Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering

Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article proposes the unscented Kalman filtering (UKF) and deep neural network algorithm (DNN) as an innovative approach to detect and prevent islanding events in microgrids. Initially, the UKF works as a stage-one state observer to analyze the voltage signals at the distributed generation (DG) terminal or point of common coupling (PCC). Then, the UKF-estimated voltage signal is provided to DNN for calculating the DNN residuals (DNNR) index by simply taking the vector subtraction of the UKF-estimated voltage from the measured PCC voltage. Then, the DNNR index is continually monitored on the DG terminal or PCC, and if the DNNR is more than the prespecified threshold value, the presented MAIP scheme works successfully to detect the islanding event. The presented MAIP method is proven through massive simulations on standard IEEE UL174 test beds via MATLAB/Simulink software. Results reveal that the suggested MAIP method effectively detects the islanding events in unbalanced/ balanced load generation situations. In addition, the presented MAIP scheme can discriminate between islanding/non-islanding events. The method has a very low computational burden, a very decreased non-detection zone, prompt operation, and a high accuracy of 98.5%.