基于IWOA-ELM-AE的电力资产信息管理系统异常数据检测方法  

Abnormal data detection method based on IWOA-ELM-AE for power asset information management system

作　　者：李凯 靳书栋 刘宏志 王艳梅 杨晓营 LI Kai;JIN Shudong;LIU Hongzhi;WANG Yanmei;YANG Xiaoying(Economic&Technology Research Institute,State Grid Shandong Electric Power Company,Jinan 250022,Shandong,China)

机构地区：[1]山东省电力公司经济技术研究院,山东济南250022

出　　处：《沈阳工业大学学报》2024年第3期255-262,共8页Journal of Shenyang University of Technology

基　　金：山东省科技计划项目(S2021RCDT2B0826)。

摘　　要：针对当前电力资产信息管理系统难以准确自主发现异常数据的问题,提出了一种基于IWOA-ELM-AE的电力资产信息管理系统异常数据检测方法。在管理系统框架下分析了可能存在的异常类型,将改进鲸鱼优化算法(IWOA)用于优化极限学习机自编码器(ELM-AE),建立了电力信息系统异常数据优化检测模型。将模型应用于电力资产信息异常数据检测,并建立性能评估指标体系以衡量其效果。结果表明:所提方法的检测性能评估结果与传统模型相比具有显著优势,能够更为准确地检测电力资产信息中存在的异常数据。Aiming at the problem that the current power asset information management system is difficult to detect abnormal data accurately and independently,a method based on IWOA-ELM-AE for detecting abnormal data in the power asset information management system was proposed.The analysis for possible anomaly types under the framework of the management system was performed,the improved whale optimization algorithm(IWOA) was used to optimize the ELM-AE,and the corresponding abnormal data optimization detection model for power information system was established.The as-proposed model was applied to the detection of abnormal data of power asset information,and the performance evaluation index system was established to measure its effect.The results show that the test performance evaluation results of as-proposed method has remarkable advantages over the traditional model,and can detect the abnormal data in the power asset information more accurately.

关 键 词：信息管理系统 电力资产 异常数据检测 极限学习机 自编码器 鲸鱼优化算法 检测性能 评估指标 

分 类 号：TM715[电气工程—电力系统及自动化]