Neural network based adaptive out-of-step protection strategy for electrical power systems

被引:0
作者
Abdelaziz, AY [1 ]
Irving, MR [1 ]
Mansour, MM [1 ]
ElArabaty, AM [1 ]
Nosseir, AI [1 ]
机构
[1] BRUNEL UNIV,DEPT ELECT ENGN & ELECT,UXBRIDGE UB8 3PH,MIDDX,ENGLAND
来源
ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS | 1997年 / 5卷 / 01期
关键词
neural networks; electrical power systems; out-of-step protection strategy;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Application of artificial intelligence to power systems has resulted in an overall improvement of solutions in many areas. This paper presents a new strategy for adaptive out-of-step protection of synchronous generators based on neural networks. The paper describes the neural networks architecture adopted as well as the selection of input features for training the neural networks. A feed forward model of the neural network based on the stochastic back-propagation training algorithm has been used to predict the out-of-step condition. Due to power network configuration changes, the performance of the protective relays can vary. Consequently, a new adaptive out-of-step protection strategy is suggested in this paper. This depends firstly on detecting the case of the system through case detection neural networks by some pre-fault local measurements at the machine to be protected, and then calculating the new out-of-step condition through an adaptive routine. The capabilities of the developed adaptive out-of-step prediction algorithm have been tested through computer simulation for a typical case study. The results of using the proposed algorithm demonstrate the adaptability of the proposed strategy.
引用
收藏
页码:35 / 42
页数:8
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