Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Abo‐Elyousr, Farag K.; Nassrat, L. S.

doi:10.12928/telkomnika.v16i3.5103

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The contamination deposited on the insulator surface combined with the presence of moisture dissolves the salt deposited and encourages the formation of the conductive layer that acts as a highly variable and nonlinear resistor to be formed for LC to flow [3,4]. With high LC flowing between the high voltage and grounded side of insulator, flashover occurrence that will lead to power outage from the system is more likely to happen [5][6][7]. Flashover phenomenon reduces the power system reliability and efficiency, contributes to economic losses, and permanent insulator impairment [8,9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface leakage current of overhead insulators under environmental and electrical stresses

Abdullah¹,

Piah²,

Othman³

et al. 2020

Bulletin EEI

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Leakage current is one of the critical aspects to consider for overhead transmission line insulator’s condition and performance assessment. As the leakage current increase, the size of the dry band also will increase leads to the dry band arcing, deteriorate the insulator performance and contribute to the development of insulator flashover. Based on the literature study, other than the existence of contaminations on the surface of insulator combined with moisture, the variation in leakage current is also affected by the environmental and electrical stresses. Previous researches have shown the effect of environmental and electrical stresses on surface leakage current based on experimental and simulation results. This paper outlines an analytical approach based on dimensional analysis to propose a new mathematical model of leakage current under environmental and electrical stresses. To justify the applicability of the derived dimensional model, the new model has been validated using previous researcher’s experimental results. The validation indicated that the proposed model had shown a good agreement with the previous experimental results. The proposed dimensional equation for this research work can be potentially used as a predictive performance model to evaluate and monitor the leakage current and insulator’s performance

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Section: Introductionmentioning

confidence: 99%

Prediction of surface leakage current of overhead insulators under environmental and electrical stresses

Abdullah¹,

Piah²,

Othman³

et al. 2020

Bulletin EEI

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show abstract

“…the data acquisition analysis for multiple sensors using modulation frequency with telemetry technique can be done. [5]In addition, ANFIS is also widely used in various researches, such as the use of ANFIS for the stability of single machine power output [6] and multi engine [7], broiler cage temperature setting [8], color clashing on ancient inscriptions [9], for Short-Term Load Forecasting [10], Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators [11], for determination of PH value [12], to detect breast cancer [13], determination of river flow direction [14], Optimization of the Production of Inactivated Clostridium novyi Type B Vaccine [15], Quantitative Structure-Activity Relationship Model for HCVNS5B inhibitors based on an Antlion [16] determination of forecasting volcanic eruption [17], density prediction for long term wind power [18], prediction water quality index of Satluj river in India [19]. To predict the seismic strength level cannot be predicted certainty by stochastic method.…”

Section: Introductionmentioning

confidence: 99%

Determination Model of Tectonic Seismicity Level In Earthquake Early Warning System By Implementing ANFIS (Adaptive Neuro Fuzzy Inference Systems)

D.Y¹,

Yuliawati²,

Warsito³

et al. 2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Determination Model of tectonic seismicity level using ANFIS method in the real time earthquake early warning system will be described in this research. the system is based on the data from monitoring tectonic activities using flux magnet sensor, ground temperature sensor and Radio Frequency. The system learning uses recursive square estimator algorithm (RSQ) and backpropagation gradient descent. It results in an error value of 1.1527 for epoch 100 in the training process. Whereas, the test process results in an error value of 1.1527 and in outline that the result of determination with ANFIS is close to the actual value of the seismicity level.

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Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Abstract: Polluted insulators at high voltages has

Cited by 2 publications

References 26 publications

Prediction of surface leakage current of overhead insulators under environmental and electrical stresses

Prediction of surface leakage current of overhead insulators under environmental and electrical stresses

Determination Model of Tectonic Seismicity Level In Earthquake Early Warning System By Implementing ANFIS (Adaptive Neuro Fuzzy Inference Systems)

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