Research on Supraharmonic detection in renewable energy grid Inte-gration based on improved ResNet18 - Yangyang Zhu; Fei Zhong; Jiaqi Gao; Yuntai Cao; Xiaotian Wang; Zhihong Jiang (Results in Engineering)
With the increasing proportion of distributed generation in power systems and the widespread adoption of new energy vehicles, more and more devices that utilize power electronic technology are being integrated into the power grid. This has led to a continuous rise in the level of supraharmonic emissions, and the supraharmonics associated with the integration of renewable energy into the grid have become one of the urgent power quality issues that need to be addressed in the power grid. The research analyzes why traditional methods for detecting supraharmonics cannot fundamentally eliminate spectral leakage. An improved ResNet18 transfer learning model is proposed to address the characteristics of supraharmonics in renewable energy grid integration. This model eliminates spectral leakage and provides a comparative analysis of convolutional neural networks and commonly used transfer learning detection models. The experimental results show that the improved IR_ResNet18 model significantly outperforms the original ResNet18 model and other models in terms of accuracy. The detection accuracy of the improved ResNet18 model is 3.2 % higher than that of the original ResNet18 model, and even more noticeably, its accuracy exceeds that of other models, reaching as high as 99.9 %. The improved ResNet18 model demonstrates greater reliability when processing new data, offering stronger generalization capability, better model stability, and enhanced robustness.