Research on power grid harmonics recognition based on improved YOLOv5 algorithm

The identification and analysis of harmonics in power grids are crucial for ensuring the stability, reliability, and efficiency of electrical systems. Harmonics, which arise from non-linear loads, can lead to various operational issues such as equipment damage, reduced energy efficiency, and power quality degradation. Traditional methods for harmonic detection often suffer from limitations, including low recognition accuracy, slow processing speed, and challenges in real-time monitoring, making them less suitable for dynamic grid environments. This study addresses these challenges by proposing an enhanced YOLOv5 (You Only Look Once version 5) algorithm for harmonic recognition. The proposed method introduces several key modifications to the YOLOv5 architecture, including improved feature extraction techniques and optimization of the loss function to better capture harmonic patterns in complex power grid data. By leveraging the strengths of YOLOv5 in real-time object detection and combining them with domain-specific improvements, the algorithm is able to achieve superior performance in both harmonic classification accuracy and processing speed. Experimental results demonstrate that the proposed approach significantly outperforms conventional methods, offering a more robust and efficient solution for the detection and analysis of harmonics in power grids. This research presents a promising tool for the development of intelligent monitoring systems that can enhance power grid operation and maintenance, ensuring optimal performance and minimizing potential risks.