A fault-location algorithm for parallel line based on the long short-term memory model using the distributed parameter line model

被引:7
|
作者
Taheri, Behrooz [1 ]
Salehimehr, Sirus [1 ]
Sedighizadeh, Mostafa [2 ]
机构
[1] Islamic Azad Univ, Fac Elect Biomed & Mechatron Engn, Qazvin Branch, Qazvin, Iran
[2] Shahid Beheshti Univ, Fac Elect Engn, Tehran, Iran
关键词
deep learning; fault location; long short-term memory (LSTM); power system protection; CIRCUIT TRANSMISSION-LINES; IMPEDANCE; PROTECTION; NUMBER; SCHEME;
D O I
10.1002/2050-7038.13032
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Parallel transmission lines are widely used in modern power systems due to their ability to increase power transmission and reliability of the power network. However, due to these networks' complexity, fault locating is a potential problem in these types of systems. Besides, when the wind farms enter these systems, a new challenge will be created for the fault location (FL) techniques; and the reason is that the impedance continuously varies in wind power plants. This paper proposes a novel FL algorithm for parallel transmission lines using the distributed parameter line model. This method uses long short-term memory networks for estimating source impedance and other network parameters. The algorithm shows accurate performance during noisy conditions and precisely determines the fault distance during both three-phase and single-phase faults. The presented method is implemented using PSCAD and Python software. The simulation results illustrate the applicability of the proposed method to estimate FL in the presence of wind farms in the system.
引用
收藏
页数:19
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