摘要
电力系统具有一定脆弱性,可能由于不确定因素影响而产生异常,挖掘其中的异常用电数据具有重大意义.电力负荷异常是电力系统中主要的异常之一.提出了基于小波检测电力负荷异常的方法,利用ARFIMA统计方法结合小波,能够快速准确全面地发现电力负荷异常数据,方便有关部门缩小排查范围电力负荷数据异常检测和处理.同其它异常检测方法的实验对比证明,方法具有较高的实用价值,可以推广使用,对电力产业发展有推动作用.
Power system has certain vulnerability,which may cause abnormalities due to uncertain factors.It is of great significance to mine the abnormal electricity consumption data.Abnormal load is one of the main abnormalities in power system.In this paper,a method based on wavelet to detect abnormal load is proposed.Using ARFIMA statistical method and wavelet transform,the abnormal data of power load can be found quickly,accurately and comprehensively,which facilitates the detection and processing of abnormal data of power load by relevant power departments.Compared with other anomaly detection methods,this method has high practical value and can be popularized and used to promote the development of power industry.
作者
张春辉
白翠芝
张蔓娴
ZHANG Chun-hui;BAI Cui-zhi;ZHANG Man-xian(Bureau of Yuxi Power Supply,Yunnan Power Grid Limited Liability Company,Yuxi 653100,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第S02期49-54,共6页
Journal of Yunnan University(Natural Sciences Edition)
作者简介
张春辉(1983-),男,吉林人,高级工程师,主要研究电力大数据与信息化.E-mail:47847599@qq.com.
