基于异常负荷回归学习算法的电力物联网安全强化验证建模分析  

Modeling and Analysis of Power IoT Security Strengthening Verification Based on Abnormal Load Regression Learning Algorithm

作　　者：姚海燕 向新宇 於志渊 杜忠 屠永伟 YAO Haiyan;XIANG Xinyu;YU Zhiyuan;DU Zhong;TU Yongwei(Hangzhou Yuhang Electric Power Design Institute Co.,Ltd.,Hangzhou 310000,China;State Grid Hangzhou Power Supply Company,Hangzhou 310000,China;Beijing Broadthinking Technology Co.,Ltd.,Beijing 100000,China)

机构地区：[1]杭州市电力设计院有限公司余杭分公司,浙江杭州310000 [2]国网浙江省电力有限公司杭州供电公司,浙江杭州310000 [3]北京博思汇众科技有限公司,北京100000

出　　处：《微型电脑应用》2023年第2期176-178,共3页Microcomputer Applications

摘　　要：传统电力物联网安全强化验证方法误差较大,因此,提出基于异常负荷回归学习算法的电力物联网安全强化验证建模分析方法。优化异常负荷的回归学习算法,求解回归学习算法函数。确定参数后建立异常负荷信道检测模型,通过模型辨识和修正异常负荷,完成电力物联网安全强化验证建模分析。实验结果表明,所提方法的平均相对误差与负荷预测精度指标优于传统方法。The traditional security enhancement verification method of power IoT has large error. Therefore, the modeling and analysis method of power IoT security enhancement verification based on abnormal load regression learning algorithm is proposed. We optimize the regression learning algorithm of abnormal load, and solve the regression learning algorithm function. After the parameters are determined, the abnormal load channel detection model is established. Through the model identification and correction of abnormal load, the security enhancement verification modeling analysis of power IoT is completed. Experimental results show that the average relative error and load forecasting accuracy of the proposed method are better than those of traditional methods.

关 键 词：异常负荷 回归学习算法 电力物联网 安全强化验证 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]