A Novel Fault Prediction Technique for Oil-Immersed Transformers Based on Advanced Gradient Boosting and Particle Swarm Optimization (PSO)

Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning framework that integrates Hist Gradient Boosting (HGB) with a metaheuristic Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, thereby guaranteeing classifier robustness. The proposed method underwent a two‑stage evaluation: first, Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and HGB were benchmarked, revealing HGB as the most effective method; second, PSO was applied to optimize HGB’s hyperparameters, yielding further performance improvements. Experimental results demonstrate that the hybrid HGB‑PSO model achieves an accuracy of ۹۷.۸۵ %, precision of ۹۸.۹۰ %, recall of ۹۷.۳۳ %, and an F۱‑score of ۹۸.۹۹ %. All simulations and comparative analyses against state‑of‑the‑art methods were implemented in Python, and confusion‑matrix analysis was employed to assess predictive performance comprehensively. These findings demonstrate that the hybrid HGB‑PSO method achieves superior accuracy and robustness in transformer fault prediction.

کلیدواژه ها:

Power Transformer ، Machine learning ، Gradient Boosting ، PSO ، Fault Prediction