Article ID Journal Published Year Pages File Type
7112794 Electric Power Systems Research 2015 8 Pages PDF
Abstract
Drastic variations of reactive power consumed by electric arc furnaces (EAFs) often lead to significant voltage fluctuations at the connecting network bus and yield noticeable flickers of lighting devices as well as cause malfunctions of the electrical equipment. If the flicker severity levels are predictable, corrective solutions such as controls of EAF electrodes and reactive power compensators can be developed to mitigate the voltage fluctuations. This paper presents a hybrid approach that combines an improved radial basis function neural network (IRBFNN) and Grey model for the forecast of flicker severity levels. Field measurements are used to train and implement the forecasting model. Test results of ΔV10, short-term flicker severity (Pst) and long-term flicker severity (Plt) obtained by the proposed and five other methods are then under comparisons. Results indicate that more accurate flicker forecast is obtained by adopting the proposed method.
Related Topics
Physical Sciences and Engineering Energy Energy Engineering and Power Technology
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