Fault diagnosis of power transformer based on multi-layer SVM classifier

Lv, Guorong; Cheng, Haozhong; Zhai, Hongchen; Dong, Liang

doi:10.1016/j.epsr.2004.07.013

Cited by 137 publications

(46 citation statements)

References 19 publications

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“…As some random factors exist in the test gear magnetic load test bearing accelerometer coupling tachometer driving motor parameter selection process, it is very difficult to choose the global optimal parameters that the result maybe imprecise and unreliable. Besides, some researchers [31,32] presented that a large value for c or a small value for c would over-fit the training data, and c plays an important role in the generalization performance of SVM. Therefore, the novel optimization method for these parameters is particularly important in the gearbox fault diagnosis.…”

Section: Performance Analysis Without Optimizationmentioning

confidence: 99%

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

2013

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Section: Performance Analysis Without Optimizationmentioning

confidence: 99%

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

2013

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“…( ) 32 17 (2,17), (3,18), (4,19), (5,20), (6,21), (7,22), (8,23), (9,24), (10,25), (11,26), (12,27), (13,28), (14,29), (15,30), (16,31), (17, 32)}; Otherwise, 0…”

Section: Frsn P Systems Diagnosis Matrix Reasoning Stepsmentioning

confidence: 99%

“…( ) 32 17 (7,5), (8,5), (9,6), (10,6), (11,7), (12,7), (13,8), (13,9), (14,8), (14,9), (14,10), (15,9), (15, 10)}; Otherwise, 0 ij s = ,…”

Section: Frsn P Systems Diagnosis Matrix Reasoning Stepsmentioning

confidence: 99%

“…In recent years, artificial intelligence approaches have been proposed with high performance programs and in developing more smart diagnostic techniques for power transformers based on DGA methods, such as support vector machine [1] [3] [4] [5], fuzzy logic [6] [7] [8] [9] [10], neural network [11]- [18], grey clustering [19] [20], wavelet networks [21].…”

Section: Introductionmentioning

confidence: 99%

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Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

Yahya¹,

Ai²,

Yahya³

2016

JILSA

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This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with "low", "medium", and "high" descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

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“…The development of artificial intelligence injects new vigor into this field and has aroused wide research enthusiasm. Relative scholars applied Artificial Neural Networks [6][7], Bayesian Networks [8], Support Vector Machines [9][10][11], and Relevance Vector Machine [12][13] and so on into transformer fault diagnosis and have achieved very fruitful results. The framework of this type methods is generally based on prior knowledge (pairs of fault data and fault result) to determine parameters of the classification models and then determine the fault type based on new fault data.…”

Section: Introductionmentioning

confidence: 99%

A New DGA Based Transformer Fault Diagnosis Scheme Suitable for Time-Series Fault Data

Liang¹,

Li²

2017

JRST

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The quality of original data is crucial to the performance of diagnosis model. To improve the performance of transformer diagnosis model based on Dissolved gas analysis (Dga), a new diagnosis scheme suitable for time-series dissolved gas data is proposed in this paper. after the analysis of traditional transformer diagnosis architecture, a fault data extraction step is added to the architecture to improve the quality of original fault data. The fault data extraction step is mainly composed of two parts, invalid data correction and determination of possible initial fault time based on fault early warning. finally, the numerical results validate that the accuracy and sensitivity of Dga based fault diagnosis for the transformer are improved by extracting fault feature of time-series data.

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Fault diagnosis of power transformer based on multi-layer SVM classifier

Cited by 137 publications

References 19 publications

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

A New DGA Based Transformer Fault Diagnosis Scheme Suitable for Time-Series Fault Data

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