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XGBoost算法下供电线路停电敏感度识别

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原文链接
  • NETL
  • NSTL
  • 万方数据
由于忽略了数据时序特征,供电线路停电识别结果ROC曲线的AUC值不高.因此,提出XGBoost算法下供电线路停电敏感度识别方法.采用缺失补充以及归一化的方法处理供电线路数据时序特征,并分析相应的停电相似度量,采用XGBoost集成机器学习算法学习分析该相似度量的敏感度特征,结合贡献度分析特征属性值以识别出供电线路的停电敏感度.实验结果表明:应用供电线路停电敏感度识别方法后,得出的识别结果表现出的ROC曲线AUC值较高,识别准确度较高,满足了供电线路运维工作中对停电敏感度的信息需求.
Sensitivity Identification of Power Supply Line Outages Under XGBoost Algorithm
Due to ignoring the temporal characteristics of the data,the AUC value of the ROC curve for identifying power outa-ges in the power supply line remains suboptimal.Therefore,a sensitivity identification of power supply line outages based-on XGBoost algorithm is proposed.The missing supplementation and normalization methods are used to process the temporal characteristics of power supply line data and analyze the corresponding power outage similarity measure.What's more,the XG-Boost algorithm integrated machine learning algorithm is used to learn and analyze the sensitivity characteristics of this similari-ty measure and combine the contribution analysis feature attribute values to identify the power outage sensitivity of the power supply line.The experimental results show that the recognition results obtained after the application of the proposed method ex-hibit a better ROC curve AUC value and recognition accuracy,it be able to meet the information requirements for power outage sensitivity in power supply line operation and maintenance work.

power supply linespower outage sensitivityXGBoost algorithmsensitivity identificationROC curvediscreti-zation processingdata temporal characteristicsintegrated machine learning algorithms

卢海明、辜小琢、陈晓瑜、李文珊、王滢桦、方立勤、王思烨

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广东电网有限责任公司汕头供电局,广东 汕头 515041

供电线路 停电敏感度 XGBoost算法 敏感度识别 ROC曲线 离散化处理 数据时序特征 集成机器学习算法

2024

西南师范大学学报(自然科学版)
西南大学

西南师范大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.805
ISSN：1000-5471
年,卷(期)：2024.(4)