Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree

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 estab...

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Veröffentlicht in:IET generation, transmission & distribution Jg. 15; H. 20; S. 2926 - 2942
Hauptverfasser: Zhang, Xiaoxing, Fang, Rongxing, Zhang, Guozhi, Fang, Yaqi, Zhou, Xiu, Ma, Yunlong, Wang, Kun, Chen, Kang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Wiley 01.10.2021
Schlagworte:
Combinatorial mathematics
Computer vision and image processing techniques
Optimisation techniques
Organic insulation
Transformers and reactors
ISSN:1751-8687, 1751-8695
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Zusammenfassung: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.
Bibliographie:Funding information
Natural Science Foundation of Hubei Province (2020CFB166)
ISSN:1751-8687
1751-8695
DOI:10.1049/gtd2.12229