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。

针对目前我国相当一部分地区电网的管理未摆脱手工计算的现状，研制了基于图形界面的电网计算分析工具软件。可视化、部分计算过程的自动化成为此软件的重要特色，并实现了智能程序与算法程序的结合。该软件已在保定和太原地区电网得到了较长时间的应用。

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.

The conventional graph editing of one-line substation diagrams for a graphical user interface is time-consuming and needs considerable effort. A method for automatic generation of the substation diagrams is described in this paper. In this method, artificial intelligence (AI) frames are adopted for the representation of substations and their network, some expert rules on the layout and drawing of substation buses are accumulated and summarized, and AI rule-based inference techniques are used for branch analysis on different types of buses and drawing of the buses and their branches. A vertical coordinates conversion relationship is obtained to narrow the testing of substation diagram components’ vertical layout. The method has been programmed and applied to two power system analysis programs, one of which is undergoing field test in a transmission system for real time fault diagnosis in northern China.