In order to improve the efficiency and accuracy of fault diagnosis of power supply and distribution system in intelligent buildings, a fault diagnosis method based on Bayesian network and wavelet transform was proposed. Firstly, the topological structure of power supply and distribution network in intelligent buildings was analyzed in detail in theory. Secondly, the switching and electrical quantities in fault information were filtered and reorganized by wavelet transform. Finally, the fault information after the reorganization was modeled and analyzed by Bayesian network, and the fault diagnosis results were obtained. In this paper, the process of extracting electrical and switching quantities from fault information was introduced in detail. According to the fault characteristics of the existing intelligent building power supply and distribution system, the corresponding recovery strategy was given. IEEE⁃39 multi⁃node complex power fault system is taken as an example, the simulation results show fault diagnosis result of the proposed method is fast and accurate. The research results have important reference value for fault diagnosis research of intelligent building power supply and distribution network.

A single terminal fault location method for overhead transmission lines in general interconnected power system with n⁃bus was proposed. High accuracy in fault location was achieved by using both an accurate distributed parameters model for the faulted transmission line, and a two⁃bus Thevenin equivalent network model for the power system that accurately accounted for its inter connectivity. The method was tested by using transient fault data obtained from MATLAB simulations of an 11⁃bus interconnected power system. The results show that the method is capable of estimating the fault distance with high accuracy for various fault conditions. And it is sensitive to errors in the value of the local bus impedance, but insensitive to errors in the value of the remote bus impedance.

      Power grid is diagnosed after a failure to prevent the fault occurred by inferring the information that the fault generated.The method of model prediction (MP) and abductive reasoning network(ARN) is proposed to forecast the power system fault. MP predicted the troublefree operation data of the power grid by using historical data, and compared with the actual grid runtime data, the difference was calculated and used as the input of fault diagnosis system. ARN was used to bulid the fault diagnosis system and solve the complicated relationships between data processing and the corresponding candidate fault section. The fault location can be found before protection device and circuit breaker by combining the method of MP and ARN. The test results showed that the model prediction method can quickly and accurately diagnose the fault compared with BP neural network method.