Development of an Intelligent Forecasting Unit for the Protection Device Against Leakage Currents in Electric Motors

Abstract

The use of more advanced electric motor protection devices, that are able not only to record, but also to predict the achievement of dangerous values the leakage current, makes it possible to warn and inform in advance about a possible danger for service personnel. Most often, neural networks are used to solve this problem. On the basis the obtained experimental data, neural networks were synthesized, both on the basis technological parameters and on the basis theory of time series forecasting. A comparison of the operating features of a neural network based on technological parameters and a neural network based on the theory of time series forecasting indicates that: the first type of neural network works more efficiently with sharp emissions of the predicted leakage current; the second type of neural networks more accurately models the value of the predicted value near its relatively averaged readings. The forecasting features these neural networks proved the feasibility of combining them into a single intelligent block with the possibility of choosing the best forecast at a certain point in time.