Research on Dynamic Error Correction of Electric Energy Meters Using Adaptive Wavelet Packet Decomposition and Generative Adversarial Networks (GANs)

High-precision correction of dynamic errors in electric energy meters is a key technical challenge to ensure measurement accuracy. Traditional methods face issues of insufficient feature extraction capability and limited nonlinear correction effects when dealing with dynamic error characteristics. To address these challenges, a fusion algorithm based on Adaptive Wavelet Packet Decomposition (AWPD) and Generative Adversarial Networks (GANs) is proposed. AWPD is used to extract multi-scale features of the error signal, achieving adaptive decomposition of the signal through entropy criteria. GANs leverage the adversarial mechanism between the generator and discriminator to achieve nonlinear error correction. Experimental results demonstrate the outstanding performance of this fusion model under various operating conditions. Under light-load conditions, the mean error is reduced from 0.15 % to 0.02 %, and the standard deviation of the error is reduced from 0.08 % to 0.01 %. Under random operating conditions, even in high-noise environments, the peak error after correction is reduced from 0.48 % to 0.07 %. Furthermore, the model achieves a correction accuracy rate of over 95 % under complex operating conditions such as full load and load fluctuations, demonstrating strong robustness and adaptability. In summary, the fusion model based on AWPD and GANs can significantly improve the accuracy and stability of dynamic error correction, providing an efficient and reliable solution for dynamic error correction in electric energy meters and exhibiting good prospects for engineering applications.