Application of electric power image recognition technology in power line state assessment

As the main channel of power transmission, the safe and stable operation of power lines is directly related to the overall stability of electric power system(EPS). The purpose of this article is to discuss the application of power image recognition technology in the state assessment of power lines. By constructing a convolutional neural network(CNN)model and comparing it with the recurrent neural network (RNN) and the long-term and short-term memory network (LSTM), this article assesses the performance of different algorithms in the state assessment of power lines. The results show that CNN shows higher computational efficiency and real-time performance when dealing with complex power images. With the increase of image complexity, CNN shows significant advantages in running time. At the same time, in the accuracy of power line assessment, CNN is 25.86% higher than the comparison algorithm, which fully proves its superiority and practicability. To sum up, this article successfully applies the power image recognition technology to the state assessment of power lines, and constructs an efficient and accurate assessment model based on CNN. The model improves the accuracy and real-time performance of the state assessment of power lines and provides strong technical support for the safe and stable operation of EPS.