Power Management of Grid-Connected Resources Based on Neural Networks in the Presence of Harmonics and Voltage Imbalance

Nowadays, microgrids have emerged as a platform for maximizing the utilization of distributed energy resources (DERS) as an alternative to traditional power grids. Microgrids consist of multiple distributed generation units, such as wind turbines, photovoltaic systems, diesel generators, and fuel cells, which are controllable and can operate in both grid-connected and islanded modes. Despite their numerous advantages, the operation of these systems presents several technical challenges. Voltage imbalance and harmonics are among the critical technical issues in microgrid operation, which are addressed in this study. In this research, a model predictive control (MPC) scheme is proposed for distributed energy resources to enhance the power management quality of integrated microgrids under conditions of voltage imbalance and harmonics. The proposed strategy primarily includes droop control, model predictive control, and an unbalance compensation mechanism

کلیدواژه ها:

Distributed generation resources ، neural networks ، model predictive control ، microgrids ، voltage imbalance ، harmonics