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

Zhang, Xiaoxing; Fang, Rongxing; Zhang, Guozhi; Fang, Yaqi; Zhou, Xinbo; Ma, Yunlong; Wang, Kun; Chen, Kang

doi:10.1049/gtd2.12229

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Although the decision performance of each decision tree in random forest is different, the decision weights given to each tree are the same, which can weaken the accuracy of the model to some extent [2]. The use of a single algorithm is prone to poor model identification performance due to the randomness of the samples; however, model fusion works well for problems such as user credit identification, risk prediction, and power equipment fault diagnosis [3][4][5][6]. Therefore, in this paper, we applied the model fusion approach to risky user identification based on mobile network communication behavior and propose a Stacking model fusion mobile network risky user identification model based on feature selection and hybrid sampling to fully combine the advantages of various algo-rithms and complement each other's strengths to achieve an overall model performance improvement using model fusion.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

Lin

Wang

2023

CIT. J. comput. inf. technol

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With the development of economic globalization and modern information and communication technology, the situation of communication fraud is becoming more and more serious. How to identify fraudulent calls accurately and effectively has become an urgent task in current telecommunications operations. Affected by the sample set and the current state of the art, the current machine learning methods used to identify the imbalanced distribution dataset of positive and negative samples have low recognition accuracy. Therefore, in this paper, we propose a new hybrid model solution that uses feature construction, feature selection and imbalanced classes handling. A stacking model fusion algorithm composed of a two-layer stacking framework with several state-of-the-art machine learning classifiers is adopted. The results show that the risk user identification model based on mobile network communication behavior established by our stacking model fusion algorithm can accurately predict the category labels of telecom users and improve the risk of telecom users. The generalization performance of the identification is high, which provides a certain reference for the telecommunications industry to identify risk users based on mobile network communication behaviors.

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Section: Introductionmentioning

confidence: 99%

Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

Lin

Wang

2023

CIT. J. comput. inf. technol

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Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic

Laayati

El-Bazi

Hadraoui

et al. 2023

Digital Technologies and Applications

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Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Nanfak

Eke

Meghnefi

et al. 2023

IEEE Trans. Dielect. Electr. Insul.

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Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29 compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

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

Cited by 4 publications

References 28 publications

Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

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