Research on Automatic Identification and Location Technology of Key Equipment in Distribution Lines Based on Deep Learning
DOI:
https://doi.org/10.13052/dgaej2156-3306.4045Keywords:
Distribution circuit, deep learning, object detection, equipment identification, operation and maintenance, low-carbon energyAbstract
With the global emphasis on sustainable energy systems and low-carbon economies, the intelligent operation and maintenance of distribution networks has become crucial for enhancing energy efficiency and building an environmentally friendly energy system. This study proposes an automatic identification and location technology for key equipment in distribution lines based on deep learning, aiming to improve the intelligent level of distribution networks and reduce the environmental impact during the processes of energy production and consumption. Through deep convolutional neural networks (CNNs) and object detection algorithms, the method presented in this paper can efficiently identify and locate key equipment in distribution lines, helping to monitor the stable supply of energy, reduce operation and maintenance costs, and support the promotion and application of low-carbon energy by improving energy efficiency. Experimental results show that the proposed method has significant advantages in terms of equipment identification rate, location accuracy, and response speed, especially in reducing energy waste and optimizing the operation efficiency of distribution networks, and it has high practical application value. The research in this paper provides strong support for the assessment of environmental impacts in energy production and consumption, energy-saving technologies, and the construction of sustainable energy systems, and it has broad environmental and social significance.
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