A neural network based power system stabilizer suitable for on-line training - A practical case study for EGAT system

被引：26

作者：

Changaroon, B

Srivastava, SC

Thukaram, D

机构：

[1] Elect Generating Author Thailand, Elect Maintenance Div, Nonthaburi 11130, Thailand

[2] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India

[3] EPSM, Energy Program, Asian Inst Technol, Pathumthani 12120, Thailand

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2000年 / 15卷 / 01期

关键词：

artificial neural network; functional link network; low frequency oscillations; on-line training; power system stabilizer;

D O I：

10.1109/60.849124

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 [动力工程及工程热物理]; 0820 [石油与天然气工程];

摘要：

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on Functional Link Network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks, Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS.

引用

页码：103 / 109

页数：7

共 19 条

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[2]

An on-line self-learning power system stabilizer using a neural network method [J].