Transformer fault diagnosis based on homotopy BP algorithm

Zhao, Jin; Zheng, Ruirui; Li, Jianpo

doi:10.1109/icemi.2009.5274664

Cited by 7 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there are some defects in the BP algorithm, such as easy to fall into local convergence (i.e., easy to fall into local minima), accuracy of solution is not high, and higher requirements for initial values. To address this concern, various improved algorithms have been proposed, such as the BP neural network for variable learning rate [106], the homotopic BP algorithm [117], and the BP algorithm with momentum term [118]. Apart from the common BP neural network structure, there are some other types of network structure, such as probabilistic neural network structure [119], combined genetic algorithm (GA) multi-layer feedforward network [120], competitive learning theory based self-organized network [121], RBF network [122][123], and WNN [67,[124][125][126][127].…”

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

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

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.

show abstract

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

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

“…However, there are some defects in the BP algorithm, such as the fact it easily falls into local convergence (i.e., easily falls into local minima), the accuracy of the solution is not high, and higher requirements for initial values. To address this concern, various improved algorithms have been proposed, such as the BP neural network for variable learning rate [106], the homotopic BP algorithm [117], and the BP algorithm with momentum term [118]. Apart from the common BP neural network structure, there are some other types of network structure, such as probabilistic neural network structure [119], combined genetic algorithm (GA) multi-layer feedforward network [120], competitive learning theory based self-organized network [121], RBF network [122,123], and WNN [67,[124][125][126][127].…”

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

confidence: 99%

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

Cheng

2018

Energies

View full text Add to dashboard Cite

Abstract: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.

show abstract

Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Trappey

et al. 2015

Computers & Industrial Engineering

View full text Add to dashboard Cite

Transformer fault diagnosis based on homotopy BP algorithm

Cited by 7 publications

References 4 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

Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Contact Info

Product

Resources

About