UMGAN: multi-scale graph attention network for grid parameter identification

Parameter identification of transmission lines plays a crucial role in power systems, and many deep learning methods have been continuously applied to this domain. However, these methods are highly sensitive to data corruption, and as the scale of the power grid continues to expand, the model's solving accuracy deteriorates. In response to these challenges, this paper introduces a multi-scale graph attention network (UMGAN) that leverages the spatial structural characteristics of the power grid. In order to enable the model to fully exploit the correlations between power grid branches and learn more precise node feature representations, we have designed a simplified graph neural network convolution kernel, creating a lighter U-shaped structure for multi-scale sampling of power grid data. Additionally, an attention layer has been incorporated at skip connections to reduce the impact of data corruption. Furthermore, for the multi-task learning module, we have devised a multi-task loss function tailored for power grid parameter identification tasks. This loss function effectively balances multiple objectives, allowing simultaneous identification of multiple parameters. Experimental results demonstrate that the multi-scale graph attention network proposed in this paper outperforms other machine learning and deep learning methods, providing more accurate predictions of power grid branch parameters.