Graph-based data-driven discovery of interpretable laws governing corona-induced noise and radio interference for high-voltage transmission lines
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910061956169728 |
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| author | Xu, Hao Chen, Yuntian Kang, Chongqing Zhang, Dongxiao |
| author_facet | Xu, Hao Chen, Yuntian Kang, Chongqing Zhang, Dongxiao |
| contents | The global shift towards renewable energy necessitates the development of ultrahigh-voltage (UHV) AC transmission to bridge the gap between remote energy sources and urban demand. While UHV grids offer superior capacity and efficiency, their implementation is often hindered by corona-induced audible noise (AN) and radio interference (RI). Since these emissions must meet strict environmental compliance standards, accurate prediction is vital for the large-scale deployment of UHV infrastructure. Existing engineering practices often rely on empirical laws, in which fixed log-linear structures limit accuracy and extrapolation. Herein, we present a monotonicity-constrained graph symbolic discovery framework, Mono-GraphMD, which uncovers compact, interpretable laws for corona-induced AN and RI. The framework provides mechanistic insight into how nonlinear interactions among the surface gradient, bundle number and diameter govern high-field emissions and enables accurate predictions for both corona-cage data and multicountry real UHV lines with up to 16-bundle conductors. Unlike black-box models, the discovered closed-form laws are highly portable and interpretable, allowing for rapid predictions when applied to various scenarios, thereby facilitating the engineering design process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20600 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Graph-based data-driven discovery of interpretable laws governing corona-induced noise and radio interference for high-voltage transmission lines Xu, Hao Chen, Yuntian Kang, Chongqing Zhang, Dongxiao Symbolic Computation Artificial Intelligence Applied Physics The global shift towards renewable energy necessitates the development of ultrahigh-voltage (UHV) AC transmission to bridge the gap between remote energy sources and urban demand. While UHV grids offer superior capacity and efficiency, their implementation is often hindered by corona-induced audible noise (AN) and radio interference (RI). Since these emissions must meet strict environmental compliance standards, accurate prediction is vital for the large-scale deployment of UHV infrastructure. Existing engineering practices often rely on empirical laws, in which fixed log-linear structures limit accuracy and extrapolation. Herein, we present a monotonicity-constrained graph symbolic discovery framework, Mono-GraphMD, which uncovers compact, interpretable laws for corona-induced AN and RI. The framework provides mechanistic insight into how nonlinear interactions among the surface gradient, bundle number and diameter govern high-field emissions and enables accurate predictions for both corona-cage data and multicountry real UHV lines with up to 16-bundle conductors. Unlike black-box models, the discovered closed-form laws are highly portable and interpretable, allowing for rapid predictions when applied to various scenarios, thereby facilitating the engineering design process. |
| title | Graph-based data-driven discovery of interpretable laws governing corona-induced noise and radio interference for high-voltage transmission lines |
| topic | Symbolic Computation Artificial Intelligence Applied Physics |
| url | https://arxiv.org/abs/2603.20600 |