An on-line self-learning power system stabilizer using a neural network method

被引:18
作者
Cheng, SJ [1 ]
Zhou, RJ [1 ]
Guan, L [1 ]
机构
[1] HUAZHONG UNIV SCI & TECHNOL,DEPT ELECT POWER ENGN,WUHAN 430074,HUBER,PEOPLES R CHINA
基金
中国国家自然科学基金;
关键词
D O I
10.1109/59.589773
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 [电气工程]; 0809 [电子科学与技术];
摘要
Based on the extensive theoretical analysis of self-learning algorithm a novel on-line neural network self-learning algorithm is proposed. This algorithm aims to learn the inverse dynamics of a controlled system. Samples can be easily obtained by the measurements. A reference model or a given orbit is used to generate ideal system responses. A scheme for on-line real-time implementation of such a controller is given. The proposed algorithm has been used to design a self-learning power system stabilizer. Simulation results show that the proposed self-learning neural network based PSS is very effective in damping out the lower frequency oscillations.
引用
收藏
页码:926 / 931
页数:6
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