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基于离群点检测的低压台区同期线损异常辨识研究 被引量:8

Research on identification of synchronous line loss anomaly in low⁃pressure platform based on outlier detection
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摘要 为缩短电压参量的实际传输距离,加强对同期线损量异常行为的抵抗能力,提出基于离群点检测的低压台区同期线损异常辨识方法。通过定义离群点的方式,确定线损参量的实际分类条件,根据最大负荷损失数值实现基于离群点检测的低压台区同期线损计算。在此基础上,采集大量的线损数据信息,利用核函数实现对辨识参数的优化与应用,完成基于离群点检测低压台区同期线损异常辨识方法的设计。实验对比结果表明,与传统SVDD型辨识测量手段相比,应用基于离群点检测的异常辨识方法后,实时电压参量差极值仅能达到35 V,定向电子传输距离也缩短至3.8μm,可提升对低压台区同期线损异常行为的抵抗能力。 In order to shorten the actual transmission distance of voltage parameter and strengthen the resistance to the abnormal behavior of synchronous line loss,a method for identifying the abnormal behavior of synchronous line loss in low⁃voltage stage based on outlier detection is proposed.The method of defining outliers is used to determine the actual classification conditions of line loss parameters.According to the maximum load loss value,the calculation of line loss over the same period in the low⁃pressure stage based on outlier detection is realized.On this basis,a large amount of line loss data information is collected,and the optimization and application of identification parameters are realized by using the kernel function,and the design of the identification method of line loss anomaly in low⁃voltage platform based on outliers is completed.The experimental comparison results show that compared with the traditional SVDD identification method,the outlier detection based anomaly identification method can only achieve 35 V difference in real⁃time voltage parameters,and the directional electron transmission distance is reduced to 3.8μm,which can improve the resistance to the abnormal behavior of line loss in the low⁃voltage stage during the same period.
作者 周丽霞 谭志强 郑思达 张勋 戚成飞 ZHOU Lixia;TAN Zhiqiang;ZHENG Sida;ZHANG Xun;QI Chengfei(Marketing Service Center(Measurement Center),State Grid Hebei Electric Power Co.,Ltd.,Beijing 100032,China)
出处 《电子设计工程》 2022年第3期75-78,83,共5页 Electronic Design Engineering
基金 国网冀北客服中心项目(63018519001D)。
关键词 离群点检测 低压台区 同期线损 异常辨识 负荷损失量 线损数据 outlier detection low⁃pressure platform synchronous line loss anomaly identification load loss line loss data
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