Smarter Grids Through Deeper Learning

Spatiotemporal Graph Models for Distribution Systems

Modern power-distribution systems are becoming increasingly complex due to widespread integration of distributed-energy resources and the need for fast, reliable fault detection. This talk explores recent advances in graph-based deep learning for power systems, which model networks as spatiotemporal graphs to capture both spatial structure and measurement dynamics.

Focusing on deep sparse spatiotemporal generative models, including graph convolutional autoencoders and generative architectures, the talk highlights applications such as fault classification and location in distribution systems and behind-the-meter load and photovoltaic disaggregation. Experimental results show substantial performance gains over existing state-of-the-art methods.

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