Existing denoising algorithms for power quality disturbances (PQDs) have shortcomings such as loss of true signal components

poor denoising performance

and inability to identify 1/f noise and Laplace noise. In order to improve the accuracy and efficiency of PQD identification under noisy conditions

a joint identification method based on neural blind deconvolution (NBD) time-frequency domain denoising is proposed. First

a joint identification model combining NBD and Transformer is constructed. The NBD integrates a time-domain quadratic convolution filter and a frequency domain linear filter to realize denoising

while the Transformer is responsible for extracting features and performing classification on the denoised data. Second

to ensure optimal training effect

a dynamic weighting strategy based on Bayesian uncertainty is proposed

and a joint loss function composed of kurtosis

envelope spectrum objective function

and cross-entropy loss is introduced to optimize the proposed model. Finally

based on the IEEE Std 1159-2019 standard

25 classes of PQDs are generated and simulated. The simulation results show that the proposed method achieves accurate identification of PQDs under different noise types

and outperforms other methods in terms of F1 score

Params

and FLOPs

thereby improving denoising performance

identification accuracy and computational efficiency.