Price Prediction based Congestion Management using Growing RBF Neural Network

被引：0

作者：

Pandey, Seema N. ^{[1
]}

Tapaswi, Shashikala ^{[1
]}

Srivastava, Laxmi ^{[2
]}

机构：

[1] ABV IHTM, Dept Informat Technol, Gwalior, India

[2] MITS, Dept Elect Engn, Gwalior, India

来源：

PROCEEDINGS OF THE INDICON 2008 IEEEE CONFERENCE & EXHIBITION ON CONTROL, COMMUNICATIONS AND AUTOMATION, VOL II | 2008年

关键词：

Congestion zones; Congestion management; Growing radial basis function neural network; Nodal congestion prices; Optimal power flow; Vector quantization clustering;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a Growing Radial Basis Function (GRBF) Neural Network based methodology for Nodal Congestion Price (NCP) prediction for congestion management in emerging restructured power system. An unsupervised learning vector quantization (VQ) clustering has been employed as feature selection technique for GRBF neural network as well as for partitioning the power system into different congestion zones. This ensures faster training for proposed neural network and furnishes instant and accurate NCP values, useful for Congestion management under real time power market environment. A case study of RTS 24-bus system is presented for demonstrating the computational efficiency and feasibility of this approach.

引用

页码：482 / +

页数：3

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