models as an extension of this work. Another direction is the development of solution methods with a multi-objective framework that combines the closeness of parameters and maximum benefits for power system operators. All authors have contributed to the preparation of this manuscript. [PERSON], [PERSON] and [PERSON] designed the strategy, studied the data, and wrote the manuscript. [PERSON] and [PERSON] revised the manuscript and investigated the optimization methodology. Finally, [PERSON] and [PERSON] reviewed, edited, and supported different improvements to the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Espoo, Finland. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.