Reconfigurable instrument for neural-network-based power-quality monitoring in 3-phase power systems

2013 ◽  
Vol 7 (12) ◽  
pp. 1498-1507 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso ◽  
David Granados-Lieberman ◽  
Roque A. Osornio-Rios
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Power Quality ◽  
Quality Monitoring ◽  
Power Quality Monitoring

Reconfigurable instrument for power quality monitoring in 3-phase power systems

2011 ◽  
Author(s):  
Rene J. Romero-Troncoso ◽  
Eduardo Cabal-Yepez ◽  
Arturo Garcia-Perez ◽  
Roque A. Osornio-Rios ◽  
Ricardo Alvarez-Salas ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Quality ◽  
Quality Monitoring ◽  
Power Quality Monitoring

Fault Location Method in Complex Power Network Based on Power Quality Monitoring Data

2013 ◽  
Vol 732-733 ◽  
pp. 1320-1327
Author(s):  
Jian Lan ◽  
Dong Xu Wang ◽  
Xue Zheng ◽  
Kai Pei Liu ◽  
Cai Xia Yang
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Fault Location ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Fault Line ◽  
Location Method ◽  
Voltage Dip ◽  
Power Quality Monitoring ◽  
Trained Neural Network

In this paper, a fault location method using power quality monitoring data is proposed. The fault location is divided into two classes including fault line recognition and fault distance calculation. Along the each power line in the target network some fictitious fault points are set and the fault voltage of each monitoring bus is calculated. The voltage dip calculation results and the fault line data are matched to form the learning samples and then train the designed fault line recognition BP artificial neural network. The trained neural network searches the fault line when actual fault occurs. After it finishes fault line searching the fault position is calculated using the function of voltage dip amplitude and fault position. The correctness and effectiveness of the proposed method is proved by the simulation results in standard IEEE-14 bus system.

scholarly journals Power Quality Monitoring and Disturbances Classification based on Autoencoder and Neural Network for Electrical Power Supply

2020 ◽  
Vol 18 ◽  
pp. 261-265
Author(s):  
A.D. Gonzalez-Abreu ◽  
◽  
V. Martínez Viol ◽  
M. Delgado Prieto ◽  
J.J. Saucedo-Dorantes ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Power Supply ◽  
Electrical Power ◽  
Quality Monitoring ◽  
Power Quality Monitoring
Sign in / Sign up

Export Citation Format

Share Document