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。

using an error back propagation algorithm similar to the BP algorithm for artificial neural networks. The fault diagnosis models don’t vary with the change of the network structure, so they can be applied to any transmission power system. Furthermore, they have clear semantics, rapid reasoning, powerful error tolerance ability and no convergence problem during the diagnosing procedure. Experimental tests show that the approach is feasible and efficient, so the prototype program based on the approach is promising to be used in a large transmission power system for on-line fault diagnosis.

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.