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为综合分析电网故障风险要素,利用电力行业时空大数据,融合气象数据、灾害数据、城市人口、POI等多源数据的城市空间依赖性与空间异质性特征以及多元要素尺度差异性,采用多层线性回归(HLM)和地理加权回归(GWR)技术对电网故障风险的时空分布特征进行综合建模。以2017年广州市电网低压故障风险为例,HLM模型揭示了相关影响因子的多尺度层级特征。按照年度和月度构建不同GWR模型发现,不同月份不同区域电网低压故障风险的主要影响因素及其关联强度均存在较大差异。总的来说,HLM模型侧重分析影响要素的关系层级结构,而GWR模型则侧重于精细尺度下影响因素关系的量化分析,可为电网故障风险的预防与治理提供多视角、精细化的技术方法支撑。
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