Open AccessResearch on Traffic Classification Technology of Electric Power Communication Network Based on Hidden Markov Model
Author(s) -
Nanfang Li,
Zongrong Li,
Lei Zhao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/5/052084
Subject(s) - hidden markov model , reliability (semiconductor) , computer science , telecommunications network , markov chain , markov model , power grid , key (lock) , data mining , power (physics) , stability (learning theory) , reliability engineering , distributed computing , computer network , engineering , artificial intelligence , machine learning , computer security , physics , quantum mechanics
With the penetration of information and communication technology into power production and operation, the dependence of power grid on power communication network becomes stronger and stronger, and the reliability and transmission performance of communication network become the key factors affecting the safe and stable operation of power grid. Based on the analysis and comparison of the existing network traffic models, a hidden Markov model with both long-correlation and short-correlation characteristics is selected to fit and predict the traffic characteristics of the power communication network itself. The article focuses on the development and changes of the production and management of power grid companies, and analyzes the company’s business structure and the bearing relationship of various types of power communication networks. The traffic classification technology of power communication network based on hidden Markov model proposed in this paper has higher accuracy and better stability, and the scores of each evaluation index are higher.