A novel microgrid islanding classification algorithm based on combining hybrid feature extraction approach with deep ResNet model

Microgrids are essential for developing the future energy systems. Microgrids can be utilized in grid-connected or island mode, enabling increased integration of renewable energy sources into a power system. However, due to the increased penetration of converter-based renewable energy sources, the quality of power in microgrids may be adversely affected. Therefore, finding an appropriate technique to classify and detect islanding and non-islanding events in microgrids is one of the major challenges associated with the design of renewable energy sources. This paper presents a new hybrid approach by using wavelet transform, Stockwell transform and residual neural networks for classification and detection of islanding and non-islanding events. The proposed hybrid approach consists of two main stages: in the first stage, optimum feature images of islanding and non-islanding events are obtained by performing wavelet transform and Stockwell transform. In the second stage, a residual neural network model which is fine-tuned with optimal hyperparameters is determined in order to detect and classify islanding and non-islanding events. Thus, feature image data obtained from microgrid test model are structured as input to residual neural network for classifying of islanding and non-islanding events. By employing the hybrid signal processing approaches with deep learning-based residual neural networks, the validation accuracy of 99.40% is obtained for islanding and non-islanding events with average detection time of 0.1260 s. The effectiveness of the hybrid approach was evaluated through comparative analysis with the results obtained for normal and noisy environments in approaches used by similar studies presented in the literature. Unlike traditional passive island detection techniques, the proposed deep learning-based hybrid approach is considered to have superior performance due to its more dynamic behavior.