A new method for flicker severity forecast

H. J. Lu, G. Chang, H. Su
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引用次数: 2

Abstract

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. Test results based on the proposed model are compared with two other neural network methods. It shows that more accurate forecast is achieved for the flicker prediction based on the proposed method.
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一种新的闪烁强度预报方法
对于电弧炉(EAF)负荷快速无功消耗引起的电压剧烈波动,准确预测闪变水平是非常重要的。本文提出了一种基于灰色理论和径向基函数神经网络(RBFNN)相结合的预测模型,分别用于直流和交流电炉负荷运行时闪变严重程度的预测。基于该模型的测试结果与其他两种神经网络方法进行了比较。结果表明,基于该方法的闪变预测精度较高。
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