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.