基于改进型贝叶斯网络模型和HHT的电网故障诊断方法研究
作者:
作者单位:

(国网重庆市电力公司信息通信分公司,重庆 401120)

通讯作者:

伍冲翀,(1991—),男,硕士研究生,中级工程师,主要从事电力信息安全等方面的研究;E?mial:22214126@163.com

中图分类号:

TM863

基金项目:

国网重庆信通公司数据质量监测中心信息系统项目(SGCQXT0JSXX2200064)


Power grid fault diagnosis method based on improved Bayesian network model and HHT
Author:
Affiliation:

(Information & Telecommunication Company, State Grid Chongqing Electric Power Company, Chongqing 401120, China)

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    摘要:

    电网安全稳定运行是其进行可靠输电、变电、配电的前提。当电网发生故障时,在故障区域进行快速、准确的定位对缩短故障时间十分重要。先从电网相关监测系统获取元件开关量和电气量信息,并根据故障区域形成相关开关量信息的初始决策表,提取电气量信息的有效信号;再采用粗糙集理论、贝叶斯网络、希尔伯特?黄变换(Hilbert?Huang transform,HHT)等理论,计算元件故障度和畸变度;然后,利用改进的D?S证据理论,对元件开关量的故障度与电气量的畸变度进行融合;最后,以某区域电网的局部拓扑为实例,对该改进型贝叶斯网络模型进行了仿真测试,该实例仿真结果表明该模型可提升算法诊断速度。并以IEEE 39节点为例进行了仿真,该仿真结果表明,开关量的引入可提升故障诊断精度,且融合数据降低了评估模型内的不确定程度。

    Abstract:

    The safety and stable operation of power grid is the prerequisite for reliable transmission, transformation, and distribution. Therefore, when the power grid fails, it is very important to locate the fault quickly and accurately and shorten the fault time. Firstly, the information of component switching value and electrical quantity is obtained from the relevant monitoring system of the power grid. The initial decision table of relevant switching value information is formed according to the fault area, and the effective signal of electrical quantity information is extracted. Then, the rough set theory, Bayesian network, Hilbert-Huang transform (HHT), and other theories are used to calculate the component fault degree and distortion degree. Subsequently, the improved D-S evidence theory is employed to fuse the fault degree of component switching value with the distortion degree of electrical quantity. Finally, the local topology of a regional power grid is used to test the improved Bayesian network model. The simulation results show that the model can improve the diagnosis speed. The IEEE 39 node is used as an example, and it is verified that the introduction of switching value can improve the diagnostic accuracy, and data fusion reduces the uncertainty in the evaluation model.

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引用本文

伍冲翀,王健,龚黎慧倩.基于改进型贝叶斯网络模型和HHT的电网故障诊断方法研究[J].电力科学与技术学报,2025,40(2):42-49.
WU Chongchong, WANG Jian, GONG Lihuiqian. Power grid fault diagnosis method based on improved Bayesian network model and HHT[J]. Journal of Electric Power Science and Technology,2025,40(2):42-49.

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  • 在线发布日期: 2025-06-06
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