Research and application of transmission line fault location using k-nearest neighbor algorithm

In recent years, the research on smart grid in the country has achieved significant results, resulting in increasing grid capacity and transmission distances. Transmission lines, as the link for energy transfer in power systems, are responsible for connecting the generation end and consumption end of the power system, which is crucial to ensure its normal and stable operation. Accurate pinpointing of faults in transmission lines can reduce the manpower and resources required for post-fault line inspection work, and improve the reliability of power supply. As a result, the article investigates transmission line defects and localization extensively. Locating transmission line faults is a common use of the KNN method. Based on clustering for noise reduction and density trimming, this research offers an improved KNN method to address the limitations of existing KNN algorithms while working with huge datasets. Using clustering, this approach removes noise and speeds up the search of K-nearest neighbours to improve the classification efficiency of the KNN algorithm without sacrificing accuracy. The experimental findings show that this approach may significantly enhance the KNN algorithm's efficiency and accuracy when dealing with big datasets. It also shows good performance overall.