根据元件故障与保护动作和断路器跳闸之间的内在逻辑关系，建立了面向元件的电网故障诊断模型，并采用误差反向传播的梯度下降法修正网络参数。该模型是一种由Noisy-Or、Noisy-And节点组成的特殊的贝叶斯网络，能够处理电网故障诊断中的不确定性，具有语义精确、推理快速、学习效率高等特点，适用于大规模电力系统的多重复杂故障诊断。实际电网故障案例验证了该方法的正确性和有效性。

This paper presents an application method of Bayesian networks (BN) to the reliability assessment of power systems. Bayesian networks provide a flexible framework to represent probabilistic information and to make inference on it. Uncertainty and dependency of the components' information in a system are easily incorporated in the analysis. The flexibility of the probabilistic nference algorithms in Bayesian networks permit to compute both the system's reliability indices and the mutual affection on reliability indices of all components. However, a BN cannot be constructed easily based on the topology of the relating power system. The paper gives a new method to construct a Bayesian network based on the assessed system's fault tree or its minimal path set. The method is efficient and can compute components failure probabilities on the condition of the system failure. Its advantages are demonstrated through two examples.

介绍了一种利用人工智能的知识表示、搜索和推理技术进行电网自动结线分析的新方法，利用此方法研制的电网自动结线分析器已被成功地应用于若干实际电网的在线和离线分析计算软件中。同传统方法相比，此方法具有算法简便，易于编程实现，可避免传统方法在开关两侧的大量编号，电网拓扑数据便于维护等优点，母线的节点分析采用规则，而不是传统的复杂的搜索方法。