Alternate and Effective Dissolved Gas Interpretation to Understand the Transformer Incipient Faults

Manoj, Teruvai; Ranga, Chilaka; Ghoneim, Sherif S. M.; Rao, U. Mohan; Abdelwahab, Saad A. Mohamed

doi:10.1109/tdei.2023.3237795

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…The trapezoidal MF with its degree of membership (DOM) is depicted in figure 3. DOM is calculated using (10). More information regarding MFs and DOMs can be found in [41,42].…”

Section: Fuzzificationmentioning

confidence: 99%

“…In (10), the input of the FL model is denoted as x, and μ A (x) is the degree of membership function. 'a' and 'b' are the lower and upper limits of the MF, respectively.…”

Section: Fuzzificationmentioning

confidence: 99%

“…These stresses accelerate the ageing of oil and paper insulation at a faster rate. Although ageing is a normal phenomenon in the continuous operation of transformers, accelerated ageing due to abnormal stresses must be avoided for a longer service life [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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An intelligence expert system for quality health monitoring of power transformers using testing attributes

Manoj

Ranga

2023

Eng. Res. Express

Self Cite

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Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in Reference [29]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

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“…The trapezoidal MF with its degree of membership (DOM) is depicted in figure 3. DOM is calculated using (10). More information regarding MFs and DOMs can be found in [41,42].…”

Section: Fuzzificationmentioning

confidence: 99%

“…In (10), the input of the FL model is denoted as x, and μ A (x) is the degree of membership function. 'a' and 'b' are the lower and upper limits of the MF, respectively.…”

Section: Fuzzificationmentioning

confidence: 99%

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An intelligence expert system for quality health monitoring of power transformers using testing attributes

Manoj

Ranga

2023

Eng. Res. Express

Self Cite

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“…Transformation of multiple indexes results into single-object projects through the grey correlation method is performed to obtain the degree of association. These methods are used in many evaluations, including mine cable fire hazards [21][22][23], the health of power transformers, and coal and gas protrusion risk.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Combustion Performance of the Usual Timbers in Furniture Using a Grey Correlation Method Based on Thermolysis, Ignition, and Flame Spread

Yu,

Song,

et al. 2024

Fire

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Timber is the most widely used material for furniture in view of its characteristics of light mass, high strength, easy processing, coloring, and decorative appearance. However, the flammability of wood has been frequently associated with increased fire intensity and the rapid spread of fire in buildings. In this paper, the combustion performance of six kinds of common furniture timber was investigated based on thermogravimetric analysis at 25–500 °C, cone calorimetry with 50 kW/m2 thermal radiation intensity, and flame spread experiments with 3 kW/m2 thermal radiation intensity. The ignition, weight loss, thermogenesis, smoke, and flame spread characteristics of these timbers were obtained. Subsequently, a comprehensive index system including thermal stability, heat release ability, smoke production capacity, and flame spreading speed was constructed to evaluate the combustion performance of the selected timbers. In addition, a grey correlation method relying on the game theory to assign weight was proposed for the quantitative analysis of the relevant evaluation indexes. As a result, the combustion performance of the six kinds of timber, which was defined as a specific value from poor to good, was as follows: pine (0.8696) > Chinese fir (0.8568) > Oriented Strandboard (OSB) (0.8425) > density board (0.8122) > plywood (0.8087) > elm (0.7909). Timber with poor combustion performance contributes to the reduction in fire risk in buildings. Our suggestions are of great significance for selecting furniture timber from the perspective of the prevention and control of building fires.

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“…Their operational efficiency is directly proportional to the total reliability of the power grid, which has long-term consequences for economies and society. Transformer health and lifetime must be ensured, necessitating handson inspections, labor-intensive maintenance, and periodic repairs [4,5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Transformer Health Based on the Degree of Polymerization of Insulating Paper

Albalawi

2023

INDJCSE

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Transformers are critical in providing reliable and efficient power distribution. Its health and operational efficiency are critical to ensuring an uninterrupted power supply. On a dataset of 147 transformers, this study proposes a multivariate regression approach to evaluating insulating paper conditions using machine learning techniques. The degree of polymerization (DP) will be predicted as a key indicator of transformer health using regression models (decision tree, random forest, gradient boosting trees). The Isolation Forest algorithm detected probable anomalies and highlighted transformants that deviated from expected readings. The K-Means identified distinct groups of transformants based on their chemical characteristics and DP values. The results comprehensively understand transformer safety and underscore machine learning's potential to revolutionize predictive maintenance strategies. The results indicated that the gradient-boosted tree regression tool developed a lower mean absolute percentage error (MAPE) of 1.345%, demonstrating a robust model that can provide accurate DP predictions.

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Alternate and Effective Dissolved Gas Interpretation to Understand the Transformer Incipient Faults

Cited by 9 publications

References 25 publications

An intelligence expert system for quality health monitoring of power transformers using testing attributes

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Evaluating the Combustion Performance of the Usual Timbers in Furniture Using a Grey Correlation Method Based on Thermolysis, Ignition, and Flame Spread

Machine Learning Methods for Transformer Health Based on the Degree of Polymerization of Insulating Paper

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