基于改进极限学习算法的电力信息数据融合模型  

Power Information Data Fusion Model Based on Improved Extreme Learning Algorithm

作　　者：杜猛俊 李昂 童俊 钱锦 康恺 王若丁 靳文星 DU Mengjun;LI Ang;TONG Jun;QIAN Jin;KANG Kai;WANG Ruoding;JIN Wenxing(Hangzhou Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000,China;National Key Laboratory of Electromagnetic Energy,Naval University of Engineering,Wuhan 430033,China)

机构地区：[1]国网浙江省电力有限公司杭州供电公司,浙江杭州310000 [2]海军工程大学电磁能技术全国重点实验室,湖北武汉430033

出　　处：《计算机与现代化》2024年第10期61-64,86,共5页Computer and Modernization

基　　金：国家自然科学基金青年基金资助项目(52007196)。

摘　　要：针对电力通信网络中电力流和信息流交互能力差,导致电力信息数据效用低,无法满足实际新型电力系统需求的问题,提出一种基于通信数据融合方法的改进极限学习算法来提高新型电力系统中电力信息数据采集和高能效的数据处理性能。首先,采用低秩自回归张量补全(Low-rank Autoregressive Tensor Completion,LATC)算法来整合电力流、信息流传递回终端的多源异构数据,并消减缺失数据影响;进一步采用极限学习机(Extreme Learning Machine,ELM)算法将数据间的联系构建为数据特征,并输出特征集完成数据特征级融合。随后,为了提高融合任务中的融合准确率,在极限学习机中加入注意力机制作为底层基础架构。最后,实验结果表明了该方法的有效性。In view of the poor interaction ability of power flow and information flow in power communication network,which leads to the low utility of power information data and can not meet the needs of the actual new power system,this study proposes an improved extreme learning algorithm based on communication data fusion method to improve the performance of power information data acquisition and energy-efficient data processing in the new-type power system.Firstly,the low rank autoregressive tensor completion(LATC)algorithm is used to integrate the multi-source heterogeneous data transmitted by power flow and information flow back to the terminal,and reduce the impact of missing data.Further,the extreme learning machine(ELM)algorithm is used to construct the relationship between the data as data features,and the feature set is output to complete the data feature level fusion.Then,in order to improve the fusion accuracy in the fusion task,attention mechanism is added to the extreme learning machine as the underlying infrastructure.Finally,the experimental results show the effectiveness of the method.

关 键 词：电力通信数据 ELM算法 数据融合 LATC算法 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]