A New Method for Flicker Severity Forecast

被引：0

作者：

Lu, H. J. ^{[1
]}

Chang, G. W. ^{[1
]}

Su, H. J. ^{[1
]}

机构：

[1] Natl Chung Cheng Univ, Dept Elect Engn, Chiayi 621, Taiwan

来源：

2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES) | 2013年

关键词：

Electric arc furnace; voltage fluctuation; neural network; grey theory; prediction; SELECTION; NETWORKS;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：5

共 16 条

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