Radial Basis Function Neural Network Application to Power System Restoration Studies

被引：7

作者：

Sadeghkhani, Iman ^{[1
]}

Ketabi, Abbas ^{[2
]}

Feuillet, Rene ^{[3
]}

机构：

[1] Islamic Azad Univ, Najafabad Branch, Dept Elect Engn, Najafabad 8514143131, Iran

[2] Univ Kashan, Dept Elect Engn, Kashan 8731751167, Iran

[3] Grenoble INP, Grenoble Elect Engn Lab G2ELab, F-38402 St Martin Dheres, France

来源：

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | 2012年 / 2012卷

关键词：

OVERVOLTAGES; ENERGIZATION;

D O I：

10.1155/2012/654895

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.

引用

页数：10

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