基于改进TOPSIS算法的电力通信网络路由优化

Optimization of power communication network routing based on improved TOPSIS algorithm

摘要

为了解决通信网络业务分布不均带来的电网运行风险问题,针对通信网络路由优化提出了基于相对熵的TOPSIS改进算法.应用该算法对建立的通信网络业务风险度和网络风险度评估模型进行业务重要度计算,并利用实际网络拓扑模型对算法有效性进行验证.结果表明,在针对单个业务或者整体网络时,TOPSIS改进算法较负载均衡算法和风险平衡算法能更有效地减少路径分配时的风险值,降低业务风险度.所提的TOPSIS改进算法有效改善了网络均衡度,进而解决了因业务集中带来的网络中断风险问题.

Abstract

In order to solve the problem of power grid operation risk caused by the uneven distribution of communication network services, an improved TOPSIS algorithm based on relative entropy was proposed for communication network routing optimization. This algorithm is applied to calculate the service importance degree of the established communication network service risk degree and network risk degree evaluation model, and the effectiveness of the algorithm is verified by using the actual network topology model. The results show that the improved TOPSIS algorithm can reduce the risk value of route allocation more effectively than load balancing algorithm and risk balancing algorithm for a single service or the whole network. The proposed TOPSIS algorithm can effectively improve the network balance, and furthermore solve the problem of network interruption risk caused by service concentration.

关键词

相对熵 / TOPSIS算法 / 拓扑模型 / 网络优化

Key words

relative entropy / TOPSIS algorithm / topological model / network optimization

参考文献

[1] SU S, CHAN W L, LI K K, et al. Context information—based cybersecurity defense of protection system[J]. IEEE Transactions on Power Delivery, 2007, 22(3): 1477-1481.

[2] CLEVELAND F. IEC TC57 security standards for the power system's information infrastructure—beyond simple encryption[C]// IEEE PES Transmission and Distribution Conference and Exhibition, May 21—24, 2006, Dallas: IEEE, 2006: 1079-1087.

[3] LIU N, ZHANG J, ZHANG H, et al. Security assessment for communication networks of power control systems using attack graph and MCDM[J]. IEEE Transactions on Power Delivery, 2010, 25(3): 1492-1500.

[4] DING D, HAN Q L, XIANG Y, et al. A survey on security control and attack detection for industrial cyber—physical systems[J]. Neurocomputing, 2018, 275: 1674-1683.