Traditional power grid anomaly detection methods rely on converting expert knowledge into fixed rules and thresholds, which cannot meet the demands of rapidly evolving power grid systems. The current anomaly detection research mainly focuses on electricity theft and equipment failures as the main analysis objects, but the analysis of overcurrent anomalies is insufficient. This paper analyzes the characteristics of overcurrent anomalies, and discusses the problems and deficiencies of traditional experience-based rules. Through feature engineering, we determines the feature variables, and proposes an XGBoost-based power grid overcurrent anomaly detection model. Through experimental data testing and evaluation, the indicators of the model proposed in this paper outperform the detection methods based on traditional experience-based rules. In the 5-fold cross-validation, the minimum F1 score of the proposed model showed a 19.2% improvement compared to traditional rules, while the average value demonstrated a 15.1% improvement. The experimental results did not show significant performance differences, confirming the effectiveness of the model in anomaly detection. Compared to other commonly used machine methods for anomaly detection, the proposed model in this paper achieved an improvement of 6.4% to 8.7% in F1 score, demonstrating advantages in terms of stability and accuracy. The extreme case testing with training data significantly less than the testing data, along with the conducted interpretability analysis of the model, demonstrated that the proposed model exhibits high transparency and reliability. Moreover, it shows good generalization performance, making it suitable for effective deployment in real-world environments for overcurrent anomaly detection.