Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

Gouda, Osama E.; El-Hoshy, Salah H.; Ghoneim, Sherif S. M.

doi:10.1109/access.2021.3107332

Cited by 24 publications

(10 citation statements)

References 29 publications

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“…The primary objective of this study requires the development of an ML-based health index (HI) model. It is suggested that HI be used to forecast the predicted output parameter, which is conceptually connected to the input characteristics, centered on the crucial assessment 4 . SVM can generate fresh information and categorize non-linear problems.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The oil temperature increases and several gases will be generated when the fault occurs. Generally, the combustible gasses found in the TO in service are hydrogen , methane , ethane , ethylene , and acetylene 3 , 4 . The pollutants in oil are mostly the consequence of the degradation of insulating elements (oil or sheet) because of faults or chemical responses in the apparatus in question.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ngwenyama,

Gitau

2024

Sci Rep

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“…Here, the objective is to obtain a model that facilitates non-intrusive online detection of incipient fault with a high degree of accuracy. The testing of the developed system has been done using fault cases reported in IEC-TC10 fault database (Duval and DePablo, 2001;Gouda et al, 2021). The outcome of the diagnostic tests has been found to be reliable and suggests that the developed model can be regarded as a prospective online incipient fault identification system.…”

Section: Introductionmentioning

confidence: 98%

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Equbal

Nezami²,

Hashem³

et al. 2022

Front. Energy Res.

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E-Nose finds its use in a wide range of applications such as quality assessment in food processing to toxic gas identification in chemical industry either in the offline or online mode. Their usage can be extended to transformer condition monitoring in the online mode. Considering the importance of transformers in power system and the impact it could create if faults in them are unidentified or left unattended, their functioning should be monitored on a real time basis. This work, describes the realization of a prospective E-Nose for online transformer incipient fault identification. The resistive gas sensor array has been simulated in real time using variable resistances forming one arm of a Wheatstone bridges. Separate variable resistances have been calibrated using characteristics of different fault gas sensors. The sensor array of the E-Nose helps to identify the transformer fault gases resulting from an incipient fault condition at the nascent stage itself and prompts for the necessary corrective action well before a catastrophic situation arises. Furthermore, ANFIS model of the Duval’s Triangle (DT) method have been developed to facilitate the online classification of incipient faults. The ANFIS models of other popularly used incipient fault interpretation methods, reported in earlier works, have also been used for a comparative analysis on their diagnostic capabilities. The developed model has been tested using the fault cases of IEC-TC10 fault database and the results thus obtained have been found to be very promising.

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“…Several traditional DGA techniques, such as Key gases, Dornenburg, Rogers' ratios, IEC code, and Duval triangle, contribute to interpreting the cause of transformer faults [6][7][8][9][10]. The DGA methods above were developed to diagnose the transformer faults based on the combustible gases' ratios [4].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm

et al. 2022

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Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases.

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Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

Cited by 24 publications

References 29 publications

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

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

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm

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