LAUSR

The dynamic performance of a grid-connected inverter in a distributed generation system brings new challenges by affecting the power quality and dynamic stability. The traditional linear control method for the inverter requires complex processes including decoupling and control parameter tuning and relies on a pulsewidth modulation module. The nonlinear control method, e.g., the model predictive control (MPC), can address some of the aforementioned challenges but is incapable of dealing with system parameter changes. A data-driven method using the dynamic Bayesian network-based model predictive control (DBN-MPC) is proposed, which takes advantage of the predictive ability of the DBNs to implement the MPC strategy. A predictive model is built, and the prediction signals are generated based on the DBNs. Then, by constructing the cost function and optimization criteria, the controller generates the optimal switching state combination. Using the proposed DBN-MPC method, the predictive model implements parameter learning online, providing more accurate prediction signals for further optimization and the feedback correction. In addition, the control law of the proposed controller can update over time, which enables the grid-connected inverter system to achieve the optimal control. The case studies using the grid-connected inverter system and IEEE 39-bus benchmark power system integrated with the battery energy storage system demonstrate and verify the superiority of the proposed DBN-MPC method.