Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

•A novel fault diagnosis method combining DenseNet and LSTM networks is proposed for accurate identification of distribution network faults.•By utilizing the dense block design in DenseNet with SE module, the model effectively detects subtle variations in the three-phase voltage and zero-sequence current.•The LSTM network captures contextual information from fault timing signals and mines the fault signals' temporal domain features.•The use of t-SNE for visual analysis illustrates the capability of feature extraction at various stages of the network, providing insights into the model's performance and interpretability.•The proposed method demonstrates exceptional accuracy rates of 99.87 % and 99.82 % for fault identification in the simulated 10 kV and IEEE34 distribution network models, respectively, outperforming other related methods. This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.