Implementation of DSP based numerical three-step distance protection scheme for transmission lines

Author(s):  
M. Sreeram ◽  
P. Raja
2021 ◽  
Vol 256 ◽  
pp. 02017
Author(s):  
Zeya Fang ◽  
Minghao Wen ◽  
Junchao Zheng ◽  
Minghao Wen

DC line fault is one of the key problems that must be solved in a flexible HVDC system. During quite a long time between the existing main protection and backup protection of the HVDC line, there is no line protection method to detect the fault, which may lead the protection at the AC side to act before the backup protection of the DC line. To solve the problem, a novel two-step distance protection for flexible HVDC lines is proposed in this manuscript. Firstly, based on the uniform distributed parameter model, the equivalent lumped parameter model of the HVDC transmission line at low frequency is analyzed. Secondly, according to the time domain differential equation and the least squares algorithm, novel distance protection based on the iterative calculation of fault distance is proposed, which can eliminate the influence of distributed capacitive current and improve the precision of calculation. To improve the rapidity and reliability of the distance protection, low pass filters with two different cut-off frequencies are used to process the electrical quantities. Finally, simulation results show that the proposed distance protection can respond to metallic poleto-ground faults and pole-to-pole faults rapidly and reliably.


Author(s):  
Anil Vaidya ◽  
Prasad A. Venikar

Traditional electromechanical distance relays used for protection of transmission lines are prone to effects of fault resistance. Each fault condition corresponds to a particular pattern. So use of a pattern recognizer can improve the relay performance. This paper presents a new approach, known as artificial neural network (ANN) to overcome the effect of fault resistance on relay mal-operation. This method is based on pattern recognition and classification. The scheme utilizes the magnitudes of resistance and reactance as inputs. Once trained with a large number of patterns corresponding to various conditions, it can classify unknown patterns. It also has the advantage that it can adapt itself with the changing network conditions.


Author(s):  
Mahyar Abasi ◽  
◽  
Ahmad Torabi Farsani ◽  
Arash Rohani ◽  
Arsalan Beigzadeh ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document