A grid equivalent based on artificial neural networks in power systems with high penetration of distributed generation with reactive power control

In the past decades, power systems worldwide have increased in size and complexity due to the increasing penetration of distributed energy resources and the rapid growth of widespread grid interconnections. The frequent grid state changes and the use of local controllers make the determination of appropriate grid equivalents challenging. Conventional grid equivalent techniques are based on one specific grid state. Larger deviations from this grid state leads to a reduced estimation precision of the grid equivalent. To tackle this issue, an artificial neural network (ANN) based approach is proposed in this paper for modelling an interconnected power system with a high share of distributed energy resources with reactive power control. In the proposed approach, the ANN is used as a grid equivalent, i.e., the ANN learns the relationship between the grid states (operating points, switching states and controller parameters) and the power exchange at the interconnection, such that the effects of the external grid area at the boundary lines are accurately estimated. The performance of the ANN-based approach is compared to that of the state-of-the-art REI equivalent and the extended Ward equivalent.