A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Abu-Siada, A.; Hmood, S.; Islam, Syed

doi:10.1109/tdei.2013.6678888

Cited by 113 publications

(60 citation statements)

References 15 publications

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“…Fuzzy logic is widely applied for dissolved gas analysis (DGA) of power transformers [13,14] since DGA contains relatively many input parameters. If binary logic is used a contradiction can occur, where some of the input parameters correspond with a good condition but others with a bad condition.…”

Section: General Description Of Oil Indicator Algorithmmentioning

confidence: 99%

Development of Indicators for Technical Condition Indexing of Power Transformers

Poiss¹,

Vītoliņa²,

Marks³

2018

Adv. sci. technol. eng. syst. j.

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Section: General Description Of Oil Indicator Algorithmmentioning

confidence: 99%

Development of Indicators for Technical Condition Indexing of Power Transformers

Poiss¹,

Vītoliņa²,

Marks³

2018

Adv. sci. technol. eng. syst. j.

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“…1  and 2  were set at 50 ppm for all MFs of hydrogen, and at 25, 2, 6 and 5 ppm for all MFs of CH4, C2H2, C2H4 and C2H6 respectively. These values lead to an overlap of 25% between adjacent MFs, as seen in Figure 2, and yield more accurate oil health indexes than MFs based on precise sets [3], [8]- [12], [20]. Additional information on MFs and degrees of membership can be found in [22] and [23].…”

Section: Membership Functions and Degrees Of Membershipmentioning

confidence: 99%

“…It has been reported in the literature that the service lifetime of power transformers is determined mainly by the lifetime of their insulation [3]. During normal operation, electrical and thermal stresses cause deterioration of the transformer oil and paper insulation, leading to accumulation of gases within the transformer oil.…”

Section: Introductionmentioning

confidence: 99%

Expert System for Health Index Assessment of Power Transformers

Ranga¹,

Chandel²

2017

ijeei

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A fuzzy logic based technique using dissolved gas analysis to determine the health index of the oil in a power transformer is proposed in the present paper. The proposed fuzzy model integrates the DGA standards and the practical knowledge of transformer diagnostics experts in making a consistent decision on the health condition of the transformer oil. Twenty different transformer oil samples of a power utility validate the reliability of the proposed diagnostic models. Furthermore, a fuzzy logic based overall transformer health assessment model has been proposed in this paper. This model uses several diagnostic tests data of transformers including water content, break down voltage, interfacial tension. It is envisioned that the proposed fuzzy models will prove very convenient even for inexperienced engineers to determine the health index of the transformers. This shall help in initiating suitable action for proper maintenance of the transformers and enhancing their remnant life.

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“…Its running state can directly affect the reliability of the entire power supply and system stability. Through an analysis of the dissolved gas in the oil, we can obtain the operation status of the transformer to determine the type of failure and can timely detect the potential risk of transformer operation failure [1][2][3][4]. It is important to study the prediction of dissolved gas concentration in oil, and its prediction results can provide a reference value for transformer state evaluation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

et al. 2018

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Abstract:The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer's operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.

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A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Cited by 113 publications

References 15 publications

Development of Indicators for Technical Condition Indexing of Power Transformers

Development of Indicators for Technical Condition Indexing of Power Transformers

Expert System for Health Index Assessment of Power Transformers

Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

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