Exploration on Intelligent Detection Methods for Substation Equipment Based on Deep Learning

With the continuous development of industrial automation and intelligence, traditional methods of substation equipment detection face challenges of low efficiency and accuracy. This study aims to explore a deep learning-based intelligent detection method for substation equipment, aiming to improve detection efficiency, accuracy, and reduce manual costs. The advanced object detection model YOLOv5 is employed in this exploration. Through training and testing on a large number of substation equipment images, rapid and accurate identification of equipment is achieved. Experimental results show that this method outperforms traditional approaches in terms of accuracy, recall, and processing speed, demonstrating promising application prospects. Furthermore, the study delves into the performance of the YOLOv5 model in handling complex scenarios, including its ability to recognize equipment states under various weather conditions and different lighting conditions. By optimizing the model structure and adjusting training strategies, the robustness of the model is enhanced when facing challenges such as occlusion, reflection, and equipment aging. Particularly, the model's capability to detect minor faults is significantly improved, which is crucial for early fault warning and maintenance decision support. In conclusion, the successful application of deep learning-based intelligent detection technology for substation equipment provides new ideas and tools for the safe operation and intelligent management of power systems. Looking ahead, with ongoing technological advancements and further accumulation of data, we believe these intelligent detection technologies will play an increasingly important role in ensuring grid reliability, optimizing operational processes, and reducing operating costs.