Prediction of insulating transformer oils breakdown voltage considering barrier effect based on artificial neural networks

Ghoneim, Sherif S. M.; Dessouky, Sobhy S.; Elfaraskoury, Adel A.; Sharaf, Ahmed B. Abo

doi:10.1007/s00202-018-0697-5

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…Therefore, enhancing the dielectric strength of the insulating oil is the main task in research [1]. The barrier is a technique utilized to increase the breakdown voltage (VBD) of the insulating oil because it prevents particle movements and avoids the charge development inside the insulating oil [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Several works discussed the effect of barrier parameters (barrier effect) on the dielectric strength of the insulating oil, such as gap space (d) between the electrode system, the barrier position in the gap space (a/d), the barrier diameter (D), the barrier thickness (th), the electrode configuration, and the oil temperature [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The researchers tried to build a prediction equation related to the barrier parameters and the VBD of the insulation oils [1][2][3]. These works were carried out using the Design of Experiments (DOE) technique based on the results of the experimental outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Transformer Oil Breakdown Voltage with Barriers Using Optimization Techniques

Ghoneim¹,

Alharthi²,

El‐Sehiemy³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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A new procedure to optimally identifying the prediction equation of oil breakdown voltage with the barrier parameters' effect is presented. The specified equation is built based on the results of experimental works to link the response with the barrier parameters as the inputs for hemisphere-hemisphere electrode gap configuration under AC voltage. The AC HV is applied using HV Transformer Type (PGK HB-100 kV AC) to the high voltage electrode in the presence of a barrier immersed in Diala B insulating oil. The problem is formulated as a nonlinear optimization problem to minimize the error between experimental and estimated breakdown voltages (VBD). Comprehensive comparative analyses are addressed using three recent innovative marine predators, grey wolf, and equilibrium optimization algorithms to reduce the error between the experimental and estimated breakdown voltages. In addition, the experimental results are expressed in two different models via grey and real coding. Finally, simulation results are conducted with statistical indices that show the effectiveness of the proposed models for experimental verification. The total percentage errors of the tested samples between the observed and estimated VBD are 1.207, 1.222, and 1.207 for EO, GWO, MPA, respectively. The marine predator algorithm has the best performance compared with the other two competitive algorithms for grey and actual codes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Transformer Oil Breakdown Voltage with Barriers Using Optimization Techniques

Ghoneim¹,

Alharthi²,

El‐Sehiemy³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Recently, artificial intelligence and machine learning techniques have been employed in several applications with promising performance in power system applications with renewables [17][18][19][20][21][22][23][24][25][26][27][28]. Specifically, an artificial neural network (ANN) was presented in [29] to study the barrier effect parameters on the VBD of the transformer insulating oil for the point/plane and plane/plane gaps under AC high voltage. The results showed the ANN model's capability to predict the VBD with the barrier parameters' variation.…”

Section: Introductionmentioning

confidence: 99%

Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects

et al. 2021

Self Cite

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In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.

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“…Intelligent algorithms are often used for data prediction, among which the more common methods include the neural network and the Prophet model. e neural network has been applied in many fields, such as the prediction of power system load [5], oil breakdown voltage [6], and railway freight volume [7]. And the Prophet model has obtained good effect as well in the prediction of bank reserve payment [8], product sales volume [9], daily air quality index [10], and other aspects.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms

Ye¹,

Yang²,

Wu³

et al. 2020

Journal of Control Science and Engineering

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As the key metering equipment in the smart grid, the measurement accuracy and stability of electronic transformer are important for the normal operation of power system. In order to solve the problem that there is no effective way to predict the error developing trend of electronic transformer, this paper proposed two kinds of short-term prediction methods for electronic transformer error based on the backpropagation neural network and the Prophet model, respectively. First, preprocessing and visualization operation are performed on the original error data. Then, the data fitting and short-term prediction of electronic transformer error are made on the basis of the backpropagation neural network and the Prophet model, and the fitting and prediction results of the two methods are compared and analysed in combination with four evaluation indexes. Finally, the Prophet model is adopted to simulate the development trend and periodic fluctuation of error, and the reason for fluctuation is analysed. The simulation results show that the Prophet model is more suitable for the prediction of electronic transformer measurement error than the backpropagation neural network.

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Prediction of insulating transformer oils breakdown voltage considering barrier effect based on artificial neural networks

Cited by 12 publications

References 11 publications

Prediction of Transformer Oil Breakdown Voltage with Barriers Using Optimization Techniques

Prediction of Transformer Oil Breakdown Voltage with Barriers Using Optimization Techniques

Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects

Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms

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