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Risk assessment of insulator contamination in thermal power plants by integrating dynamic physical priors and spatiotemporal heterogeneous graphs - Nature

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We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

AbstractTo address the problem of predicting insulator fouling risks at thermal power plants resulting from the combined effects of various source-side facilities and meteorological conditions, we propose the Causal-STHGN model. This spatiotemporal heterogeneous graph model integrates dynamic physical priors. This paper first constructs a semantic graph of power plant entities. Source-side facility nodes are mapped to insulator monitoring points as heterogeneous nodes. Then, by integrating spatial geometric relationships, wind field directionality, source-side state characteristics, and moisture condition modulation, the model calculates dynamic physical causal priors and edge-weight priors. Furthermore, it constrains spatial dependency learning through a prior-bias graph attention mechanism. A parallel temporal structure is employed to jointly model short-term disturbances and long-term evolutionary trends.

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