Research on Deep Learning Algorithm Optimization for Data Classification and Grading in the Power Industry

The research on the optimization of deep learning algorithms for data classification and grading in the power industry aims to address the challenges posed by the complexity and diversity of power industry data, and to improve the accuracy of data classification and grading through deep learning algorithms. The research adopts Convolutional Neural Networks (CNN) as the basic algorithm, and proposes a deep learning classification model tailored to the characteristics of power industry data - PowerData-CNN. This model incorporates various innovative improvements based on traditional CNN, including the introduction of multiscale convolution kernels to capture feature information at different scales, and the addition of attention modules after the convolutional layers to enhance the model’s focus on important features. Additionally, the research adopts a strategy of dynamically adjusting the learning rate to adapt to the large and unevenly distributed nature of power industry data. Experimental results show that the optimized PowerData-CNN model achieves an accuracy of up to 0.95 in power industry data classification tasks, significantly outperforming traditional machine learning algorithms and other deep learning models.