Dynamic modeling for VSG cluster by using data-physical driven method

Abstract

In the task of system analysis for VSG cluster, aggregation modeling method is widely used for simplification. However, there are inevitable errors occur from the process of cluster aggregation. To improve the accuracy of VSG cluster modeling, a data-physical driven modeling method is presented. At first, the equivalence between aggregation error and black box modeling issue is analyzed. Secondly, a hybrid model structure is proposed, which consists of single machine aggregation model and deep neural network based aggregated-error model. Then, to illustrate the modeling procedure, test cases are studied under large disturbance and multi-operating points conditions. The simulation results confirm that the proposed method can provide satisfactory modeling accuracy.

Keywords

• Virtual synchronous generator
• Dynamic modeling
• Deep learning
• Neural network