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针对Petri网在分析复杂电力系统时的容错性差且难以适应网络拓扑变化的问题,提出一种贝叶斯Petri网模型(BPN),并基于该模型提出一种电网故障诊断方法.该方法通过电力系统网络拓扑分析确定停电区域,随后按照故障蔓延方向对停电区域内的元件分别建立BPN模型,应用Petri网推理和贝叶斯概率计算确定故障元件,最后采用均值方法对各方向上的分析结果进行融合.诊断分析表明,该方法在信息不完备的情况下具有较好的容错性,并且在网络拓扑结构发生变化后仍具有较好的适应性.由于BPN推理时根据基于统计的先验概率求取元件故障的发生概率,避免了直接对计算参数进行设定的主观性.
Aiming at the problem that Petri nets are poor in fault tolerance and difficult to adapt to the change of network topology in the analysis of complex power systems, a Bayesian Petri Net (BPN) model is proposed and a fault diagnosis method based on this model is proposed. Then, according to the direction of fault propagation, establish the BPN model for the components in the power outage region, and use Petri nets inference and Bayesian probability to determine the faulty components. Finally, the average method is used to analyze the results in all directions The results of diagnosis show that the proposed method has good fault tolerance in the case of incomplete information and still has good adaptability after the network topology changes.Because BPN reasoning based on statistical prior probabilities Take the probability of component failure, to avoid the subjective calculation parameters set directly.