Feature engineering and feature selection for fault type classification from dissolved gas values in transformer oil

Chanchotisatien, Passara; Vong, Chanvichet

doi:10.1109/icsec53205.2021.9684595

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The same DGA processes, considering non-temporal analysis and classification of fault type, reported an accuracy of 87.06% when using LGBM [95]. This work's dataset consisted of 796 samples with gases such as H2, CH4, C2H2, C2H4, and C2H6.…”

Section: Application Of Decision Tree Random Forest and Hybrid Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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Section: Application Of Decision Tree Random Forest and Hybrid Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The same DGA processes, considering non-temporal analysis and a classification of fault type, reported an accuracy of 87.06% when using the LGBM [ 98 ]. This work’s dataset consisted of 796 samples with gases such as H 2 , CH 4 , C 2 H 2 , C 2 H 4 , and C 2 H 6 .…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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“…This process leads to the production of various gases, including Methane (CH4), carbon monoxide (CO), Hydrogen (H2), Acetylene (C2H2), Ethylene (C2H4), Ethane (C2H6), carbon dioxide (CO2) [2]. To analyze the gases produced by transformer faults, a commonly used method is Dissolved Gas Analysis (DGA) [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Hechifa,

Lakehal,

Nanfak

et al. 2023

The Scientific Bulletin of Electrical Engineering Faculty

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Dissolved Gas Analysis (DGA) continues to be widely recognized as a valuable method in recent times for the early identification of issues in oil-filled power transformers. It has gained extensive adoption as a primary approach for the early discovery of these issues, relying on the analysis of dissolved gases. This contributes to enhancing the dependability of electrical systems. This paper proposes an efficient fusion method based on DGA data using the two best Machine Learning algorithms, the neural network (MLP), the naïve Bayes (NB) throughdata input vector ppm, a percentage input vector, and an Logarithmic input vector. The fusion method predictively combined the two classifiers and obtained a statistical evaluation: accuracy, recall, precision, and F-measure higher than both classifiers separately. The proposed fusion method was evaluated for performance using a test database and compared with conventional and smart methods. Results showed that the proposed model outperformed both traditional and intelligent methods in terms of diagnostic accuracy when using percentage and logarithmic input vectors. The Prediction Based Fusion (PBF) vector Percentages achieved an accuracy rate of 97.22%, while PBF vector Logarithmic achieved an accuracy rate of 95.83%. These rates were higher than those achieved by traditional methods, such as the Modified RRM/CEGB method 91.67% and Modified RRM/IEC method 90.28%. Additionally, the proposed model surpassed the accuracy rates of intelligent methods, such as CSUS ANN 88.89% and Conditional Probability 93.06%.

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Feature engineering and feature selection for fault type classification from dissolved gas values in transformer oil

Cited by 3 publications

References 19 publications

Predictive Analytics Performance on Oil and Gas: A Significant Review

Predictive Analytics Performance on Oil and Gas: A Significant Review

A Review of Predictive Analytics Models in the Oil and Gas Industries

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

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