| Autor: |
Geng ZHANG, Ya-nan WANG, Xiang-dong XING, Hong WU, Min ZHU, Yong-li ZHAO |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Guangtongxin yanjiu, Vol 00, Iss 01, Pp 15-18,35 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1005-8788 |
| DOI: |
10.13756/j.gtxyj.2021.01.004&lang=zh |
| Popis: |
Under the background of vigorously developing smart grids, the scale of power optical communication networks supporting power grid operation is becoming larger and larger, and the services carried are more diversified. However, the service routing planning of the power communication network is mainly based on the shortest path algorithm, which leads to the imbalance of the business importance distribution of the power communication networks. Therefore, the local risk of the network can be high and the overall health of the network is low. Aiming at the shortcomings of traditional routing algorithms, this paper proposes a route algorithm that uses deep reinforcement learning technology to balance the risk of network traffic, which also comprehensively considers the traditional constraints such as optical transmission constraints and link residual capacity. The algorithm considers the distribution of service importance, link capacity, and link optical signal-to-noise ratio to achieve risk equalization of power communication networks. We have carried out an experiment in a provincial power communication subnet. The result shows that the method can effectively reduce the risk balance of the power optical communication networks and provide a strong guarantee for the safe operation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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