考虑气候变化与植被管理影响的配电网风险评估
Risk Assessment of Distribution Networks Considering Climate Change and Vegetation Management Impacts
AI总结：
本文提出基于逻辑回归的配电网停电风险评估模型，融合气候与植被数据，识别风速、EVI和湿雪等关键风险因素，并在618个测试样本上实现80%匹配率，为高风险环境下预防停电提供决策支持。
摘要：
本文提出了一个面向配电网络的综合风险评估模型，重点关注气候条件和植被管理对停电风险的影响。利用包含停电记录、气象指标和植被指标的数据集，本文建立了一个逻辑回归模型，该模型在多个备选模型中表现最优，能够有效识别高度不平衡数据中的风险因素。影响停电的关键特征包括风速、以增强型植被指数（EVI）量化的植被密度以及降雪类型，其中湿雪和秋季条件表现出最强的正向效应。分析还揭示了复杂的交互作用，例如风速与EVI的联合效应，表明植被密度可以调节大风对停电的影响。基于包含618个样本的测试数据集进行的仿真案例研究表明，在±0.05的误差容限内，该模型与真实数据的匹配率达到80%，展示了所提模型的有效性和鲁棒性，同时凸显了其在高风险环境条件下为配电网停电风险缓解提供预防策略参考的潜力。未来工作将整合来自激光雷达的植被高度数据，并探索替代建模方法以捕捉潜在的非线性关系。
英文摘要：
This paper presents a comprehensive risk assessment model for power distribution networks with a focus on the influence of climate conditions and vegetation management on outage risks. Using a dataset comprising outage records, meteorological indicators, and vegetation metrics, this paper develops a logistic regression model that outperformed several alternatives, effectively identifying risk factors in highly imbalanced data. Key features impacting outages include wind speed, vegetation density, quantified as the enhanced vegetation index (EVI), and snow type, with wet snow and autumn conditions exhibiting the strongest positive effects. The analysis also shows complex interactions, such as the combined effect of wind speed and EVI, suggesting that vegetation density can moderate the impact of high winds on outages. Simulation case studies, based on a test dataset of 618 samples, demonstrated that the model achieved an 80\% match rate with real-world data within an error tolerance of \(\pm 0.05\), showcasing the effectiveness and robustness of the proposed model while highlighting its potential to inform preventive strategies for mitigating outage risks in power distribution networks under high-risk environmental conditions. Future work will integrate vegetation height data from Lidar and explore alternative modeling approaches to capture potential non-linear relationships.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
