Analysis of Power System Signal Processing Method Based on Deep Convolutional Neural Network

This study investigates the application of Deep Convolutional Neural Networks (DCNNs) in power system signal processing. The research addresses the growing challenges in modern power systems, including increased complexity and data volume. We comprehensively analyze DCNN-based methods for electric load forecasting, fault diagnosis, and power quality assessment. Through extensive experiments and case studies, we demonstrate that DCNNs consistently outperform traditional approaches in accuracy, real-time performance, and robustness. The study explores various DCNN architectures and proposes improvements tailored to power system characteristics. Results show significant enhancements in prediction accuracy and processing speed across different tasks. While challenges such as model interpretability remain, the findings highlight the potential of DCNNs to revolutionize power system signal processing. This research contributes to advancing intelligent power system management and provides a foundation for future developments in smart grid technologies.