Deep Neural Network-Based Stability Region Estimation for Grid-Converter Interaction Systems

The large-scale integration of renewables in the modern power system will lead to a large number of power electronics in the power system and pose interaction stability challenges. Impedance-based stability analysis methods have been widely adopted for the stability evaluation of interconnected power converter systems. However, they are small signal stability analysis tools that can only effectively estimate stability near a certain operating point; they are not effective for grid-converter interaction systems due to the wide variation of operating points caused by the fast and large fluctuations of renewable energy and load. To address this challenge, this article proposes a double deep neural network (DNN)-based black-box modeling and stability region estimation approach for grid-converter interaction systems. First, a DNN-based multioperating point (MOP) impedance model is proposed to build the impedance model covering multiple operating points. Next, a DNN-based stability evaluation model is developed based on the MOP impedance model and the physical nature of the whole system for the estimation of the stability region. The proposed double DNN-based method can achieve fast and accurate online estimation of the stability region for grid-converter system under large variations of renewable energy. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method to achieve accurate identification of the MOP impedance model and to generate an accurate stability region of the system.