Prediction of conductive interference from electric power converter using artificial neural network

Conductive interference occurs during operation of power converters and is a significant problem for complex electronic systems. Therefore, when designing electronic systems, it is necessary to evaluate the possible parameters of such interference in advance and take protection measures. The article proposes a practical technique for predicting the amplitude and duration of conductive interference from electricity converters using an artificial neural network. As an example, an experimental study of the parameters of conductive interference from the layout of the electricity generation system on board the aircraft was implemented. A data set for teaching a neural network has been prepared. Examples of training of an artificial neural network based on experimental data are given. For the maximum amplitude and duration of conductive interference, an acceptable divergence of the results is achieved at 795 and 1422 of the training era, respectively. For test sample, it was possible to achieve an average absolute percentage error 14.2 % and 20.3 %, to predict the amplitude and duration of conductive interference. Examples of forecasting the parameters of conductive interference using a trained neural network are given. The results obtained indicate the possibility of practical use of an artificial neural network to solve the tasks of predicting conductive interference. Further prospects for using this tool are seen in the tasks of forecasting side -by -power electromagnetic radiation from electronic devices, exposure to ultra -short pulses, etc.