Application of fuzzy equivalent matrix for fault diagnosis of oil-immersed insulation

Zhang, G.; Yasuoka, K.; Ishii, Satoshi; Li, Yang; Zhang, Yan

doi:10.1109/icdl.1999.798957

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…In rows 16-20 (Table III) that have been taken from References [13,14], the IEC standard have incorrect diagnosis in two cases and in one case was unable to diagnose the fault, too, but the Mamdani fuzzy system proposed in this paper had only one incorrect diagnosis. Considering the fuzzy system results, it is observed that the results of fuzzy solve a great deal of the problems of conventional methods.…”

Section: Mamdani Methodsmentioning

confidence: 96%

“…In Table III, the existing faults mentioned in the first 15 rows are taken from Refernece [14]. From the 15 cases that had been tested, the IEC standard had only one incorrect diagnosis (Case 7) as the Table III shows and in two cases it was unable to diagnose Table II.…”

Section: Mamdani Methodsmentioning

confidence: 98%

“…In order to have efficient rules for evaluating fuzzy system responses, in this paper, we compare the system with the actual faults reported in References [10,13,14] that are shown in Table III. For each case the measured quantity of gases is mentioned in p.p.m.…”

Section: Mamdani Methodsmentioning

confidence: 99%

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Dissolved gas analysis of transformers using fuzzy logic approach

Aghaei

Gholami

Shayanfar

et al. 2009

Int Trans Elec Energy Syst

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SUMMARYThe power transformer is a major apparatus in a power system, and its correct functioning is vital to system operations. Failure of one power transformer may cause long interruptions in supply, costly repairs and loss revenue. The analysis of oil-immersed gases as a useful and effective method can prevent main faults through diagnosing internal faults of power transformers and proposing preventive operations. Usually the relation between faults and the reason of their existence in power equipments is complicated. Therefore, fuzzy logic method is proper option in the diagnosis of transformers internal faults. In this paper, three fuzzy methods are used for specifying the internal faults of transformer through the ratio method of oil-immersed gases. Finally, comparing with commonly used methods, the proposed methods found effective enough.

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Section: Mamdani Methodsmentioning

confidence: 96%

Section: Mamdani Methodsmentioning

confidence: 98%

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Dissolved gas analysis of transformers using fuzzy logic approach

Aghaei

Gholami

Shayanfar

et al. 2009

Int Trans Elec Energy Syst

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show abstract

“…To demonstrate the effectiveness of the proposed CMAC NN fault diagnosis method, twenty power transformer DGA results of from references [7][8][9] are tested. The detailed gas data are shown in Table 4, where the AFC, IEC and CMC express the actual fault type, the diagnoses of the IEC method and the proposed CMAC NN scheme, respectively.…”

Section: Tested Data Diagnosismentioning

confidence: 99%

“…In the past decade, various fault diagnosis techniques have been proposed that include the conventional key gas method, ratio method [2][3][4][5], and recently, the expert systems [6], neural network [7,8,13,14] and fuzzy logic approaches [9][10][11][12]. The conventional key gas or ratio method is based on experience in fault diagnosis using DGA data, where may vary from utility to utility due to the heuristic nature of methods and no general mathematical formulation can be utilized.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Hung

Wang

2004

Electric Power Systems Research

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Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method is presented for the fault diagnosis of power transformers. By introducing the IEC standard 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. With application of this scheme to published transformers data, the diagnoses demonstrate the new scheme with high accuracy and high noise rejection ability. Moreover, the results also proved the ability of multiple incipient faults detection.

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Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach

Hung

Wang

2003

Advances in Soft Computing

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Application of fuzzy equivalent matrix for fault diagnosis of oil-immersed insulation

Cited by 22 publications

References 3 publications

Dissolved gas analysis of transformers using fuzzy logic approach

Dissolved gas analysis of transformers using fuzzy logic approach

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach

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