Electricity theft has become a growing cause of non-technical losses in power grids， posing significant challenges for the existing detection methods by class imbalance and inadequate feature representation. Therefore， an electricity theft detection method based on improved Wasserstein Generative Adversarial Network with Gradient Penalty （WGAN-GP） and time-frequency feature fusion was proposed. First， an adaptive gradient penalty was integrated with WGAN to model and augment original electricity theft data， thereby mitigating the problem of data sample imbalance. Then， a time-frequency feature fusion method for electricity theft detection was constructed to use a feature extraction module with attention mechanisms to capture global and periodic characteristics of user electricity usage behavior in time-domain dimensionality， while extracting frequency-domain features via Fourier transform in frequency-domain dimensionality， thereby obtaining dual representation of time and frequency domain features. Finally， high-dimensional features were fused and compressed to achieve accurate theft detection. This method was validated on a real electricity usage dataset from a power company. The results show that compared with traditional oversampling methods such as Synthetic Minority Over-sampling TEchnique （SMOTE） and ADAptive SYNthetic sampling （ADASYN）， the four comparative models， K-Nearest Neighbors （KNN） algorithm， Long Short-Term Memory （LSTM） network， Wide & Deep Convolutional Neural Network （WDCNN）， and Flowformer all achieve an accuracy of over 90.00% and an Area Under the Curve （AUC） exceeding 95.00% after applying the proposed data augmentation. On this basis， the time-frequency feature fusion electricity theft detection method attains an accuracy of 95.54% and an F1-score of 95.39%， representing respective improvements of 1.90 and 1.89 percentage points over the Flowformer model. It maintains a high detection rate of 92.71% while controlling the false alarm rate at a low level of 1.66%. The proposed method outperforms all comparative approaches in both the authenticity of generated electricity theft samples and the accuracy of electricity theft user identification， exhibiting strong practical applicability.