Power grid fault diagnosis method based on improved Bayesian network model and HHT
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(Information & Telecommunication Company, State Grid Chongqing Electric Power Company, Chongqing 401120, China)

Clc Number:

TM863

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    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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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英文版,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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  • Online: June 06,2025
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