This paper describes a new power network topology processor which has been successfully used in two practical power system application programs. Artificial intelligence (AI) techniques have been used and this provides several advantages. AI's frame representation method is used to represent electric network configuration. This avoids manual numbering on a large amount of circuit breaker terminals/circuits. Rule-based method has been adopted for substations' bus configuration analysis instead of the search method. This makes the analysis simple and adaptable to new types of bus configurations just by adding corresponding rules. AI's search techniques have been applied for network graph search making the search algorithm simpler and easier to be programmed. Some results from the application of the proposed topology processor are also included in the paper.

The framework of an assessment system on transformers' condition is proposed in this paper through mainly using data mining techniques. Moreover, a warehouse is used to collect transformers'testing data, and a multi-agent system is used to design the framework of the software. The present framework is open and flexible, so the objective system is easy to be developed and maintained. The system can support transformers' condition-based maintenance to reduce electric utility's cost. The condition of a transformer depends on its design, present and historical data relating to its installation environment, load amounts, being switched number and so on. Usually the off-line testing results, operational data, fault records and weather conditions have been stored in different systems, so finding an effective method to utilize all this information for condition assessment is difficult. Therefore, a data warehouse has been used to integrate all of the above data, and some data mining techniques have been used to find the pattern and trend of the condition of a transformer. Then whether it is healthy can be determined. In order to make the system open and flexible, Open Agent Architecture (OAA) is employed to compose the multiagent system. Seven application agents are designed to evaluate transformers' conditions synthetically. The Grey correlation method, grey theory prediction model GM (1,1), Bayesian network classifier and Bayesian network are employed in the agents.

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.

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.