应用贝叶斯网络的连锁故障模式识别

Cascading Failures Recognition Based on Bayesian Networks

电网连锁故障的演变是一个随机过程,而大部分线路断线故障都可以用断路器的开断进行描述。贝叶斯网络能够灵活描述不确定信息,并能进行不确定性推理。以贝叶斯网络理论构建系统实时网络拓扑并计算故障概率,同时引入能够反映电力系统运行状态的支路静态势能函数来综合构建出风险指标,进行连锁故障模式识别。以IEEE-30母线系统进行算例分析,仿真结果能够搜索出各种连锁故障模式集,并能反映出各个模式的严重程度,验证了该方法的合理性、有效性。

The evolution of power grid cascading failure is a random process and most of the line breaking faults can be described with breaker breaking. Bayesian network can describe the uncertain information flexibly, and conduct uncer- tainty reasoning. Build the real-time network topology using Bayesian network theory and calculate the probability of failure, the cascading failures are recognized by introducing the branch static energy function which reflects the power system state to build a risk index. IEEE-30 bus system was used for analysis. The simulation results can search out a set of serious cascading failures and reflect the severity of each mode, which shows the feasibility and validity of the proposed method.