Brain Emotional Learning-Based Intelligent Controller for Frequency Regulation of Uncertain Islanded Microgrid Considering Renewable Energy Sources

In this paper, an intelligent brain emotional learning-based intelligent controller (BELBIC) technique is presented for the secondary frequency control of microgrid. The effects of renewable energy sources and load changes, power fluctuations and dynamic disturbances along with uncertainty affecting the microgrid frequency are all considered in the studied islanded microgrid. In the intelligent method for tuning and stabilizing the microgrid frequency, the learning of BELBIC technique is based on emotional factors and is able to adjust the microgrid frequency by including nonlinear terms and overcoming the effects of model uncertainty, disturbances, environmental changes and low inertia due to renewable energy sources. The behavior of the system against various changes and disturbances is investigated and compared with the optimal PID control methods. The advantages of the proposed method include low overshoot / undershoot, short settling time and minimization of frequency deviation. According to the simulation results obtained in different scenarios, the proposed control method shows good performance for network frequency stabilization.