Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Siregar, Yulianta; Lumbanraja, Timothy Juan Hartanto

doi:10.11591/ijece.v13i4.pp3672-3685

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Yet, it's quite challenging during implementation due to too much computation and expert knowledge requirement. Siregar and Lumbanraja [16], combined three (3) methods in detecting transformer faults, the methods proposed have an insufficient cooling system, resulting in unwanted failure. Similarly, Mohammed et al [17], on transformer fault detection was proposed, yet, the approach suffers due to complexity, accuracy dependence on input data quality, potential modeling inaccuracies, and challenges in capturing real-world dynamics for effective optimization and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Transformer faults identification via fuzzy logic approach

Dapshima,

Mishra,

Tyagi

2024

IJEECS

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The need for a constant electricity supply is at an alarming rate especially in the 21st century due to the high rate of increase in industrialization across the globe. Conventional protection schemes such as differential relays, Buchholz relay, and other techniques such as genetic algorithms and artificial neural networks, do not match the precision and reliability needed for transformer fault indentification, due to their complexity in computation, tedious training system, time consumption, and need the of human experts. The method proposed in this research is the use of a fuzzy inference system in detecting potential faults in power system transformers. The faults in the transformer were observed and analyzed using a simulation system of MATLAB/Simulink software. The suggested approach ensures swift identification of faults as it relies on if-then rules and only uses current and voltage measurements with 100% independence toward the power flow direction, making it highly reliable and simple to implement compared to other techniques for transformer fault identification.

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Section: Introductionmentioning

confidence: 99%

Transformer faults identification via fuzzy logic approach

Dapshima,

Mishra,

Tyagi

2024

IJEECS

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“…To determine and monitor transformer condition, many users rely solely on dissolved gas analysis [17], so a number of standards have been reviewed [18,19] and diagnostic methods using artificial intelligence-based programmes have been developed [20,21].…”

Section: Introductionmentioning

confidence: 99%

Determining the Remaining Functional Life of Power Transformers Using Multiple Methods of Diagnosing the Operating Condition Based on SVM Classification Algorithms

Aciu,

Nițu,

Nicola

et al. 2024

Machines

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Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.

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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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Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Cited by 3 publications

References 17 publications

Transformer faults identification via fuzzy logic approach

Transformer faults identification via fuzzy logic approach

Determining the Remaining Functional Life of Power Transformers Using Multiple Methods of Diagnosing the Operating Condition Based on SVM Classification Algorithms

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

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