4.6 Article

Waveform Measurement Unit-Based Fault Location in Distribution Feeders via Short-Time Matrix Pencil Method and Graph Neural Network

期刊

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 59, 期 2, 页码 2661-2670

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2022.3233556

关键词

Active distribution networks; fault location; graph neural network; short-time matrix pencil method; waveform measurement units

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This article proposes a fault location method in active distribution feeders using the Short-Time Matrix Pencil method (STMPM) and Graph Neural Network (GNN) based on Waveform Measurement Units (WMUs). The method includes two stages: STMPM captures the dominant modes of transient changes in WMUs' sinusoidal signals, and GNN uses the captured features to identify the fault location and type. The proposed method is tested on a modified IEEE network with distributed energy resource (DER) and shows advantages over conventional approaches in fault location accuracy.
This article proposes the use of the Short-Time Matrix Pencil method (STMPM) and Graph Neural Network (GNN) for fault location in active distribution feeders based on an emerging class of sensors, known as Waveform Measurement Units (WMUs). WMUs record synchronized voltage and current waveforms in the time domain with high sampling rates. The proposed fault location framework consists of two stages. In the first stage, STMPM is adopted to capture the dominant modes of the transient changes of WMUs' sinusoidal signals due to faults in different locations of the distribution grid. The second stage is to use a grid-informed GNN model to identify the fault location and type using the captured features of the signal before, during, and post-fault with STMPM. GNN can capture the spatial-temporal relationship between data from different sensors in different locations to enhance situational awareness and fault location accuracy. The proposed method is examined on a modified IEEE network with distributed energy resource (DER) and for transient symmetrical and asymmetrical faults under different loading, DER generation level, noises, and sensors' sampling rate conditions. The results show the merits of the proposed two-stage fault location framework compared to the conventional approaches; while a challenging problem is addressed in active distribution grids.

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