Intelligent identification of power grid parameters based on dynamic weighting

Transmission line parameter identification plays a crucial role in power systems. However, existing methods for identifying transmission line parameters face several challenges: (1) when dealing with non-Euclidean data such as power grid data, a considerable amount of noise is introduced, (2) inability to treat the power grid as a global system, limiting calculations to individual transmission lines and overlooking inter-branch correlations, (3) high sensitivity to data contamination. To address these issues, this paper proposes a multi-task noisy graph attention network (MNGAN) for power grid parameter identification, which leverages the spatial structure of the power grid and considers the inter-branch correlations. By computing the homogeneity and average degree of the graph data, we introduce an adaptive attention mechanism to the model. This attention mechanism dynamically adjusts the model’s focus on key information, mitigating the impact of data contamination and improving parameter identification accuracy. Additionally, to achieve simultaneous recognition of multiple branch parameters and enhance model training speed and accuracy, we incorporate a multi-task loss function based on homoscedastic uncertainty. Experimental results demonstrate the superiority of our proposed model over other machine learning and deep learning methods, providing more accurate predictions of branch parameters.