Multifactorial condition assessment for power transformers

The utilization of data-driven artificial intelligence technology for ultra-high voltage converter valve state assessment can improve efficiency and accuracy, but there are still problems such as imbalance of raw data and poor model interpretability. This paper aims to propose a new type of ultra-high voltage converter valve state assessment method to solve these problems. An oversampling method that considers characteristic boundary information is proposed, which can retain and enhance the boundary information of minority samples. The balanced data are imported into categorical boosting to realize offline training and online evaluation of converter valve state assessment. An interpretability analysis framework of the converter valve state assessment model based on the Shapley additive explanations is proposed, which can explain the operation logic of the model and find the key factors that affect the assessment results. The calculation example shows that the evaluation accuracy of this method can reach up to 98.5%, and the running speed is faster than the conventional method. The results can provide guidance for the maintenance and repair of UHV converter valves.

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“…Because these values have different dimensions, they also need to be standardized, as shown in formula (1).…”

Section: Valve Arrester Assemblymentioning

confidence: 99%

“…In recent years, electrical equipment maintenance methods are gradually changing from traditional scheduled maintenance to condition-based maintenance [1]. State assessment of electrical equipment and then accurately judging its operating state are the basis of achieving condition-based maintenance [2].…”

Section: Introductionmentioning

confidence: 99%

A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

Mei

Zheng

et al. 2022

IET Generation Trans & Dist

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“…This system includes the implementation of continuous condition monitoring and regular maintenance of power transformers. This results in efficiency improvement, service life extension, and optimal performance of transformers [3,4]. In general, a noticeable part of failures in power transformers is primarily caused by their insulation failure [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Manoj

Ranga

2023

Eng. Res. Express

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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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“…Power transformers (PT) are generally considered to be the most crucial and expensive asset within an electrical transmission system [1]. As a result of the current worldwide increasing demand for electricity, the load on transformers is also increasing and yet most transmission systems currently have large fleets of aged transformers [2].…”

Section: Introductionmentioning

confidence: 99%

Methodology for scheduling long‐term replacement of aging power transformers, considering risk

Piamba

Romero

Mayor

2023

IET Generation Trans & Dist

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This article presents an optimization methodology to schedule the replacement of power transformers (PT) into a fleet. The objective is the minimization of the summation of the risk indices of the PT. Each PT risk index is calculated from the estimation of the life used and the strategic importance of the unit. The PT life used is estimated as a relationship of the solid insulation polymerization degree, where aging processes due to oxidation, hydrolysis, and pyrolysis are considered from the calendar date when the unit starts its operation until different future scenarios. For the calculation of the PT strategic importance, financial, security, environmental, and network performance aspects are considered. Then, using the optimization model, together with the CAPEX and the available budget over a planning time, a strategy for optimally replacing the critical units is determined. The model was applied for a group of 102 units, demonstrating its applicability and effectiveness. The developed methodology serves to support the manager of these assets in making decisions in the long term.

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Multifactorial condition assessment for power transformers

Cited by 25 publications

References 35 publications

A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

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

Methodology for scheduling long‐term replacement of aging power transformers, considering risk

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