A novel power system stabilizer based on neural network inverse system

Author

Hu, Zhijian ; Liang, Youwei ; Chen, Yunping ; Zhang, Chengxue

Author_Institution

Coll. of Electr. Eng., Wuhan Univ., China

fYear

2004

fDate

21-25 June 2004

Firstpage

510

Lastpage

514

Abstract

A novel power system stabilizer (PSS) based on the neural networks α-th order inverse system (called NNIPSS) is proposed in this paper. The reversibility of excitation system with PSS in power systems is proved firstly. Then selecting the power angle as the output variable, the control structure of the NNIPSS is given and the sample selection and training methods of the neural network inverse system are described in detail. In order to improve the dynamics performance and robustness of the pseudo-linear system, a fuzzy-PID controller is employed. Simulations of one machine infinite bus system show the effectiveness of the NNIPSS.

Keywords

data acquisition; fuzzy control; learning (artificial intelligence); neural nets; power system control; power system stability; three-term control; α-th order inverse system; data acquisition; excitation system reversibility; fuzzy-PID controller; infinite bus system; neural network inverse system; power system stabilizer; pseudo-linear system; sample selection method; Control systems; Control theory; Neural networks; Power system analysis computing; Power system control; Power system dynamics; Power system modeling; Power system simulation; Power system stability; Power systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2004. Proceedings. International Conference on

Print_ISBN

0-7803-8629-9

Type

conf

DOI

10.1109/ICIA.2004.1373423

Filename

1373423