A new neural networks approach to on-line fault section estimation using information of protective relays and circuit breakers

1994 ◽  
Vol 9 (1) ◽  
pp. 220-230 ◽  
Author(s):  
Hong-Tzer Yang ◽  
Wen-Yeau Chang ◽  
Ching-Lien Huang
Keyword(s):  
Neural Networks ◽  
Circuit Breakers ◽  
On Line ◽  
Protective Relays

scholarly journals Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2565 ◽  
Author(s):  
Huizhong Song ◽  
Ming Dong ◽  
Rongjie Han ◽  
Fushuan Wen ◽  
Md. Salam ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Programming ◽  
Power Systems ◽  
Power System ◽  
Programming Approach ◽  
Chance Constrained Programming ◽  
Circuit Breakers ◽  
On Line ◽  
Protective Relays ◽  
Constrained Programming

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

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

An Adaptive State Filtering Algorithm for Systems With Partially Known Dynamics

2002 ◽  
Vol 124 (3) ◽  
pp. 364-374 ◽  
Author(s):  
Alexander G. Parlos ◽  
Sunil K. Menon ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Case Studies ◽  
Feedforward Neural Networks ◽  
Complex Case ◽  
State Equations ◽  
Estimation Errors ◽  
Filter Performance ◽  
On Line ◽  
Noise Statistics ◽  
On Line Learning

On-line filtering of stochastic variables that are difficult or expensive to directly measure has been widely studied. In this paper a practical algorithm is presented for adaptive state filtering when the underlying nonlinear state equations are partially known. The unknown dynamics are constructively approximated using neural networks. The proposed algorithm is based on the two-step prediction-update approach of the Kalman Filter. The algorithm accounts for the unmodeled nonlinear dynamics and makes no assumptions regarding the system noise statistics. The proposed filter is implemented using static and dynamic feedforward neural networks. Both off-line and on-line learning algorithms are presented for training the filter networks. Two case studies are considered and comparisons with Extended Kalman Filters (EKFs) performed. For one of the case studies, the EKF converges but it results in higher state estimation errors than the equivalent neural filter with on-line learning. For another, more complex case study, the developed EKF does not converge. For both case studies, the off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. On-line training further enhances filter performance, decoupling the eventual filter accuracy from the accuracy of the assumed system model.

Sign in / Sign up

Export Citation Format

Share Document