Computer and Modernization ›› 2024, Vol. 0 ›› Issue (10): 61-64.doi: 10.3969/j.issn.1006-2475.2024.10.010

Previous Articles     Next Articles

Power Information Data Fusion Model Based on Improved Extreme Learning Algorithm

  

  1. (1. Hangzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China;
    2. National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China)
  • Online:2024-10-29 Published:2024-10-30

Abstract:  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.

Key words: power communication data, ELM algorithm, data fusion, LATC algorithm, attention mechanism

CLC Number: