PMU based fault location for double circuit transmission lines in modal domain

Unde, Snehal; Dambhare, Sanjay

doi:10.1109/pesgm.2016.7741819

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…In Kang and Liao, 33 measurement errors for optimizing the presented approach for multi‐circuit lines are considered by current factors that are coordinated from one or multiple branches. Unde and Dambhare 34 have proposed a parallel PI circuit for a two‐circuit line for compensation of charging current effects. A specific transformer has been chosen to separate the desired line, and both terminals can be sampled simultaneously using current and voltage signals.…”

Section: Introductionmentioning

confidence: 99%

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

Taheri

Salehimehr

Sedighizadeh

2021

Int Trans Electr Energ Syst

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Summary 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.

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Section: Introductionmentioning

confidence: 99%