Online Transient Stability Prediction Method for Microgrids Considering Current Saturation During Interactions of Different Distributed Energy Resources

In practical microgrids, current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors. Existing methods of online transient stability prediction (TSP) are suitable for power systems consisting of homogeneous distributed energy resources (DERs), thus showing limited accuracy for stability prediction of microgrids. This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation. First, a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy. It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation. Besides, impacts of load fluctuation and fault change are also considered to improve robust prediction performance. Second, to further improve prediction accuracy, an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network (DBN). Then, by combining feature selection method and deep-learning-based TSP model, an online TSP method is derived. Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions. Furthermore, the method effectively avoids feature redundancy and the curse of dimensionality. Numbers of input features are independent of the scale of microgrids.