Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Prabakar, Kumaraguru; Singh, Akanksha; Reynolds, Matthew; Lunacek, Monte; Monzón, Lucas; Velaga, Yaswanth Nag; Maack, Jonathan; Tiwari, Soumya; Roy, Jinia; Tombari, Colin; Mendoza, Ismael; Yang, Xinshuo; Iyengar, S. T.; John, W. Durban; Anand, Dhananjay; Gopstein, Avi; Vaidhynathan, Deepthi

doi:10.2172/1814596

Cited by 8 publications

(2 citation statements)

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“…The study focused on understanding the behavior and propagation of traveling waves in power transmission networks through theoretical analysis, laying the foundation for subsequent research in fault detection and localization. Prabakar et al [12] explored the utilization of traveling wave signatures for fault detection and location in medium-voltage distribution systems. The study employed case studies, signal processing techniques, and fault detection algorithms to demonstrate the effectiveness of traveling waves in enhancing fault management in distribution networks.…”

Section: Literature Surveymentioning

confidence: 99%

Signal-based fault location method using time and frequency domain

Ghodake

2024

IJSREM

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This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

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Section: Literature Surveymentioning

confidence: 99%

Signal-based fault location method using time and frequency domain

Ghodake

2024

IJSREM

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“…Other works propose the usage of time-domain methods, such as TW protection [27]. One of the most advantageous features of TWs is that they are agnostic to steady-state power flows and propagation mainly depends on line impedance [28]. The TW phenomenon originated from a sudden electric charge movement when conductors (including the conductive ground) get in touch.…”

Section: Introductionmentioning

confidence: 99%

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Jimenez-Aparicio,

Patel,

Reno

et al. 2023

IEEE Access

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This work provides a detailed protection analysis of a fast, Traveling-Wave (TW), Machine-Learning (ML), local, non-directional, economic, and setting-less protection scheme. The goal is to provide an objective evaluation of how a data-driven TW protection scheme would perform when deployed on a power distribution system as a faster alternative to over-current (OC) protection. Fault simulations consider scenarios from none to high-penetration of renewable energy resources to show its suitability for future applications on Distributed Energy Resources (DER)-dominated distribution systems. A modified IEEE 34 node system with solar Photovoltaic (PV) in multiple locations is modeled in PSCAD/EMTDC. The TW, ML method's protection scheme is built upon an efficient signal-processing stage, using the Discrete Wavelet Transform, and scaled-down Random Forest models that classify the fault location into several protection zones. The analysis is focused on the quantification of the sensitivity and selectivity of the proposed protection scheme. Furthermore, estimations of the false trip probability and average unnecessary load loss are included, as these events may occur due to the misoperation and miscoordination of the proposed datadriven, signal-based relays. Results show high TW detection rates and fault location accuracies, which cause a small and bounded percentage of imprecise fault locations and unnecessary load loss. Similarly, the same IEEE 34 node system is modeled in OpenDSS and a custom OC protection scheme is designed. The comparison between both methods leads to the conclusion that TW, ML methods can be a significantly faster aid to traditional protection.

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A travelling wave-based fault locator for radial distribution systems using decision trees to mitigate multiple estimations

Lessa

Grilo

Moraes

et al. 2023

Electric Power Systems Research

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Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Abstract: Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 8 publications

References 70 publications

Signal-based fault location method using time and frequency domain

Signal-based fault location method using time and frequency domain

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

A travelling wave-based fault locator for radial distribution systems using decision trees to mitigate multiple estimations

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