Comparison of IEC 60599 gas ratios and an integrated fuzzy-evidential reasoning approach in fault identification using dissolved gas analysis

Irungu, G. K.; Akumu, A. O.; Munda, Josiah L.

doi:10.1109/upec.2016.8114055

Cited by 6 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The strongest firing rule will be: Using FL can be tedious, and errors can creep in during rules formulation; instead, the fault identification can be completed from the fuzzified inputs degree of memberships using evidential reasoning (ER). ER is firm mathematical theory started by Dempster and advanced by Shaffer [29][30][31][32][33][34]. It has superior ability to combine evidence from various data sources to actionable information.…”

Section: Fault Identification Based On Artificial Intelligence Techniquesmentioning

confidence: 99%

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Irungu

Akumu

2020

Energies

View full text Add to dashboard Cite

The healthiness and or faultiness of oil-immersed electrical equipment using dissolved gas characterization has remained a critical and challenging task in power systems. Dissolved gas analysis (DGA) continues to be the utmost preferred technique of detecting mainly slow evolving thermal and electrical faults. However, DGA can reveal more than just faults in equipment. This research looks at broad areas where DGA can be applied to determine the healthiness or faultiness of equipment in addition to fault identification. In equipment considered normal—i.e., fault-free—DGA can give the degree of healthiness (DOH) based on Rogers ratios C2H2/C2H4 < 0.1, 0.1 < CH4/H2 < 1, and C2H4/C2H6 < 1, plus the 3 < CO2/CO < 10 ratio for identifying fault-free devices. This answers the question: How healthy or normal is the equipment? Similarly, when these ratios are violated, it signifies the presence of faults, and two things ought to be determined. One is to identify the type of fault(s), which has been the norm. The other thing that can be evaluated is the degree of faultiness (DOF), based on the extent to which the ratios have been violated. Rarely has this been done. This might answer the question for the same fault class: How severe is the fault? To synthesize the DOH and/or DOF, fuzzy logic is applied. To diagnose faults, fuzzy logic and fuzzy-evidential tools are proposed. The accuracy and effectiveness of the proposed fuzzy techniques are better than those of the IEC60599 and Rogers methods, and they are comparable to those of the Duval Triangle 1 and Pentagon 1 methods using the six IEC faults. Results from DOF evaluation have shown electrical faults to be more impactful relative to the rest.

show abstract

Section: Fault Identification Based On Artificial Intelligence Techniquesmentioning

confidence: 99%

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Irungu

Akumu

2020

Energies

View full text Add to dashboard Cite

show abstract

“…Among them, mathematical statistics method [270], WA [83,[124][125][126][127], optimized neural network [208,209], BN [87,[166][167][168], and evidential reasoning approach [45,75,151,[210][211][212][213][214][215][216][217] have already appeared and have been preliminarily applied in the DGA-based fault diagnosis of the power transformers, and they are briefly reviewed as follows:…”

Section: Other Intelligent Diagnosis Toolsmentioning

confidence: 99%

“…Liao et al [215] developed an integrated decision-making model for condition assessment of power transformers using fuzzy approach and evidential reasoning method. Irungu et al [216] developed an integrated fuzzy-evidential reasoning approach in fault identification of power transformers using DGA. The results show that the assessing model is capable of offering an overall evaluation of the observed transformer.…”

Section: Other Intelligent Diagnosis Toolsmentioning

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

“…In addition to the methods summarized in the previous sections in this chapter, some other intelligent methods as powerful diagnosis tools have been developed and applied to power transformer fault diagnosis using DGA. Among them, mathematical statistics method [270], WA [83,[124][125][126][127], optimized neural network [208,209], BN [87,[166][167][168], and evidential reasoning approach [45,75,151,[210][211][212][213][214][215][216][217] have already appeared and have been preliminarily applied in the DGA-based fault diagnosis of the power transformers, and they are briefly reviewed as follows:…”

Section: Other Intelligent Diagnosis Toolsmentioning

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.

show abstract

Comparison of IEC 60599 gas ratios and an integrated fuzzy-evidential reasoning approach in fault identification using dissolved gas analysis

Cited by 6 publications

References 20 publications

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for 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

Contact Info

Product

Resources

About