
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Predicting Insulation Resistance of Enamelled Wire using Neural Network and Curve Fit Methods Under Thermal Aging
Health monitoring has gained a massive interest in power systems engineering, as it has the advantage to reduce operating costs, improve reliability of power supply and provide a better service to customers. This paper presents surrogate methods to predict the electrical insulation lifetime using the neural network approach and three curve fitting models. These can be used for the health monitoring of insulating systems in electrical equipment, such as motors, generators, and transformers. The curve fit models and the supervised backpropagation neural network are employed to predict the insulation resistance trend of enameled copper wires, when stressed with a temperature of 290 °C. After selecting a suitable end of life criterion, the specimens’ mean time-to-failure is estimated, and the performance of each of the analyzed models is apprised through a comparison with the standard method for thermal life evaluation of enameled wires. Amongst all, the best prediction accuracy is achieved by a Backpropagation neural network approach, which gives an error of just 3.29% when compared with the conventional life evaluation method, whereas, the error is above 10% for all the three investigated curve fit models.
- University of Bergamo Italy
- Nottingham Trent University United Kingdom
- Loughborough University United Kingdom
Curve Fit Models; Neural Network; Thermal Aging, Curve Fit Models, Thermal Aging, Neural Network, Settore ING-IND/32 - Convertitori, Macchine e Azionamenti Elettrici
Curve Fit Models; Neural Network; Thermal Aging, Curve Fit Models, Thermal Aging, Neural Network, Settore ING-IND/32 - Convertitori, Macchine e Azionamenti Elettrici
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
