Distributed Cooperative Neural Inverse Optimal Control of Microgrids for Island and Grid-Connected Operations

In this paper, a novel distributed secondary cooperative control based on neural inverse optimal control scheme is synthesized for microgrids considering island and grid-connected modes. This approach is developed for each distributed generator to track frequency and voltage predefined values for island mode, and to track active and reactive power references for grid-connected mode, and for smooth switching between these two modes. To achieve these goals, a secondary control layer is proposed to define the required currents trajectories, which then are controlled using the primary controller. Both control layers are synthesized using recurrent high order neural network on-line trained using an extended Kalman filter, which builds up neural models and is to be used to implement the respective inverse optimal controllers. In addition, a pinning technique is also used for achieving synchronization of all distributed generators only requiring neighborhood information. Thus, a large reduction in the communication network complexity is obtained and the control system reliability is improved. Simulations are performed to evaluate the effectiveness of the proposed scheme with a microgrid benchmark. The obtained results illustrate adequate performances of the proposed scheme to operate the microgrids in island mode, grid-connected mode, and to achieve seamless switching between these two modes.