Proceedings of the 2016 International Conference on Civil, Transportation and Environment 2016
DOI: 10.2991/iccte-16.2016.132
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Research of the Transformer Fault Diagnosis Expert System based on ESTA and Deep Learning Neural Network programmed in MATLAB

Abstract: Some procedures or functions had be added to an ESTA (Expert System Shell for Text Animation) so that the ESTA and MATLAB can communicate via some data files.On this basis,a deep learning-DBN(Deep Belief Network) and two BP(back propagation) artificial neural network based on the MATLAB programming were researched by using directly DGA (Dissolved Gas Analysis) and characteristic gas method in transformer oil chromatographic analysis.The transformer fault diagnosis expert system based on a three ratio and chara… Show more

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Cited by 5 publications
(6 citation statements)
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“…Mlakić et al [70] employed the DL method and infrared imaging as a tool for transformer faults detection. Cui et al [71] investigated a DL-DBN (deep belief network) and two BP artificial neural networks based on the Matlab programming by using directly DGA and characteristic gas method in transformer oil chromatographic analysis. Shi et al [105] firstly constructed a classified DAEN model, and employed the typical classified data set to analyze and verify the classification performance of this model.…”
Section: ) Deep Learning (Dl)mentioning
confidence: 99%
“…Mlakić et al [70] employed the DL method and infrared imaging as a tool for transformer faults detection. Cui et al [71] investigated a DL-DBN (deep belief network) and two BP artificial neural networks based on the Matlab programming by using directly DGA and characteristic gas method in transformer oil chromatographic analysis. Shi et al [105] firstly constructed a classified DAEN model, and employed the typical classified data set to analyze and verify the classification performance of this model.…”
Section: ) Deep Learning (Dl)mentioning
confidence: 99%
“…The DGA-based transformer fault diagnosis system is a complex system in which various uncertain factors and unknown information are remained under cover, causing fuzziness and randomness in addressing these uncertain issues. In addition to the five main categories of research approaches and techniques summarized above, there are some other intelligent algorithms, such as artificial immune algorithm (AIA) [72,163,176], GA [67,68,196], improved artificial fish swarm optimizer (IAFSO) [197][198][199], PSO [69,77,80], dynamic clustering (DC) [79,81], WA [83,[124][125][126][127], SVM [5,68,72,77,80,85,154,169,170,188,199], BN [87,[166][167][168], information fusion technology [200][201][202], extreme learning machine (ELM) [203][204][205], DL [70,71,105,206,…”
Section: Application Of Other Intelligent Algorithms In Dga-based Tramentioning
confidence: 99%
“…Deep learning, which simulates the hierarchical structure of human brain, processing data from lower level to higher level and gradually composing more and more semantic concepts, has been popular in artificial intelligence. Some scholars [24][25][26][27] introduce deep autoencoder network and deep belief network to solve the problem of transformer fault diagnosis. All of the above methods have made some fault diagnosis achievements.…”
Section: Introductionmentioning
confidence: 99%