A Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer

Fernández, J. C.; Barrios, Luis Benigno. Corrales; Hernández, Fredy; Benítez, Israel; Álvarez, José R.

doi:10.1007/978-3-030-89691-1_19

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several intelligent DGAbased methods are proposed in the literature. These methods are based, among others, on Bayesian network [15], fuzzy logic [16], deep learning [17], supervised, unsupervised, and ensemble machine learning [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Hechifa,

Lakehal,

Nanfak

et al. 2023

Electr Eng

View full text Add to dashboard Cite

This paper discusses the impact of the feature input vector on the performance of DGA-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest (RF), tree ensemble (TE), gradient boosted tree (GBT), and extreme gradient tree (XGB). To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the RF, TE, GBT, and XGB algorithms, respectively. Furthermore, the performance of the methods based on best input feature were evaluated and compared with other methods of literature such as Duval Triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, clustering, and key gases with gas ratio methods. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the RF, TE, GBT, and XGBoost models, respectively. These diagnostic accuracies are higher than 83.15 % of the clustering method and 82.02 % of combined technique which are the best existing methods. Even if the performance of DGA-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Hechifa,

Lakehal,

Nanfak

et al. 2023

Electr Eng

View full text Add to dashboard Cite

show abstract

“…Several intelligent DGA-based methods are proposed in literature for this purpose. These methods are based, among others, on artificial neural network (ANN) [8], fuzzy logic [9], supervised [10], unsupervised [11], or ensemble [12] machine learning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Nanfak

Eke

Meghnefi

et al. 2023

IEEE Trans. Dielect. Electr. Insul.

View full text Add to dashboard Cite

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29 compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

show abstract

Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption

Fernández¹,

Barrios

Benítez

et al. 2022

Intelligent Computing Systems

View full text Add to dashboard Cite

A Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer

Cited by 6 publications

References 30 publications

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption

Contact Info

Product

Resources

About