Hybrid AI model for power transformer assessment using imbalanced DGA datasets

Wang, Lin; Littler, Timothy; Liu, Xueqin

doi:10.1049/rpg2.12733

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…There are several procedures for diagnosing deformities in transformer insulation. DGA analysis strategies are dependent on scientific hypotheses and practical knowledge gained by specialists across the world 45 , 46 . However, if these analysis strategies are not implemented with caution, they might detect abnormalities erroneously since they simply signal potential faults 47 .…”

Section: Review Of Existing Dga Approachesmentioning

confidence: 99%

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

Ngwenyama,

Gitau

2024

Sci Rep

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This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a massive number of consumers and disrupt commercial activities in that area. Therefore, several monitoring techniques are proposed to ensure that the unit maintains an adequate level of functionality in addition to an extended useful lifespan. DGA is a technique commonly employed for monitoring the state of OITs. The understanding of DGA samples is conversely unsatisfactory from the perspective of evaluating incipient faults and relies mainly on the proficiency of test engineers. In the current work, a multi-classification model that is centered on ML algorithms is demonstrated to have a logical, precise, and perfect understanding of DGA. The proposed model is used to analyze 138 transformer oil (TO) samples that exhibited different stray gassing characteristics in various South African substations. The proposed model combines the design of four ML classifiers and enhances diagnosis accuracy and trust between the transformer manufacturer and power utility. Furthermore, case reports on transformer failure analysis using the proposed model, IEC 60599:2022, and Eskom (Specification—Ref: 240-75661431) standards are presented. In addition, a comparison analysis is conducted in this work against the conventional DGA approaches to validate the proposed model. The proposed model demonstrates the highest degree of accuracy of 87.7%, which was produced by Bagged Trees, followed by Fine KNN with 86.2%, and the third in rank is Quadratic SVM with 84.1%.

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Section: Review Of Existing Dga Approachesmentioning

confidence: 99%

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

Ngwenyama,

Gitau

2024

Sci Rep

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“…Oversampling involves artificially increasing a limited sample size to achieve data balance. This can be done through techniques such as Synthetic Minority Oversampling Technique(SMOTE) 23 , 24 , SVM SMOTE 25 , Borderline-SMOTE 26 , Adaptive Synthetic Sampling(ADASYN) 27 , Generative Adversarial Network(GAN) 28 , and others. Common approaches at the classification algorithm level include CostSensitive 29 and Ensemble Learning 30 .…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

Wang,

Chi,

Ding

et al. 2024

Sci Rep

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In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.

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