改进贝叶斯网络在大面积停电事件应急情景动态推演模型中的应用

为了有效界定停电事件的影响因素，提高应急情景动态推演模型的准确性，避免电力突发事故的发生，设计基于改进贝叶斯网络的大面积停电事件应急情景动态推演模型。首先，对初始情景、引发情景、爆发情景、恢复情景和消失情景等五个网络层级进行分析，构建大面积停电事件情景网络；其次，考虑应急决策主体、对象、目标、方案以及决策环境，模拟大面积停电事件应急决策过程，利用改进贝叶斯网络计算多层次情景网络之间移动概率，确定事件动态演化规律；最后，通过大面积停电事件数据的输入结果，得出包含大面积停电事件动态推演路径和最优应急方案。实验结果表明：应用设计模型后，停电面积缩小率为48.57%，停电事件的实际停电面积明显缩小。完善了应急情景动态推演过程，事件应急方案与实际停电情况相匹配，可以高效应对大面积停电事件，具有较高的应用价值。

Application of improved Bayesian networks in dynamic inference model of emergency scenarios for large area power outages

In order to effectively define the influencing factors of power outage events, improve the accuracy of emergency scenario dynamic deduction models, and avoid the occurrence of power emergencies, a large-scale power outage emergency scenario dynamic deduction model based on improved Bayesian networks is designed.Firstly, five network levels of initial scenario, triggering scenario,outbreak scenario, recovery scenario, and disappearance scenario are analyzed to construct a large-scale power outage scenario network. Then, emergency decision- making subject, object, target,plan, and decision- making environment are considered, the emergency decision-making process of large-scale power outage events is simulated, improved Bayesian networks is used to calculate the probability of movement between multi-level scenario networks, and the dynamic evolution law of events is determined. Finally, based on the input results of large-scale power outage event data, the dynamic inference path and optimal emergency plan including large-scale power outage events are obtained. The experimental results show that after applying the design model, the reduction rate of power outage area is 48.57% , and the actual power outage area of power outage events is significantly reduced. The dynamic deduction process of emergency scenarios has been improved, and the emergency response plan is matched with the actual power outage situation, which can efficiently respond to large-scale power outage events and has high application value.