摘要
在智能电网飞速发展的趋势下,新型数字基础设施建设成为电力企业的核心业务之一,电力企业数据的治理和智能化分析为平台运营、数据增值变现等商业模式创新提供了条件.在电力数字化和智能化治理背景下,使用鲁棒性随机分割森林算法实现变压器损耗数据的异常值智能化检测.通过鲁棒性随机分割森林算法划分样本点以构建鲁棒性随机分割森林结构模型,通过插入和删除样本点对结构复杂度的影响程度给定该样本点的异常值评分.鲁棒性随机分割森林算法适用于实时损耗数据异常检测,兼顾异常值检测效果和运行效率,具有较高的可信度.对真实变压器损耗数据集中进行异常值检测试验,实验结果表明该算法高效、灵活,相较于其他方法,精确率、召回率及运行效率均有显著提升.
With the rapid development of smart grids,the construction of new digital infrastructure has become one of the core businesses of power companies.Power companies’governance and intelligent analytical capabilities enable opportunities for business model innovation,such as platform operation and value-added data realization.In the context of power digitization and intelligent governance,we use the robust random cut forest in this paper for transformer loss data anomaly intelligence detection.The algorithm divides sample points by random cutting to construct a random cut forest structure model by inserting and removing sample points in the structure;the anomaly score of a sample point is then given by the influence of complexity.This method is suitable for anomaly detection on real-time loss data and offers a high degree of credibility,effectiveness,and efficiency.An experiment of anomaly detection on real transformer loss data shows that the method is efficient and flexible.The accuracy,recall,and efficiency of the proposed method,moreover,is substantially better than alternatives.
作者
张国芳
温丽丽
吴蒙
刘通宇
郑宽昀
黄福兴
袁培森
ZHANG Guofang;WEN Lili;WU Meng;LIU Tongyu;ZHENG Kuanyun;HUANG Fuxing;YUAN Peisen(State Grid Sichuan Electric Power Company,Chengdu 610094,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210095,China;Nanjing Automatic Research Insititute Group Corporation(State Grid Electric Power Research Institute),Nanjing 211106,China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第6期135-146,共12页
Journal of East China Normal University(Natural Science)
