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

2026-03-20

AI Summary

This study addresses the challenge of accurately modeling audible noise and radio interference from corona discharge on ultra-high-voltage transmission lines, a key bottleneck in engineering deployment. The authors propose Mono-GraphMD, a novel framework that introduces graph-driven symbolic regression to power engineering for the first time. Operating under monotonicity constraints, it automatically discovers compact, interpretable closed-form physical laws directly from data, elucidating the nonlinear interactions among conductor surface gradient, bundle number, diameter, and other parameters under high electric fields. By transcending the logarithmic-linear structure inherent in conventional empirical formulas, the method achieves both high interpretability and strong extrapolation capability. Validated on real-world ultra-high-voltage line and corona cage datasets from multiple countries, it delivers high prediction accuracy—even for configurations with up to 16 subconductors—and yields formulas with exceptional generalizability and practical engineering utility.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• corona-induced noise
• radio interference
• UHV transmission lines
• interpretable laws
• data-driven discovery

Innovation

• symbolic regression
• graph-based discovery
• interpretable laws
• corona-induced noise
• monotonicity constraint