With the development of new power system, the scale of Electric Power Optical Communication Network (EPOCN) is growing, and the instability of the network has increased. Therefore, accurate network fault sensing has become the key to maintain the safe and reliable operation of EPOCN. This paper proposes a fault sensing method for EPOCN based on digital twin. First, we propose a digital twin based EPOCN architecture. On this basis, a fault sensing scoring method of EPOCN based on Digital Twin-empowered Fuzzy Analytic Hierarchy Process (DT-FAHP) is proposed, and the operation status of EPOCN is evaluated by scoring. A digital twin model is established by uploading the reliable operation data to the electric power optical communication digital twin network, and the fault warning of EPOCN is realized with the help of the digital twin model. Finally, an example is given to verify the superiority of the proposed method in the processing of fault sensing indicators.
The explosive growth of data volume and dimension poses a huge challenge on service carrying capacity of power communication network with limited spectrum resources. The combination of software defined network (SDN) and elastic optical network (EON) in power communication network, i.e., EON-assisted power communication network, provides a feasible solution. To alleviate the waste of spectrum caused by non-uniform service orchestration in EON-assisted power communication network under incomplete information, this paper proposes a machine learning-based intelligent service orchestration algorithm for EON-assisted power communication network. The proposed algorithm dynamically learns spectrum allocation based on local historical information to minimize network overhead. Simulation results demonstrate that the proposed algorithm has superior performances in network overhead and selection probability of optimal arm.
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