Attribute selection for fault location recognition in transmission lines

2006 ◽  
Author(s):  
S.M. de Souza ◽  
A.P. Alves da Suva ◽  
A.C.S. Lima
Keyword(s):  
Transmission Lines ◽  
Fault Location ◽  
Attribute Selection ◽  
Location Recognition ◽  
Selection For

A Novel Fault-location Method for HVDC Transmission Lines Based on Concentric Relaxation Principle and Wavelet Packet

2020 ◽  
Vol 13 (5) ◽  
pp. 705-716
Author(s):  
Congshan Li ◽  
Ping He ◽  
Feng Wang ◽  
Cunxiang Yang ◽  
Yukun Tao ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Wave Attenuation ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Evaluation Process ◽  
Location Method ◽  
Hvdc Transmission ◽  
Instantaneous Energy

Background: A novel fault location method of HVDC transmission line based on a concentric relaxation principle is proposed in this paper. Methods: Due to the different position of fault, the instantaneous energy measured from rectifier and inverter are different, and the ratio k between them is the relationship to the fault location d. Through the analysis of amplitude-frequency characteristics, we found that the wave attenuation characteristic of low frequency in the traveling wave is stable, and the amplitude of energy is larger, so we get the instantaneous energy ratio by using the low-frequency data. By using the method of wavelet packet decomposition, the voltage traveling wave signal was decomposed. Results: Finally, calculate the value k. By using the data fitting, the relative function of k and d can be got, that is the fault location function. Conclusion: After an exhaustive evaluation process considering different fault locations, fault resistances, and noise on the unipolar DC transmission system, four-machine two-area AC/DC parallel system, and an actual complex grid, the method presented here showed a very accurate and robust behavior.

Fault location on transmission lines based on travelling waves using correlation and MODWT

2021 ◽  
Vol 197 ◽  
pp. 107308
Author(s):  
V.H. Gonzalez-Sanchez ◽  
V. Torres-García ◽  
D. Guillen
Keyword(s):  
Transmission Lines ◽  
Fault Location ◽  
Travelling Waves

Digital fault location for high resistance grounded transmission lines

1999 ◽  
Vol 14 (1) ◽  
pp. 80-85 ◽  
Author(s):  
T. Funabashi ◽  
H. Otoguro ◽  
Y. Mizuma ◽  
T. Kai ◽  
N. Takeuchi ◽  
...  
Keyword(s):  
High Resistance ◽  
Transmission Lines ◽  
Fault Location

scholarly journals Non-contact fault location and identification method for same-tower multi-circuit transmission lines

2021 ◽  
Vol 7 ◽  
pp. 147-158
Author(s):  
Tao Yin ◽  
Jian Li ◽  
Dongsheng Cai ◽  
Qi Huang ◽  
Weihao Hu
Keyword(s):  
Transmission Lines ◽  
Fault Location ◽  
Identification Method

scholarly journals A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 255
Author(s):  
Lei Wang ◽  
Yigang He ◽  
Lie Li
Keyword(s):  
Deep Learning ◽  
Transmission Lines ◽  
Fault Location ◽  
Short Term Memory ◽  
Engineering Application ◽  
Energy Operator ◽  
Sampling Frequency ◽  
Learning Technology ◽  
Long Distance ◽  
Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

Sign in / Sign up

Export Citation Format

Share Document