Fault Diagnosis of Transmission Lines Based on IGWO-CNN

As a crucial component of the power system, the normal and stable operation of transmission lines is vital to the production and daily life of electricity users. To ensure the stable functioning of the power system, the study focuses on ten faults in transmission lines. Utilizing MATLAB/Simulink, a simulation model of the transmission lines is constructed. Leveraging the powerful feature extraction capabilities of Convolutional Neural Networks (CNN), a fault diagnosis model based on CNN is established. To enhance diagnostic accuracy further, the Grey Wolf Optimizer (GWO) integrated with Levy Flight and Tent Map is adopted to optimize the model, introducing an IGWO-CNN algorithm for transmission line fault diagnosis. Simulation results demonstrate that the fault classification model can accurately categorize all types of faults, and the fault localization model can accurately determine the fault location.