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Voltage Sag Source Location Using Collaborative Training and Knowledge Transfer in Sparsely Measured Distribution Networks
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DOI：10.1109/tim.2026.3718088.png)
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Abstract
En 中文
Voltage sags can lead to substantial economic losses, making accurate source location the critical first step toward effective mitigation. Given that the absence of detailed network parameters constrains the use of impedance-based approaches, this study explores deep-learning-based alternatives for source location. To address the challenge of limited training samples—arising from sparse measurements in distribution networks—this work proposes a knowledge transfer framework employing a collaborative training strategy for voltage sag source location (VSSL). An interpretability-enhanced graph convolutional neural network (IIGCN) is developed, using the voltage magnitude depth difference (VMDD) derived from recorded waveforms as input features. A cross-region knowledge transfer framework is then introduced to capture general mapping relationships between VMDD and corresponding source positions. IIGCN models for different subregions are trained collaboratively, with parameter exchange and compensation mechanisms ensuring robust performance despite limited training data. The proposed approach is evaluated on both synthetic and real-world systems, achieving about 85 % accuracy under stringent training conditions. Comparative analyses demonstrate that the method consistently outperforms existing techniques, delivering superior VSSL performance across diverse application scenarios.
Keywords:
Cross-region knowledge transfer
distribution network
sparse voltage measurement
voltage sag source location (VSSL)
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5.9
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1.9W
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5.8W
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