EDA-ANN based transformer fault recognition with dissolved gas

Zou, Kexu; Zhao, Wei; Xu, Weiting; Xinjie, Lv; Gao, Feng; Yin, Wenjun

doi:10.1109/isgt-asia.2013.6698754

Cited by 1 publication

(2 citation statements)

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“…This proposed method can precisely discriminate among disc-to-disc short circuit faults, radial deformation and axial displacement defects and determine their location or extent with a good accuracy. Besides, a model combined estimation of distribution algorithm (EDA) with ANN is developed in [131], called EDA-ANN method, which is employed to realize the fault recognition with dissolved gas. This EDA is a new population evolutionary algorithm based on probabilistic model.…”

Section: Ann-based Transformer Fault Diagnosis Using Dga: a Surveymentioning

confidence: 99%

“… large-scale parallel information processing ability  strong fault tolerance [41], [119]  RBF neural network [42], [122][123], [128][129]  knowledge discovery-based neural network [43]  knowledge extraction-based neural network [44]  fuzzy reasoning-based neural network [45]  MLP neural network-based decision [46]  BP neural network [103]  recurrent ANN [104]  DL based ANN [105]  hybrid ANN and EPS [106]  GRNN [40,107]  combined with mathematical morphology [108]  combined GA multi-layer feedforward network [120], [135]  combined with competitive learning theory [121]  WNN and FWNN [67,[124][125][126][127]  EDA-ANN [131]  combined with FAHP [134]…”

Section: Advantages and Disadvantages Working Process Primary Meansmentioning

confidence: 99%

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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: Ann-based Transformer Fault Diagnosis Using Dga: a Surveymentioning

confidence: 99%

Section: Advantages and Disadvantages Working Process Primary Meansmentioning

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

View full text Add to dashboard Cite

show abstract

EDA-ANN based transformer fault recognition with dissolved gas

Cited by 1 publication

References 10 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

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