Abstract
This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.
Highlights
With the development of the smart grid, phasor measurement units (PMUs) play an increasingly important role in data-driven real-time fault location
A new method of fault line selection and location based on D-PMU is proposed in [5]; through traveling wave signals collected by the D-PMU, fault locations are found accurately
Pix2Pix model was based on the PyTorch deep learning
Summary
With the development of the smart grid, phasor measurement units (PMUs) play an increasingly important role in data-driven real-time fault location. A new method of fault line selection and location based on D-PMU is proposed in [5]; through traveling wave signals collected by the D-PMU, fault locations are found accurately. This method requires clock synchronization and a high sampling frequency of PMUs [13,14]. A new method based on Pix2Pix is proposed to enhance the accuracy of traveling wave-based fault location. (3) Pix2Pix is proposed to locate fault points in transmission lines, and efficiently improve the accuracy of the arrival time of the traveling wave head.
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