基于贝叶斯网络的配网设备故障仿真研究

Fault simulation research of distribution network equipment based on Bayesian network

电力系统中各配网设备的协调配合是电网安全稳定运行的重要保障,作为配网中重要的能量交换设备,变压器的运行状况对电网具有重要影响,因此需要对其故障进行诊断。针对其故障的复杂化和多样化,采用贝叶斯网络对其诊断模型进行构建,并对变压器运行过程中出现的故障进行识别和诊断。采集变压器历史故障数据,采用Matlab仿真软件对数据进行训练并对其结果进行验证,通过与贝叶斯网络模型的诊断结果进行对比,验证了构建的故障诊断模型能够有效克服信息缺失造成的故障诊断误差,有效提升故障诊断准确性,具有一定的实际应用价值。

As an important energy exchange equipment in distribution network,the operation condition of trans⁃former has an important influence on the power network,therefore,its fault needs to be diagnosed.In view of the complexity and diversity of its faults,a Bayesian network diagnosis model was used to identify and diagnose the faults occurring during the operation of the transformer.The historical fault data of the transformer was collected,and the data was trained by Matlab simulation software and the results were verified.By comparing the diagnosis re⁃sults with the Bayesian network model,it was verified that the fault diagnosis model could effectively overcome the fault diagnosis error caused by lack of information and improve the accuracy of fault diagnosis,having a certain practical application value.