Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data

Jiménez-Aparicio, Miguel; Wilches-Bernal, Felipe; Reno, Matthew J.

doi:10.1109/access.2023.3296737

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Travelling-wave technique accuracy is improved based on the way the recorded wave is analysed, such as by using wavelet or artificial intelligence methods for wave analysis [10,11]. This analysis requires a small measurement window (e.g., 100 microseconds) for the time-domain features extraction [12]. While travelling-wave techniques for fault location in DNs benefit from enhanced accuracy through advanced wave analysis, a limitation arises from their requirement for a high-sampling device, thus limiting their fault analysis to only where the device is installed.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

Numair,

Aboushady,

Arraño-Vargas

et al. 2023

Energies

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Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network.

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Section: Introductionmentioning

confidence: 99%

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

Numair,

Aboushady,

Arraño-Vargas

et al. 2023

Energies

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Development of overcurrent relay based on wavelet transform for fault detection in transmission line

Ananwattanaporn,

Lertwanitrot,

Ngaopitakkul

et al. 2024

Sci Rep

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This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

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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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Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data

Cited by 4 publications

References 46 publications

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

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

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