An online parameter monitoring method for power transformers based on DEA

Dırık, Hasan; Ozdemir, Muammer

doi:10.1109/melcon.2012.6196628

Cited by 3 publications

References 10 publications

(9 reference statements)

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Bayesian estimation of electrical transformer parameters

Neumayer

Bretterklieber

Zangl³

2014

2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Electrical transformers are among the oldest and most well studied devices in electrical engineering. Accurate knowledge of the transformers parameters is still required for modeling and simulation of the behavior of the transformer in all scenarios -for the single component as well as for the transformer within a larger system. Measuring the transformer parameters requires several experiments covering both, AC and DC-current measurements. In this work we will present a model based approach to estimate transformer parameters from a reduced set of measurements. This includes the estimation of the DC resistance of the transformer from AC measurements only. The methodology is based on the Bayesian framework, which provides a powerful tool for gaining knowledge about the parameters of interest from indirect measurement data.

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Bayesian estimation of electrical transformer parameters

Neumayer

Bretterklieber

Zangl³

2014

2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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An Online Short-Circuit Impedance Estimation Approach to Power Transformer Fault Detection

Puntano,

Meira,

Ruschetti

et al. 2023

2023 XX Workshop on Information Processing and Control (RPIC)

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Investigation and Understanding the Conditions of Power Transformer Internal Faults using On-line Technique

Aburaghiega¹,

Farrag²,

Hepburn³

2024

RE&PQJ

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Loss of a power transformer in a utility, generation plant or process can cost many millions of pounds, depending on how long it is out-of-service. Internal faults are said to be the most likely cause of disruption in transformer performance and interruption of power supply. Improving understanding of the relationship between types of problem in transformers and their indicators will help to identify internal faults and their locations. Developing an on-line method to monitor and investigate the health conditions of the transformer will help asset managers assess plant while it is operational, leading to reduced running costs and increased life. The proposed method is based on measuring electrical parameters at both sides of a transformer to differentiate between healthy and faulty conditions while it is still in service. In this study, two windings of five sections each have been simulated. The values for each section’s resistance and impedance are calculated based on copper windings, interleaved construction and insulation between sections. The simulated power transformer is connected to an inductive load. Comparison of simulated input and output voltages and currents has been conducted to identify indicators of the transformer’s health status. Developed faults at different locations in the transformer windings are used to study the transformer performance and to recognise the fault indicators. The simulation results show very clearly that there are trends in the measured parameters that are attributed to the type of fault within the transformer. Hence a simple logic comparator can be easily deployed to identify the fault location in relation to the transformer health status.

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An online parameter monitoring method for power transformers based on DEA

Cited by 3 publications

References 10 publications

Bayesian estimation of electrical transformer parameters

Bayesian estimation of electrical transformer parameters

An Online Short-Circuit Impedance Estimation Approach to Power Transformer Fault Detection

Investigation and Understanding the Conditions of Power Transformer Internal Faults using On-line Technique

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