Online Dissolved Gas Analysis of Power Transformers Based on Decision Tree Model

Basuki, Arief; Suwarno, &

doi:10.1109/icpere.2018.8739761

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…To simplify the assessment of hundreds of power transformer data, a Machine Learning-based DPM has been developed using the tool provided by MATLAB. The use of machine learning in power transformer assessment has also been reported by several studies [17,[37][38][39][40][41][42][43][44][45].…”

Section: Svm Model For Duval Pentagonmentioning

confidence: 76%

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

et al. 2020

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In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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Section: Svm Model For Duval Pentagonmentioning

confidence: 76%

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

et al. 2020

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“…The DT algorithm is a machine learning method that estimates likelihood by developing a tree-like structure that repeatedly splits into smaller and smaller segments until it terminates in nodes called leaf nodes. The classification of data items in a decision tree is done through an iterative process of repeatedly questioning the features associated with the items [51][52][53]. The questions are enclosed in the nodes with each interior node pointing to a child node for every probable answer to its queries.…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

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This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

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“…This equation can be approximated into a second-order approximation using Taylor's expansion series with the final equation involving a gradient and a Hessian, as shown in Eq. (6).…”

Section: Xg Boost Classifier (Boosting)mentioning

confidence: 99%

“…The trained decision tree-based monitoring system can be deployed with each transformer for online DGA and fault classification. For instance, the well-known C4.5 algorithm demonstrated satisfactory performance in field tests by reducing the training data and improving classification performance by repeatedly training the decision tree while deleting the misclassified instances by the current decision tree [6,7].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning Methods for better Dynamic Assessment of Transformer Status

Ghosh

Dutta

2021

J. Inst. Eng. India Ser. B

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Analyzing dissolved gases in the transformer's mineral oil helps to detect and classify the systemic faults in electric power transformers. Formerly, empirical methods such as Rogers ratio, Duval triangles 1-4-5, and pentagons 1-2 were used for transformer fault classification. Loose fit for every transformer type is one of the most prominent disadvantages of conventional methods. Formulating robust machine learning algorithms, such as the decision trees, can significantly overcome the loose fit issue. This paper focuses on implementing four different decision tree algorithms, including a regular decision tree classifier, a bagging classifier, a boosting classifier, and a stacking classifier to classify six different transformer fault types distinctly. Further, this study shows that the efficacy and accuracy of the four mentioned classifiers could be far exceeded when combined using a wisdom of the crowd approach. The wisdom of the crowd approach essentially merges the predicted classes from the four individual classifiers and decides on the final prediction via a hardvoting routine. The computational evaluation revealed that the given voting approach could significantly improve power transformers' online diagnostic accuracy up to 91%, thus aiding early forecast of power transformers' preventive maintenance.

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Online Dissolved Gas Analysis of Power Transformers Based on Decision Tree Model

Cited by 15 publications

References 7 publications

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

Ensemble Machine Learning Methods for better Dynamic Assessment of Transformer Status

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