Abstract: Nowadays, the recognition of the partial discharging(PD) pattern at the cable terminations based on machine learning often leads to the insufficient generalization ability and low recognition accuracy due to the lack or imbalance of the marked data. In order to solve the problem, a novel method for improving the recognition accuracy of the cable termination PD based on the improved Wasserstein generative adversarial network(WGAN) is presented in this paper. Taking the wavelet T-F spectrums corresponding to the pulses as the object, this method first trains the improved WGAN model with the conditional generation capabilities and stable training process to generate the new samples. Then it uses the samples to expand the original data set and improve the sample diversity. Finally, the expanded data set is used to train the new PD classifier. The experiment results show that, compared with the other generative models, the method in this paper generates new high-quality samples more stably. When this method is used to expand the typical cable termination defect data, the new classifier trained has better generalization ability and is applicable to different classifiers. This method effectively suppresses the over-fitting risks caused by the lack or imbalance of data in the identification of partial discharging types in engineering, improving the recognition accuracy.