电力光通信网中风险均衡路由算法研究

Research on Risk Balancing Routing Algorithm in Power Optical Communication Network

在大力发展智能电网的背景下,支撑电网运转的电力光通信网规模日趋庞大,其承载的业务更加多样化。然而电力光通信网的业务路由规划主要以最短路径算法为主,导致电力光通信网存在业务重要度分布不均衡,从而导致网络局部风险过高的问题。针对上述现状,文章采用深度强化学习技术,以网络业务风险均衡为目标,提出了基于强化学习的电力光通信网风险均衡路由算法。该算法考虑业务重要度分布情况、链路容量和链路光信噪比,实现了电力光通信网风险均衡化。文章选取某省电力通信子网验证方案的有效性,研究结果表明,该方法能够有效地降低电力光通信网风险均衡度,为电网的安全运行提供有力保障。

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.