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 7 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

Lecture Notes in Networks and Systems

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Fault diagnosis of power transformers based on dissolved gas analysis and improved LightGBM hybrid integrated model with dual‐branch structure

Lv,

Liu,

Jiang

et al. 2024

IET Electric Power Appl

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Aiming at the fault diagnosis problems of imbalanced data and insufficient mapping of characteristic information in fault samples collected by transformers at present, which lead to low accuracy and large diagnostic deviation in actual applications, a power transformer fault diagnosis method based on dissolved gas analysis and an improved LightGBM hybrid integrated model with a dual‐branch structure (DIL‐DS) is proposed. Firstly, multi‐characteristic dissolved gas ratio analysis is used to construct multi‐dimensional supplementary feature vectors, which enrich the characterisation features of transformers and facilitate efficient diagnosis of classification models. Secondly, a dual‐branch structure combining focal‐gradient harmonic loss and cross‐entropy loss is introduced to improve the attention and recognition ability of the model to a few categories in the dataset and alleviate the influence of data imbalance on the diagnostic results. Then, an improved grey wolf optimisation (GWO) is designed to improve LightGBM and realise the iterative optimisation of hyperparameters. At the same time, the Jacobian regularisation method is introduced to denoise LightGBM to solve the problem that the model is sensitive to noise. Finally, the LightGBM hybrid integrated model is developed to ensure the accuracy and stability of model diagnosis under the changeable and imbalanced dataset. Experiments show that the proposed DIL‐DS can effectively solve the limitation of class imbalance, improve the overall fault diagnosis performance, and is suitable for transformer fault identification.

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

Cited by 7 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

Fault diagnosis of power transformers based on dissolved gas analysis and improved LightGBM hybrid integrated model with dual‐branch structure

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