@article {4821,
	title = {
	Artificial neural networks for predicting the gassing tendency under electrical discharge in insulating oil for extended time, ISSN 1582-4594

},
	journal = {Journal of Electrical Engineering},
	volume = {14},
	number = {2},
	year = {2014},
	pages = {pp 45-52},
	abstract = {This study will focus on the investigation of the effect of electrical discharge on physical, chemical, electrical properties of transformer oil, and on the development of a mathematical model describing the gassing of insulating oil under electrical discharge, using the information contained in the measured values. For predicting the gassing tendency for extensive ageing periods, we use the model developed, for an intelligent system design. The predictor{\textquoteright}s parameters are chosen based on their influence degree by the electrical field. Various scenarios were considered. The study was carried on two types of fluids, under electrical stress for different ages. The 6802, 6181 and 924 ASTM tests methods were used for the measurements of parameters in degradation. All the results obtained are summarized and compared. The properties which are strongly dependent have been specified, a multiple linear regression model for each fluid as a function of its DDP, DDF, turbidity and aging period is developed. This model is for the estimation of the gas quantity cumulated under electrical discharge. The prediction is made, by implanting the stepwise regression results into a neural network system, which has been tested on experimental results obtained from laboratory samples, and high prediction accuracy has been achieved.},
	url = {https://www.researchgate.net/publication/297361317_Artificial_neural_networks_for_predicting_the_gassing_tendency_under_electrical_discharge_in_insulating_oil_for_extended_time},
	author = {Boudraa Saliha and Mokhnache Leila}
}
