Hybrid RVM–ANFIS algorithm for transformer fault diagnosis

Fan, Jingmin; Wang, Feng; Sun, Qiuqin; Feng, Bin; Liang, Fangwei; Xiao, Xuanyi

doi:10.1049/iet-gtd.2017.0547

Cited by 42 publications

(27 citation statements)

References 26 publications

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“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, wavelet analysis (WA), optimized neural network, Bayesian network (BN), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

Section: Contentmentioning

confidence: 99%

“…The second one is to integrate the fuzzy diagnosis technique with other intelligent techniques to form hybrid fault diagnosis techniques, such as evolutionary fuzzy logic [52], grey relational fuzzy diagnosis algorithm [141], fuzzy Petri Nets knowledge representation algorithm [143], integrated neural fuzzy algorithm [55][56][57], FWNN [58], rough set based fuzzy diagnosis [58,144], fuzzy clustering algorithm [145][146][147], fuzzy C-means algorithm [148,149], and probabilistic fuzzy diagnosis algorithm [150][151][152]. For this research direction, a couple of examples are given as follows:…”

Section: Fuzzy Theory In Dga-based Transformer Fault Diagnosis: a Surveymentioning

confidence: 99%

“…Fan et al [55] proposed a hybrid method which combines the relevance vector machine and the adaptive neural fuzzy inference system to address the misdiagnosis of conventional methods that is caused by ambiguous characteristic of some of the record data for the analysis. This algorithm can achieve an accuracy rate as high as 95% and exceeds single adaptive neural fuzzy inference system, SVM, and ANN in distinguishing multiple faults and samples with ambiguous characteristic.…”

Section: Fuzzy Theory In Dga-based Transformer Fault Diagnosis: a Surveymentioning

confidence: 99%

“…can well solve the issues with fuzziness and uncertainty hard to accurately determine the fuzzy membership function the relationship between the transformer faults phenomena, fault causes, fault mechanisms and fault types is ambiguous the sample data is required to be complete in the fuzzy rule table first establish a DGA-based transformer fault database to formulate fuzzy rules then input the DGA data to conduct fuzzification, fuzzy processing and defuzzification then optimize the fuzzy rules based on the optimization algorithms finally repeat calculation in turn until optimal result is determined employ fuzzy correlation matrix [142] combined with evolutionary fuzzy logic [52] combined with grey relational fuzzy diagnosis algorithm [141] combined with Petri Nets knowledge representation algorithm [143] combined with integrated neural fuzzy algorithm [55][56][57] combined with FWNN [58,124] combined with rough set [58,144] fuzzy clustering algorithm [145][146][147] fuzzy C-means algorithm [148,149,155] probabilistic fuzzy diagnosis algorithm [150][151][152] combined with expert system [98] combined with DL, RL, and other ML methods…”

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 for Large Oil-Immersed Power Transformers: A Survey

Cheng

2018

Energies

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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.

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Section: Contentmentioning

confidence: 99%

Section: Fuzzy Theory In Dga-based Transformer Fault Diagnosis: a Surveymentioning

confidence: 99%

Section: Fuzzy Theory In Dga-based Transformer Fault Diagnosis: a Surveymentioning

confidence: 99%

Section: Advantages and Disadvantages Working Process Primary Meansmentioning

confidence: 99%

See 2 more Smart Citations

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

Cheng

2018

Energies

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Section: ★★★mentioning

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

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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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Health status diagnosis of distribution transformers based on big data mining

Wang

Chen

et al. 2019

Trans Emerging Tel Tech

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In the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.

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Hybrid RVM–ANFIS algorithm for transformer fault diagnosis

Cited by 42 publications

References 26 publications

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

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

Health status diagnosis of distribution transformers based on big data mining

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