基于小波分析的农业用户异常用电识别与判断  

Identification and Judgment of Abnormal Power Consumption of Agricultural Users Based on Wavelet Analysis

作　　者：刘人境[1] 齐国华 LIU Ren-jing;QI Guo-hua(School of Management,Xi'an Jiaotong University,Xi'an 710049,China)

机构地区：[1]西安交通大学管理学院,陕西西西安710049

出　　处：《数学的实践与认识》2023年第1期65-75,共11页Mathematics in Practice and Theory

基　　金：国家社科基金资助项目(15BGL082);国家社科重大基金资助项目(18ZDA104)

摘　　要：提高电能利用效率,合理分配稀缺资源是电网公司能源监控建设的重要内容.通过国家电网青海分公司在2020年2月到2021年5月对某农业灌溉重点监测用户用电量情况进行监测,提出采用小波分析方法对监测数据进行去噪和对奇异值的挖掘.通过小波分析,全面而细致的量化了数据在不同频域下的波动情况,在较大程度上保留数据的原始信息并且对奇异数据具有较高的识别效果.计算结果表明:运用该方法可以有效挖掘出正常数据的波动情况,并且得出的奇异数据主要分布时间段,与实际情况相吻合。Improving the efficiency of electric energy utilization,rationally allocating scarce resources are important contents of energy monitoring and construction of power grid companies.From February 2020 to May 2021,this paper monitors a key agricultural monitoring user and electricity situation through the State Grid Qinghai Branch,and proposes the use of wavelet analysis to denoise the monitoring data and mine the singular values.Through wavelet analysis,the fluctuation of data in different frequency domains is comprehensively and meticulously quantified,and the original information of the data is retained to a large extent and has a high recognition effect on the singular data.The calculation results show that the method can effectively mine the fluctuation of normal data,and the main distribution time of the singular data is consistent with the actual situation.

关 键 词：监测 小波分析 异常值 

分 类 号：TM73[电气工程—电力系统及自动化]