Analysis of gasses generated by electrical discharges in low viscosity silicone oil

Endo, Fumio; Yamamoto, S.; Sadakane, T.; Yamagishi, Akira; Miyagi, Katsunori; Ōkubo, H.

doi:10.1109/cmd.2008.4580279

Cited by 2 publications

(1 citation statement)

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“…The faults in this approach are compared with the gas concentration profile. According to the IEEE standard, KGs are gases produced in OITs that alert to observational fault-type diagnostics, depending on which gases are common or prominent at certain temperature levels 63 . If there is no previous DG data is provided for analysis, hazards in the apparatus can be detected and evaluated using the guidelines indicated in Table 7 .…”

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%