# Identification of Transformer Parameters Using Dandelion Algorithm
## 1 Introduction
This eliminates the need for service disruptions associated with traditional parameter estimation methods. Table 1 introduces recent algorithms in parameter estimation of transformers and their main outcomes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Optimization Algorithm** & **Transformer Type** & \multicolumn{2}{c}{**Main Outcomes/Shortcomings**} \\ \hline \multirow{2}{*}{[7]} & Artificial hummingbird & \(\bullet\) & Single-phase & \(\bullet\) & Emphasizing accuracy under various loading conditions \\  & optimizer & \(\bullet\) & Three-phase & \(\bullet\) & The greatest concordance with experimentally measured \\ \hline \multirow{2}{*}{[14]} & Coyote optimization & \(\bullet\) & Single-phase & \(\bullet\) & The greatest concordance with experimentally measured \\  & algorithm & \(\bullet\) & Three-phase & \(\bullet\) & False convergence \\ \hline \multirow{2}{*}{[15]} & Jellyfish search optimizer & \(\bullet\) & Single-phase & \(\bullet\) & No statistical analysis \\  & chaotic optimization & \(\bullet\) & Single-phase & \(\bullet\) & No statistical analysis \\ \hline \multirow{2}{*}{[17]} & Artificial bee colony & \(\bullet\) & Single-phase & \(\bullet\) & Using various scenarios for validation \\  & & & & \(\bullet\) & There is no comparison with competitive algorithms \\ \hline \multirow{2}{*}{[18]} & \multirow{2}{*}{Sime mold optimizer} & \(\bullet\) & Single-phase & \(\bullet\) & False convergence and accuracy \\  & & & & & \\ \hline \multirow{2}{*}{[19]} & Improved Tasmanian devil & \(\bullet\) & Single-phase & \(\bullet\) & Improved algorithm \\  & & & \(\bullet\) & Robustness and accuracy \\ \hline \multirow{2}{*}{[20]} & Generalized normal & \multirow{2}{*}{\(\bullet\)} & Single-phase & \(\bullet\) & Least computational efforts and fast convergence \\  & distribution optimizer & & & & \\ \hline \multirow{2}{*}{[21]} & Hurricane optimization & \multirow{2}{*}{\(\bullet\)} & Single-phase & \(\bullet\) & Validation through three different sizes of transformers \\  & algorithm & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent algorithms in parameter estimation of transformers. While metaheuristics offer promising results for transformer modeling, their effectiveness hinges on two key factors: a thorough exploration of the model's complex search landscape and fine-tuning of algorithm parameters like population size and iteration count. Furthermore, specific control settings in some algorithms significantly impact performance. Inappropriate settings can lead to increased computation time, reduced accuracy, and a higher risk of getting stuck in suboptimal solutions (local optima). This highlights the need for improvement in existing methods or the development of new variants, acknowledging the \"no free lunch\" theorem [22], which states that there is no single best approach for all optimization problems. This has motivated researchers to improve existing methods or create entirely new ones, aiming for strong performance across diverse and challenging optimization problems. Therefore the dandelion algorithm (DA) [23] is introduced in this study. Encouraged by its success in solving engineering problems like maximum power point tracking of photovoltaic systems [24; 25], allocation of energy storage devices [26], parameters extraction of energy storage devices [27], controller tuning in various applications [28; 29; 30; 31], and economic dispatch [32], even in optimal design of steel frames [33], the authors explore the application of the DA in identifying optimal parameters for electrical transformers, ultimately aiming to ensure reliable power system operation. This approach has proven effective in finding near-optimal solutions for various real-world engineering challenges. The key to its success lies in its ability to balance exploration (searching for new possibilities) and exploitation (refining promising solutions), leading to competitive performance compared to other algorithms. The key contributions of this research can be enumerated in the following points:  
1.