Optimised Time for Travelling Wave Fault Locators in the Presence of Different Disturbances Based on Real-World Fault Data

Abstract

The real-world travelling wave fault data investigated in this paper indicate disturbances generate unpredictable, non-stationary and random waveforms which may cause maloperation of protection and control elements in a power system including travelling wave fault locators (TWFL). This type of fault locator is directly dependent on the detection of an accurate time of arrival (ToA) of travelling waves (TW) generated by a fault. This detection becomes complicated in the presence of disturbances when their ToAs are detected earlier than the fault TWs. Since travelling waves occur in the high-frequency bands (e.g. >50 kHz), in this paper a capacitor voltage transformer is employed to measure the TW voltage signals; this involves acquiring the current flowing to the ground and removing the lowfrequency components (50/60 Hz). Disturbances create high magnitude pulses in the pre-fault section of a TW fault signal that last for a short time. Therefore, the time when a TWFL starts its computations requires to be optimised so that the effect of the disturbances is eliminated. The analysis techniques mentioned in this paper are based on real-world travelling wave fault data, and the solution uses statistical tools, such as cost function, mean and standard deviation, alongside Digital Signal Processing algorithms.Index Terms--fault location, optimised time, power network faults, power system disturbance, real-world data, short-circuit fault, travelling waves, travelling wave fault location

Analysis by Traveling Waves for a Protection Scheme of Transmission Lines with a UPFC

“…Las perturbaciones en las líneas de transmisión provocan un mal funcionamiento en los localizadores de fallas, ya sean por eventos de maniobra u otro tipo de perturbaciones, para atender este problema. En [23] se propuso una técnica de optimización del tiempo para usar con localizadores de fallas basados en ondas viajeras, esto evita perturbaciones y, como resultado, evita decisiones imprecisas y posibles operaciones incorrectas. Lo que indica que un evento de falla es diferente en tiempos, magnitudes y frecuencias que otros tipos de perturbaciones.…”

“…Para reducir el riesgo de malfuncionamiento del algoritmo para la localización de la falla es necesario complementar el entrenamiento con eventos de otro tipo de perturbaciones para contemplar escenarios más realistas, sin embargo, se ha demostrado en diversos trabajos [23][24], que para este tipo de algoritmos basados en TWs las diferencias entre los eventos son considerables, por lo tanto, en entrenamientos como el propuesto en este trabajo los resultados en la clasificación no variarían de forma.…”

Identification of Permanent Faults and Disturbance Induced by Deicing Based on Permutation Entropy for the Large-Scale Clean Energy Transmission in Winter

“…Zhuang et al and Nie et al [6,7] applied a trend prediction model for the ice freezing and development of deicing based on the micrometeorology monitor. e cause and the process of deicing faults are studied in [8][9][10][11][12][13][14][15][16][17][18][19][20], and the reinforcement of isolators of transmission lines is proposed. e filter is usually unset for the DC deicing device, so there are a large number of high-frequency harmonics in the output voltage.…”