Transmission Line Distance Relaying Using On-Line Trained Neural Networks

2005 ◽  
Vol 20 (2) ◽  
pp. 1257-1264 ◽  
Author(s):  
A.H. Osman ◽  
T. Abdelazim ◽  
O.P. Malik
Keyword(s):  
Neural Networks ◽  
Transmission Line ◽  
On Line ◽  
Distance Relaying

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

On-line learning algorithms for locally recurrent neural networks

1999 ◽  
Vol 10 (2) ◽  
pp. 253-271 ◽  
Author(s):  
P. Campolucci ◽  
A. Uncini ◽  
F. Piazza ◽  
B.D. Rao
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Learning Algorithms ◽  
On Line ◽  
Locally Recurrent ◽  
On Line Learning

A New Distance Relaying Method with System Load Condition Considered in a Transmission Line

2005 ◽  
Vol 277-279 ◽  
pp. 686-691
Author(s):  
Hyun Kyung Moon ◽  
Seung Ho Hyun ◽  
Sung J. Lee
Keyword(s):  
Transmission Line ◽  
Fault Location ◽  
Load Condition ◽  
Fault Current ◽  
Loop Analysis ◽  
Fault Resistance ◽  
Before And After ◽  
Location Algorithm ◽  
Distance Relaying ◽  
Feed Effect

This paper presents a novel fault location algorithm for a distance relay of a transmission line. Under the assumption that the source voltages and impedances of both ends are not changed before and after a fault, the fault current and the voltage of the terminal end are estimated including the loading condition. Then, the fault location expression, independent of the fault resistance, is derived using these voltages and currents through a loop analysis, in the manner that the in-feed effect of the terminal end is eliminated. The suggested algorithm is applied to a typical transmission line to show its effectiveness.

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