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.

翻译：全球向可再生能源的转型促使超高压（UHV）交流输电技术发展，以弥合偏远能源基地与城市负荷中心之间的空间距离。尽管超高压电网具有优越的输电容量与效率，其实际部署常受电晕可听噪声（AN）和无线电干扰（RI）的制约。由于此类排放必须满足严格的环境合规标准，准确预测对于超高压基础设施的大规模建设至关重要。现有工程实践多依赖经验定律，但固定的对数线性结构限制了其预测精度与外推能力。本文提出一种单调性约束的图符号发现框架Mono-GraphMD，可揭示电晕AN与RI的紧凑可解释定律。该框架揭示了表面梯度、分裂数及子导线直径间的非线性相互作用如何主导高场强排放的物理机制，并实现了对电晕笼数据及多国实际超高压线路（含16分裂导线）的精确预测。与黑箱模型不同，所发现的闭合形式定律具有高度可移植性和可解释性，可快速应用于不同场景下的预测，从而辅助工程设计过程。