Gorgani Firouzjah, K., Ghasemi, J.. (1404). Ensemble Learning Algorithm for Power Transformer Health Assessment Using Dissolved Gas Analysis. فناوری آموزش, 13(2), 387-402. doi: 10.22061/jecei.2025.11450.805
K. Gorgani Firouzjah; J. Ghasemi. "Ensemble Learning Algorithm for Power Transformer Health Assessment Using Dissolved Gas Analysis". فناوری آموزش, 13, 2, 1404, 387-402. doi: 10.22061/jecei.2025.11450.805
Gorgani Firouzjah, K., Ghasemi, J.. (1404). 'Ensemble Learning Algorithm for Power Transformer Health Assessment Using Dissolved Gas Analysis', فناوری آموزش, 13(2), pp. 387-402. doi: 10.22061/jecei.2025.11450.805
Gorgani Firouzjah, K., Ghasemi, J.. Ensemble Learning Algorithm for Power Transformer Health Assessment Using Dissolved Gas Analysis. فناوری آموزش, 1404; 13(2): 387-402. doi: 10.22061/jecei.2025.11450.805
Department of Electrical Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran.
تاریخ دریافت: 22 آبان 1403،
تاریخ بازنگری: 07 بهمن 1403،
تاریخ پذیرش: 10 بهمن 1403
چکیده
Background and Objectives: Power transformer (PT) health assessment is crucial for ensuring the reliability of power systems. Dissolved Gas Analysis (DGA) is a widely used technique for this purpose, but traditional DGA interpretation methods have limitations. This study aims to develop a more accurate and reliable PT health assessment method using an ensemble learning approach with DGA. Methods: The proposed method utilizes 11 key parameters obtained from real PT samples. In this way, synthetic data are generated using statistical simulation to enhance the model's robustness. Twelve different classifiers are initially trained and evaluated on the combined dataset. Two novel indices (a risk index and an unnecessary cost index) are introduced to assess the classifiers' performance alongside traditional metrics such as accuracy, precision, and the confusion matrix. An ensemble learning method is then constructed by selecting classifiers with the lowest risk and cost indices. Results: The ensemble learning approach demonstrated superior performance compared to individual classifiers. The learning algorithm achieved high accuracy (99%, 92%, and 86% for three health classes), a low unnecessary cost index (6%), and a low misclassification risk (16%). This result indicates the effectiveness of the ensemble approach in accurately detecting PT health conditions. Conclusion: The proposed ensemble learning method provides a reliable and accurate assessment of PT health using DGA data. This approach effectively optimizes maintenance strategies and enhances the overall reliability of power systems by minimizing misclassification risks and unnecessary costs.
