Modelling the Effect of Thermal Aging on Transformer Oil Electrical Characteristics Using a Regression Approach

Abdi, S.; Haddad, A.; Harid, Noureddine; Boubakeur, A.

doi:10.3390/en16010381

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…In particular, Risos (2018), Risos and Gouws (2019), and Abdi et al. (2023) have noted that the conductivity of insulating oil increases markedly with age. The magnitude of this effect depends on heating of the oil and, as noted by Clilverd et al.…”

Section: Discussionmentioning

confidence: 99%

Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data

Pratscher,

Ingham,

Mac Manus

et al. 2024

Space Weather

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Magnetotelluric (MT) impedances from 62 sites in southern South Island of Aotearoa New Zealand have been used to model geomagnetically induced currents (GIC) in four transformers during two solar storms. Induced electric fields during the storms are calculated from the MT impedances using the magnetic fields measured at the Eyrewell (EYR) geomagnetic observatory, approximately 200 km north of the study area. Calculated GIC during the sudden storm commencements (SSC) give a generally good match to GIC measured by the network operator, Transpower New Zealand. Long period GIC (periods longer than about 10,000 s) are less well modeled. Calculations based on thin‐sheet modeling, which has restrictions on the shortest period of variation which can be modeled, perform less well for the GIC associated with SSC, but are equally good, if not better, at modeling longer period GIC. Consistent underestimation of large GIC at one transformer (HWBT4) near Dunedin are likely to be the result of uncertainty in the assumed values of line, transformer, and earthing resistances. The assumption of a spatially uniform magnetic field across the study area, which is implied by use of the magnetic field measured at EYR as a basis for calculation, may also lead to incorrect calculation of GIC. For one storm use of magnetic field data from a magnetometer within the study area leads to much improved modeling of the observed GIC. This study compares modeled and measured GIC using specifically measured MT impedance data.

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Section: Discussionmentioning

confidence: 99%

Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data

Pratscher,

Ingham,

Mac Manus

et al. 2024

Space Weather

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“…As one of the most important components in power system, the operational condition of power transformers has a direct impact on the reliability and safety of power delivery in the electrical grid. Therefore, identifying potential issues with transformer performance in advance, predicting its future operating condition, identifying and solving problems in advance, can improving power supply reliability are of great significance [1][2][3]. In the past decade, Multiple researchers globally have undertaken in-depth investigations regarding forecasting the future operational conditions of transformers.…”

Section: Introductionmentioning

confidence: 99%

Research for transformer operation state prediction method based on BO-CNN-GRU

Zhou,

Xu,

Xing

et al. 2024

J. Phys.: Conf. Ser.

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In the realm of electrical engineering, this study introduces a novel approach for forecasting transformer operational conditions by leveraging BO-CNN-GRU (Bayesian Optimized Convolutional Neural Network Gated Loop Unit). The initial step involves an in-depth analysis of the key parameters that significantly impact the operational performance of the transformer. Then, the comprehensive weights of each characteristic parameter of the transformer are obtained by the G1 method, entropy weight method, and CRITIC method, and the comprehensive state data of the transformer is obtained. Finally, BO (Bayesian optimization) and CNN (convolutional neural network) are used to optimize the GRU neural network to form a comprehensive prediction model of the future operation state for the transformer based on BO-CNN-GRU. Employing transformer operation status data, we develop a unified neural network model to forecast the forthcoming operation statuses of transformers. This model can more accurately and faster predict the future status of transformers. By analyzing specific cases, it has been determined that the predictive model’s average accuracy in forecasting transformer operational status one month ahead is at an impressive 98.44%, which can accurately predict the future state changes of transformers.

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“…In recent years, new techniques, such as fuzzy inference systems (FLs) and ANN, have been applied to develop predictive models for the purpose of estimating the required parameters. These techniques are widely used and have been employed by many researchers for various engineering works [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging

Sayadi,

Mahi,

Fofana

et al. 2023

Energies

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The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

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Modelling the Effect of Thermal Aging on Transformer Oil Electrical Characteristics Using a Regression Approach

Cited by 11 publications

References 31 publications

Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data

Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data

Research for transformer operation state prediction method based on BO-CNN-GRU

Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging

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