面向电力线资产风险的可扩展地理空间机器学习：整合遥感技术开展雷击与植被风险建模

中文摘要 AI 辅助

电力网络日益暴露于对天气敏感的故障机制中，需要针对资产级别的空间显式风险建模，以支持有效的干预规划。本研究为公用事业资产管理提供了一种模块化、鲁棒且可解释的故障概率（PoF）建模框架。核心贡献是一种资产级架构，该架构可扩展至新的环境数据源及额外的PoF类型，无需重构底层流程，这对工业场景尤为重要——工业场景中风险模型必须在适应数据可用性变化、资产管理优先级调整及气候驱动灾害条件的同时保持可操作维护性。我们采用统一的地理空间机器学习流程，针对植被相关和雷击相关的故障模式验证了该框架，实现整合多源预测因子，包括地形（SRTM）、植被状况（MODIS归一化差异植被指数 - NDVI）、雷击气候学（LIS VHRMC）、OpenStreetMap衍生的邻近性特征及公用事业运营记录。该架构计算高效、可操作扩展，适合公用事业规模部署，能为检查优先级排序、植被管理、资产加固及韧性规划提供可操作的资产级风险分层，支持更早干预和更具气候韧性的网络运营。

英文摘要

Electric power networks are increasingly exposed to weather-sensitive failure mechanisms that require asset-level, spatially explicit risk modelling for effective intervention planning. This study contributes a modular, robust, and explainable probability-of-failure (PoF) modelling framework for utility asset management. The central contribution is an asset-level architecture that can be scaled to new environmental data sources and additional PoF types without reworking the underlying pipeline. This is particularly relevant for industry settings, where risk models must remain operationally maintainable while adapting to changing data availability, asset-management priorities, and climate-driven hazard conditions. We demonstrate the framework for vegetation-related and lightning-related failure modes using a harmonised geospatial machine-learning pipeline. The implementation integrates multi-source predictors, including topography (SRTM), vegetation condition (MODIS Normalised Difference Vegetation Index - NDVI), lightning climatology (LIS VHRMC), OpenStreetMap-derived proximity features, and utility operational records. The resulting architecture is computationally efficient, operationally extensible, and suitable for utility-scale deployment. It provides actionable asset-level risk stratification for inspection prioritisation, vegetation management, asset hardening, and resilience planning, supporting earlier intervention and more climate-resilient network operations.

发表机构

• SA Power Networks（SA电力网络公司）

机构由 AI 辅助整理，请以论文原文为准。