Power Information Data Fusion Model Based on Improved Extreme Learning Algorithm
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 infor-mation 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 infor-mation flow back to the terminal,and reduce the impact of missing data.Further,the extreme learning machine(ELM)algo-rithm is used to construct the relationship between the data as data features,and the feature set is output to complete the data fea-ture 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.
power communication dataELM algorithmdata fusionLATC algorithmattention mechanism
万方数据
维普
