Research of the Transformer Fault Diagnosis Expert System based on ESTA and Deep Learning Neural Network programmed in MATLAB

Cui, Qiming; Liang, Kai; Gao, Hongyu; Chen, Guodong; Wang, Naiyu; Jin, Shaoyu; Zhong, Changqing; Cui, Shuting; Sun, Daojun; Fang, Jie; Han, Chengwei; Xu, Bin; An, Yuhong; Xu, Minghu

doi:10.2991/iccte-16.2016.132

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%

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Cheng¹,

Chen²

2018

Preprint

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Compared with conventional methods in fault diagnosis of power transformers, which have defects such as imperfect encoding and too absolute encoding boundary, this paper systematically reveals various intelligent approaches applied in fault diagnosing and decision making of large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one certain aspect, causing some shortcomings in various degrees cannot be revealed effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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Section: ) Deep Learning (Dl)mentioning

confidence: 99%

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Cheng¹,

Chen²

2018

Preprint

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“…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%

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Cheng¹,

Yu²

2018

Preprint

View full text Add to dashboard Cite

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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“…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%

A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

Yan

Zhang

2017

Mathematical Problems in Engineering

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The fault diagnosis method based on dissolved gas analysis (DGA) is of great significance to detect the potential faults of the transformer and improve the security of the power system. The DGA data of transformer in smart grid have the characteristics of large quantity, multiple types, and low value density. In view of DGA big data’s characteristics, the paper first proposes a new combined fault diagnosis method for transformer, in which a variety of fault diagnosis models are used to make a preliminary diagnosis, and then the support vector machine is used to make the second diagnosis. The method adopts the intelligent complementary and blending thought, which overcomes the shortcomings of single diagnosis model in transformer fault diagnosis, and improves the diagnostic accuracy and the scope of application of the model. Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment.

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Research of the Transformer Fault Diagnosis Expert System based on ESTA and Deep Learning Neural Network programmed in MATLAB

Cited by 5 publications

References 0 publications

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

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