Research on Intelligent Management and Control Method of Power Information Communication OTN Network Based on OSU Technology
The conventional power information communication OTN network intelligent management and control mechanism is generally fixed, and the scope of management and control is limited, resulting in an increase in the ultimate packet loss rate. Therefore, the design and research of the power information communication OTN network intelligent management and control method based on OSU technology are proposed. According to the current measurement, the real-time data acquisition and pre-processing of power communication shall be carried out first, and the multi-level mode shall be adopted to break the limits of control range, and the multi-level intelligent control mechanism shall be set. Based on this, the OSU power information communication OTN network intelligent control model shall be constructed, and the adaptive dispatching processing method shall be adopted to achieve intelligent control processing. The test results show that compared with the control method of artificial intelligence grid voltage modulation strategy and the parallel calculation method of multi-source data grid primary frequency modulation capability, the intelligent control method of OSU electric power information communication OTN network designed by this design has a relatively low packet loss rate, which indicates that with the help of OSU technology, this method has high stability and reliability, and OTN control effect of the network has been further improved, and the preset control goals and tasks have been verified in practice.
Authors
- Shengqi Qian
- Yuxiao Sun (ORCID: https://orcid.org/0009-0003-6817-7865)
- Haichuan Niu (ORCID: https://orcid.org/0009-0005-8234-5938)
- Yanyuan Zhu (ORCID: https://orcid.org/0000-0002-1617-7463)
- Yu Wu (ORCID: https://orcid.org/0000-0003-2120-497X)
Institutions
- State Grid Corporation of China (China) (CN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/iaecst64597.2024.11117906
- Primary Topic
- E-commerce and Technology Innovations
- Type
- article
- Field-Weighted Citation Impact
- 0.00