Graph-based data-driven discovery of interpretable laws governing corona-induced noise and radio interference for high-voltage transmission lines

TL;DR

This paper introduces Mono-GraphMD, a graph-based symbolic discovery framework that uncovers interpretable laws governing corona-induced noise and radio interference in high-voltage transmission lines, improving prediction accuracy and understanding.

Contribution

The paper presents a novel monotonicity-constrained graph symbolic discovery method for deriving interpretable laws in high-voltage line emissions, surpassing empirical models in accuracy and interpretability.

Findings

Accurately predicts corona-induced noise and interference across multiple datasets.

Uncovers mechanistic laws linking surface gradient, bundle number, and diameter.

Provides portable, interpretable formulas for engineering design.

Abstract

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.