## 6 Conclusions  
In response to the challenges of extracting characteristics from series arc faults and low accuracy in the fault world, this paper proposes a fault diagnosis method based on AHA-XGBoost. Through experimental analysis using an independently developed fault arc test platform, the superiority and effectiveness of this method have been verified. It can provide methodological support for subsequent series fault arc diagnosis and simultaneously improve accuracy. The main conclusions are as follows:  
1. Feature selection optimization: Using the global search ability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arc are screened out from high-dimensional time-frequency domain features (such as wavelet packet entropy and high-frequency harmonic distortion rate) so as to overcome the local optimal defects of traditional filtering feature selection methods (such as the mutual information method). 2. Lightweight integrated modeling: Combining the parallel computing advantages of XGBoost and regularization strategies, a low-complexity and high-explanatory fault classification model is constructed. Through AHA synchronous optimization of XGBoost's hyperparameters (such as learning rate and tree depth), the detection accuracy and real-time performance are significantly improved. 3. AHA optimizes XGBoost, which converges faster and has a better effect. The analysis of the experimental conclusions shows that the series arc fault detection method based on AHA-XGBoost has significant advantages. By using the AHA algorithm to adaptively filter high-dimensional time-frequency domain features, it effectively addresses the challenge of insufficient extraction of arc-sensitive features, reducing the feature dimension to an optimal subset. Combining the XGBoost regularization model, it ensures a high detection accuracy of 97.523% while reducing model complexity, and its parallel computing characteristics enhance computational efficiency. Experiments verify that this  
Figure 11: AUC curves of each model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Specificity (Sp)** & **Sensitivity (Se)** & **Accuracy (Acc)** & **Precision** & **Recall** & **F1** \\ \hline BP neural network & 96.811 & 85.324 & 93.173 & 92.536 & 85.324 & 88.784 \\ Extreme learning & 96.904 & 75.607 & 90.16 & 91.882 & 75.607 & 82.954 \\ Random forest & 97.139 & 94.94 & 96.442 & 93.894 & 94.94 & 94.415 \\ Decision tree & 97.608 & 94.028 & 96.474 & 94.796 & 94.028 & 94.411 \\ AHA-XGBoost & 98.8743 & 95.648 & 98.098 & 97.523 & 95.648 & 96.576 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fault arc diagnosis results of different methods. method meets the requirements of GB14287.4--2014, with a feature dimension reduction of about 40% compared to traditional methods, and a misjuddgnent rate reduced to 1.9%, maintaining an identification accuracy of 98.098% even in low-current (<5 A) scenarios. This fusion algorithm provides a highly interpretable solution for multi-type electrical arc fault detection, offering practical engineering value for improving the reliability of electrical fire warning systems. The core tasks in the future will focus on enhancing robustness in complex real-world environments (such as strong noise and diverse loads), achieving efficient integration and optimization of embedded systems, and ensuring continuous compliance with relevant safety and compatibility standards. This will be achieved by overcoming current limitations through methods such as collecting more comprehensive data, improving noise immunity, and model lightweighting, thereby facilitating the transition from laboratory achievements to large-scale practical applications. Conceptualization, L.Q. ; Methodology, L.Q. ; Formal analysis, T.K. ; Writing--original draft, L.Q. ; Writing--review & editing, S.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article.