Islanding Detection for Active Distribution Networks Using a WaveNet+UNet Classifier

This paper proposes an AI-based approach for islanding detection in active distribution networks. A review of existing AI-based studies reveals several gaps, including model complexity and stability concerns, limited accuracy in noisy conditions, and limited applicability to systems with different types of resources. To address these challenges, this paper proposes a novel approach that adapts the WaveNet generator into a classifier, enhanced with a denoising UNet model, to improve performance in varying signal-to-noise ratio (SNR) conditions. In designing this model, we deviate from state-of-the-art approaches that primarily rely on long short-term memory (LSTM) architectures by employing 1D convolutional layers. This enables the model to focus on spatial analysis of the input signal, making it particularly well-suited for processing long input sequences. Additionally, residual connections are incorporated to mitigate overfitting and significantly enhance the model's generalizability. To verify the effectiveness of the proposed scheme, over 14 000 islanding/non-islanding cases are tested, considering different load active/reactive power values, load switching transients, capacitor bank switching, fault conditions in the main grid, different load quality factors, SNR levels, changes in network topology, and both types of conventional and inverter-based sources.