An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning

The rapid and effective islanding detection and disconnection of the microgrid are significant for preventing equipment from failure and safeguarding humanity’s safety. To address the drawbacks of active methods and passive methods, an intelligent islanding detection strategy based on parameter-optimized variational mode decomposition (VMD) and deep learning was developed. Firstly, the proposed adaptive variational mode decomposition (AVMD) strategy improves the optimal mode number and penalty term of VMD by utilizing the relative entropy between the original signal and the intrinsic mode functions (IMFs). Then, the Teager energy operator (TEO) further extracts sequence features to track the instantaneous energy of the IMFs. Finally, the AVMD-TEO-MPE -based features are used to train the one-dimensional convolutional neural network (1D-CNN) as a deep learning classifier. Simulation results indicate that the proposed method can effectively differentiate the islanding state under different working conditions with a testing accuracy level of 100% within a maximal detection time of 46.402 ms. It is also noise resistant to a degree. Comparative analysis confirms that the proposed method outperforms the existing method in distinguishing between islanding and non-islanding events.