构建可扩展的电网资产风险模型，融合遥感数据预测植被与雷击风险。
Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling
- 基于多源遥感与地理数据，构建可扩展的资产级故障概率模型。
- 在真实电网数据上实现高精度风险分层，支持巡检与加固优先级决策。
- 模型模块化设计，适合电力公司长期维护与气候适应性调整。
电力网络日益面临天气相关的失效威胁，亟需在资产层面进行空间明确的风险建模以支持有效干预。本文提出一种模块化、稳健且可解释的故障概率（PoF）建模框架，适用于电力资产管理。核心贡献在于可扩展的资产级架构，能无缝集成新环境数据源和新增故障类型，无需重构底层流程。该框架特别适用于工业场景，确保模型在数据变化、管理重点转移和气候灾害加剧背景下仍具备可运维性。我们通过统一的地理空间机器学习流程，验证了针对植被与雷击两类故障模式的应用效果。模型整合了多种预测因子：地形（SRTM）、植被状态（MODIS NDVI）、雷暴气候学数据（LIS VHRMC）、OpenStreetMap提取的邻近特征以及电网运营记录。结果表明，该架构计算高效、可操作扩展，适用于大规模电网部署，能提供可行动的资产级风险排序，支持巡检优先、植被管理、设备加固与韧性规划，助力提前干预与更具气候韧性的电网运行。
原文摘要 · Abstract (English)
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.
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