高级计量体系是新型电力系统建设的重要组成, 然而高级量测系统依赖于网络信息基础设备, 存在较大的安全问题. 本文基于隐马尔可夫模型并结合人工免疫算法对高级量测系统的网络安全态势进行了评估. 首先, 采用计数器算法来获取电力网络中的安全观测数据; 其次, 通过隐马尔可夫模型描述网络安全状态的变化过程, 并利用人工免疫算法计算不同状态之间的转移概率矩阵; 接着, 利用态势评估误差对状态转移矩阵进行修正; 最后, 通过计算得到不同时刻处于不同安全状态的概率, 再结合风险损失向量最终得到安全态势评估值. 实验表明, 该方法具有较好的评估效果, 能够较为准确地捕获到系统中存在的安全缺陷, 从而保证高级量测系统的安全运行; 准确地发现系统中的相关安全缺陷, 从而保证新型电网环境安全、平稳和可靠地运转.

Advanced metering infrastructure is an important component in the construction of new power systems; however, advanced measurement systems rely on network information infrastructures and present major security issues. This study evaluates the network security posture of advanced metering systems by combining a hidden Markov model with an artificial immunity algorithm. First, a counting algorithm is used to obtain the security observation data in the power network. Subsequently, the Markov model is used to describe the change in the network security state, whereby the artificial immunity algorithm calculates the transfer probability matrix between different states. The state transfer matrix is then modified based on the state assessment error. Upon calculating the probability of being at different security states at different times and combining it with the risk loss vector, the final value of safety posture is obtained. The experiments demonstrate that the method proposed in this study has a good assessment effect and can capture the safety defects in the system more accurately, ensuring the safe operation of the advanced measurement system by accurately identifying the relevant safety defects in the system for a safe, smooth, and reliable operation of the new grid environment.