Deep Learning-Enhanced Stability Control for Renewable Energy-Based Microgrids

Numerous electrical loads are situated in remote locations where it is not feasible to obtain power supply through the primary power grid. A Microgrid situated in the local area is capable of delivering the necessary power supply under these circumstances. There is typically an opportunity to integrate several of these Microgrids; however, the integration of all Microgrids may be hindered by geographical constraints. In these situations, estableshement of a smart grid through the integration of various Microgrids, coupled with an effective battery management system, presents a viable solution to address numerous challenges. A new intelligent grid control system is introduced in this paper, accompanied by a centralized battery management system. The use of various renewable energy sources across multiple sites for power generation is regarded as a means to ensure stable and dependable energy supply for multiple electric loads stationed in diverse locations. The suggested approach is implemented utilizing an Artificial Neural Network (ANN) associated Long Short Term Memory (LSTM) to ensure a consistent and high-quality supply across various load buses. Artificial intelligence-based operating systems are designed to manage energy efficiently across different conditions in this study. To enhance power quality, an innovative inverter control mechanism has been integrated into the system. The MATLAB/Simulink software package is employed to achieve the desired outcomes across different case studies.