AI Controller Interconnecting Multiple Microgrids Matching All Electrical Parameters

Due to differences in electrical parameters including voltage, temperature, power loss, frequency, phase angle, and power quality, integrating numerous hybrid microgrids (HMGs) into an electrical bus (EB) grid is a significant difficulty in current power systems. In order to guarantee system-wide compatibility and synchronization, this article suggests an Artificial Intelligence (AI)-based Smart Controller architecture that can smoothly link many HMGs to a shared EB grid while automatically adjusting mismatched electrical characteristics. The suggested controller uses a multi-layered artificial intelligence (AI) method that combines reinforcement learning for adaptive control techniques, fuzzy logic systems for dynamic parameter adjustment, and machine learning (ML) models for real-time data prediction. Through IoT-enabled sensors, each HMG which consists of renewable energy sources like solar, wind, and storage is locally monitored, and important metrics are sent to a centralized AI controller. Each microgrid's voltage, frequency, power factor, and harmonic content are examined by the controller, which then uses phase synchronization, voltage regulation, and coordinated inverter control to implement corrective measures. According to MATLAB/Simulink simulation findings, the AI controller successfully minimizes interconnection disturbances, keeps voltage and frequency variations below ±1%, and lowers total harmonic distortion (THD) to 3%. Additionally, the controller exhibits strong performance in the face of partial failures and dynamic loading, guaranteeing grid stability and continuous power quality. In instruction to connect several Hybrid Microgrids (HMGs) with a single Electrical Bus (EB) Grid, the proposed work presents an Artificial Intelligence-based Smart Controller that makes sure that all electrical parameters are accurately matched in real time. varied combinations of renewable get-up-and-go sources, such as terrestrial, airstream, and battery storage, may be found in each microgrid. These sources frequently function asynchronously and produce varied electrical outputs. When linked to a shared grid, these variations in voltage, frequency, phase angle, and harmonic distortion can cause instability and subpar power quality. The smart controller combines reinforcement learning, fuzzy logic, and machine learning methods to resolve this. By enabling real-time monitoring and modification of crucial electrical characteristics, these AI approaches guarantee that all HMGs run in unison with the EB grid.Each microgrid's sensor data is collected by the controller, which then analyses it to look for discrepancies or irregularities before sending out correction signals to the inverters and control circuits in each HMG. According to simulation results, the controller efficiently lowers total harmonic distortion (THD), keeps voltage and frequency within reasonable bounds, and makes sure that all microgrids share power evenly. A more dependable, effective, and sustainable smart grid infrastructure is made possible by the scalable integration of renewable-powered microgrids into national or regional power systems made possible by this clever and modular approach.