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基于风力预测的输电线路风偏放电预警

Wind Swing Discharge Warning for Transmission Lines Based on Wind Forecast
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摘要 为提高对强对流环境下输电线路风险的预警精度,研究了输电线路风偏放电预警方法。首先,基于气象雷达的历史监测数据,构建双层串联SVM分类器,生成强对流风力预测模型。其次,构建输电线路风偏几何模型,分析非水平风对输电线路的影响,推导临界状态下风偏角和风偏风速的计算公式。最后,采用广义极值分布函数计算输电线路风偏放电概率,发布风偏放电风险预警等级。结果表明,基于气象雷达数据和强对流风力预测的输电线路风偏放电预警方法有效、可靠,可为强对流天气下的输电线路维护、电网运行、防灾管理提供决策参考。 To improve the accuracy of early warning for transmission lines in strong convective environments,a wind swing discharge warning method for transmission lines was studied.Based on historical monitoring data from meteorological radar,a double-layer series SVM classifier was constructed to generate a strong convective wind force prediction model.Furthermore,a geometric model of wind swing for transmission lines was constructed,and the impact of non horizontal wind on transmission lines was analyzed.The calculation formulas for wind deviation angle and wind speed under critical conditions were derived.Finally,the generalized extreme value distribution function was used to calculate the probability of wind induced discharge on transmission lines,and a early warning level for wind induced discharge was issued.The results indicated that the wind swing discharge warning method for transmission lines based on meteorological radar data and strong convective wind prediction is effective and reliable.This study provides decision-making references for transmission line maintenance,power grid operation,and disaster prevention management under strong convective weather.
作者 陈科技 康丽莉 张琳琳 陈赛慧 CHEN Keji;KANG Lili;ZHANG Linlin;CHEN Saihui(Electric Power Economics and Technology Research Institute,State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310014,China;不详)
出处 《武汉理工大学学报(信息与管理工程版)》 CAS 2024年第4期677-682,共6页 Journal of Wuhan University of Technology:Information & Management Engineering
基金 国网浙江省电力有限公司科技项目(B311JY21000M).
关键词 风偏放电预警 强对流风力预测模型 气象雷达数据 输电线路 双层串联SVM分类器 early warning of wind swing discharge strong convective wind forecast model meteorological radar data transmission line double-layer series SVM classifier
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