Artificial Intelligent Fault Diagnostic Method for Power Transformers using a New Classification System of Faults

Kim, Yonghyun; Park, Tae‐Sik; Kim, Seonghwan; Kwak, Nojun; Kweon, Dong-Jin

doi:10.1007/s42835-019-00105-0

Cited by 19 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support vector machine (SVM) is also widely used in fault diagnosis to improve the accuracy of fault classification. SVM is an effective method to deal with large dimensionality of independent variables without recalculating from initial conditions to obtain new decision boundaries (Kim et al, 2019). However, the classification accuracy using a single SVM is not very high.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Intelligent techniques help to resolve the uncertainty of traditional DGA methods due to boundary problems and unresolved codes or multi-fault scenarios (Wani et al, 2021). Researchers have applied many artificial intelligence techniques to DGA fault diagnosis, such as neural networks (Duan and Liu, 2011;Wang et al, 2016;Qi et al, 2019;Yan et al, 2019;Yang et al, 2019Yang et al, , 2020Luo et al, 2020;Velásquez and Lara, 2020;Mi et al, 2021;Taha et al, 2021;Zhou et al, 2021), support vector machine (SVM) (Wang and Zhang, 2017;Fang et al, 2018;Huang et al, 2018;Illias and Liang, 2018;Kari et al, 2018;Kim et al, 2019;Zeng et al, 2019;Zhang et al, 2019;Zhang Y. et al, 2020;Benmahamed et al, 2021), and clustering (Islam et al, 2017;Misbahulmunir et al, 2020). These techniques involve statistical machine learning, deep learning, etc.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

View full text Add to dashboard Cite

Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance. This paper presents a systematic review of the literature on the application of artificial intelligence techniques for DGA-based diagnosis and for solving intractable problems in early transformer fault diagnosis, which include neural networks, clustering, support vector machines, etc. In addition to reviewing the applications of these intelligent techniques, the diagnostic thinking proposed in this literature, such as the introduction of temporal parameters for comprehensive analysis of DGA data and the extraction of optimal features for DGA data, is also reviewed. Finally, this paper summarizes and prospects the artificial intelligence techniques applied by researchers in transformer fault diagnosis.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

mentioning

confidence: 99%

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The diagnosis and remote monitoring systems is controlling the technical conditions of the particular railway infrastructure components (transformers, overhead contact line equipment, overhead power lines, etc.) [1][2][3][4][5][6][7][8]. One of the exiting diagnosis systems' serious shortcomings is a lack of a methodological foundation for a complex analysis of diagnostic information flow, retrospective data accumulation and storage methods, which have particular importance as a raw training set data for machine learning, clusterization and classification networks.…”

Section: Introductionmentioning

confidence: 99%

The use of predictive analysis algorithms and methods for the overhead contact line operation mode selection

Smerdin

Ermachkov²,

Sidorov³

et al. 2022

E3S Web Conf.

View full text Add to dashboard Cite

The article tells about the problem of the railway infrastructure objects reliable, safe, and effective operation on an example of the overhead contact line operation mode rational selection. The overhead contact line operation mode modelling results are described. The model is considering exploitation and climatic conditions and is based on the clusterization, classification, and neuronal network modelling methods for predictive analysis of a large amount of data.

show abstract

“…B. M. Taha et al (2017), [13] Conditional probability 403 83.13-86.85% T. Kari et al (2018), [37] Adaptive neuro-fuzzy inference system (ANFIS) and Dempster-Shafer Theory 697 84.4±3.7 % Y. Kim et al (2018), [38] SVM and KNN 189 88% X. Zhang et al (2019), [34] BA-PNN-based methods 139 98.46% I.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Meta-Heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults

et al. 2021

View full text Add to dashboard Cite

Detection of transformer faults avoids the transformer's undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers' ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques' parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon's rank-sum, confirms the algorithm's accuracy.

show abstract

Artificial Intelligent Fault Diagnostic Method for Power Transformers using a New Classification System of Faults

Cited by 19 publications

References 8 publications

Fault diagnosis of transformer using artificial intelligence: A review

Fault diagnosis of transformer using artificial intelligence: A review

The use of predictive analysis algorithms and methods for the overhead contact line operation mode selection

Adaptive Dynamic Meta-Heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults

Contact Info

Product

Resources

About