Prediction method for instrument transformer measurement error: Adaptive decomposition and hybrid deep learning models

Abstract

The measurement accuracy of current transformers is crucial for power system protection and trade fairness. The high penetration of renewable energy into the power grid has affected the transient performance of power systems, posing significant challenges for accurate current transformer measurement. To address this issue, this paper proposes a prediction model for transformer measurement accuracy based on an adaptive dual-modal decomposition strategy and a hybrid deep learning architecture. The framework integrates an enhanced Adaptive Time-Varying Filter (A-TVF), an enhanced Adaptive Variational Mode Decomposition (A-VMD), the Residual Error Index (REI), and the Maximum Information Coefficient (MIC). First, A-TVF preprocesses the collected data by setting REI as the optimization objective to adaptively adjust filter construction parameters, including the B-spline order, bandwidth threshold, and decomposition number, and decomposes the collected ratio error sequence to reduce the non-stationarity of the original sequence. Subsequently, indices such as PE and Kurt are used to screen the decomposed sub-sequences and reconstruct the complex components. Then, A-VMD is applied to further decompose the complex components, minimizing MIC by adaptively determining the decomposition number, penalty factor, convergence accuracy, and fidelity parameters. Afterward, the complexity of the subcomponents obtained from the secondary decomposition is calculated, and the entire sequence is reconstructed. Finally, a hierarchical prediction model integrating Temporal Convolutional Networks (TCN), Bidirectional Gated Recurrent Units (BiGRU), and a Multi-Head Attention mechanism (MHA) is employed to predict the reconstructed components and generate the final results. Experimental results demonstrate that the proposed adaptive dual-modal decomposition method significantly improves prediction performance: compared with non-decomposition models, RMSE, MAE, and SMAPE were reduced by an average of 50.12%, 46.09%, and 37.70% in global decomposition scenarios, and by 25.92%, 23.69%, and 19.96% in rolling decomposition scenarios, respectively. These results validate the effectiveness of the proposed method in reducing data complexity and improving the accuracy and stability of Ratio Error predictions.