The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Precisely forecasting the flicker level is important for drastic voltage fluctuations associated with the rapid reactive power consumptions of electric arc furnace (EAF) loads. This paper presents a prediction model based on grey theory combined with radial basis function neural network (RBFNN) for the forecast of flicker severity caused by the operation of a dc and an ac EAF loads, respectively....
It is known that rapid voltage fluctuations caused by electric arc furnace loads may generate significant levels of flickers, which have negative impacts to human eyes and power system components. This paper proposes a grey predictor model for the forecast of flicker levels produced by an electric arc furnace load. Actual measured data are adopted to implement the predictor model. Test results based...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.