Fault Diagnosis of Distribution Networks Based on IAHA-Optimized VMD and Two-Dimensional Convolutional Neural Networks
Accurate fault diagnosis in distribution networks is often limited by empirical parameter selection in Variational Mode Decomposition (VMD) and insufficient fault feature representation under complex operating conditions. To overcome these limitations, an Improved Artificial Hummingbird Algorithm-Variational Mode Decomposition-Two-Dimensional Convolutional Neural Network (IAHA-VMD-2DCNN) framework is proposed for adaptive feature extraction and fault classification. Using minimum envelope entropy as the objective function, IAHA is employed to optimize key VMD parameters adaptively. The decomposed components are reconstructed into two-dimensional time–frequency feature maps and subsequently fed into a Two-Dimensional Convolutional Neural Network (2DCNN) for automatic feature learning and fault classification. Based on simulation data from the IEEE 33-bus distribution network, the diagnostic performance of the proposed method is evaluated under 11 operating conditions and different transition resistances. The results show that the proposed framework effectively suppresses mode mixing and achieves an overall fault classification accuracy of 95.92%. In addition, it demonstrates strong robustness and excellent feature learning capability under complex operating conditions.