Transmission line maintenance is crucial for the stable operation of power systems, as any faults can lead to severe consequences. With the increasing complexity of power systems, traditional fault diagnosis methods are no longer applicable. Based on this, the article explores the possibility of using machine learning techniques for transmission line fault diagnosis. By analyzing historical fault data, machine learning models can identify complex patterns and correlations to achieve fault prediction and diagnosis. Firstly, different types of short-circuit faults are analyzed, including three-phase short circuits, two-phase short circuits, two-phase ground short circuits, and single-phase ground short circuits, and a classification algorithm based on Linear Discriminant Analysis (LDA) is proposed. Secondly, the Box-Muller transformation is used to generate Gaussian distributed random variables from a uniform distribution to simulate fault data. The algorithm validation results show that the proposed method can effectively diagnose transmission line faults and provide strong technical support for the stable operation of power systems.

Wang Wei, Jiang Yonglin, Yin Jinyang, Li Xiaoyang, Huang Yingjie, Qiao Huilong. Research on Fault Diagnosis of Transmission Lines Based on Machine Learning. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 166-172. https://doi.org/10.25236/AJETS.2024.070323.

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