Construction of a Transmission Line Maintenance System Based on Machine Learning Algorithm Optimization

The transmission line is an important component of the power system, undertaking the key task of energy transmission. When there is an operational fault in the transmission line, it will have a negative impact on the entire power system, and not only that, but also the quality of power supply will be greatly reduced. Therefore, its safe and stable operation is a prerequisite for the normal operation of the entire power system. Considering that the different localization results of existing transmission line fault localization algorithms affect the fast recovery problem of power systems based on faults, this paper constructs a transmission line maintenance system optimized based on machine learning algorithms. Construct a machine learning based NFA model based on real-time monitoring, operation, and historical fault repair times of equipment to distinguish subtle defects of different types of transmission lines; Process the brightness, contrast, stitching method, and other aspects of the image, and finally achieve intelligent detection of subtle defects in transmission lines based on machine learning by calculating the phase grouping gradient. The experimental results show that the system proposed in this paper can better detect fault signals of transmission lines compared to traditional methods.