دورية أكاديمية
Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree
| العنوان: | Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree |
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| المؤلفون: | Xiaoxing Zhang, Rongxing Fang, Guozhi Zhang, Yaqi Fang, Xiu Zhou, Yunlong Ma, Kun Wang, Kang Chen |
| المصدر: | IET Generation, Transmission & Distribution, Vol 15, Iss 20, Pp 2926-2942 (2021) |
| بيانات النشر: | Wiley, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | Organic insulation, Optimisation techniques, Computer vision and image processing techniques, Combinatorial mathematics, Transformers and reactors, Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | Abstract The information of dissolved gas in transformer oil can reflect the potential fault in oil immersed power transformer. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on IFA‐LPboost‐CART is proposed here. First, a LPboost‐CART model is established. The classification and regression tree (CART) are used as the weak classifiers, and the linear programming boosting (LPboost) ensemble learning method is used to adjust the weight of each weak classifier to construct a strong classifier. Then the improved firefly algorithm (IFA) is adopted to optimize the number of CART and the maximum number of splits of CART in LPboost‐CART to obtain the IFA‐LPboost‐CART model. The experimental results show that, compared with the existing methods, such as CART and support vector machine (SVM), the proposed IFA‐LPboost‐CART model has higher fault diagnosis accuracy, which can provide technical support for transformer fault diagnosis. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-8695 1751-8687 |
| Relation: | https://doaj.org/toc/1751-8687; https://doaj.org/toc/1751-8695 |
| DOI: | 10.1049/gtd2.12229 |
| URL الوصول: | https://doaj.org/article/89cc3cd0f2f64a2890c8adce62e3533f |
| رقم الأكسشن: | edsdoj.89cc3cd0f2f64a2890c8adce62e3533f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17518695 17518687 |
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| DOI: | 10.1049/gtd2.12229 |