22 July 2019
control system synthesis, stability, Lyapunov methods, three-term control, distributed power generation, power generation control, learning (artificial intelligence), adaptive control, neurocontrollers, uncertain systems, MATLAB/Simulink-based simulations, Lyapunov analysis, emotional signals, low computational complexity, perturbed system set points generation, uncertainty system, proportional–integral–derivative, biologically inspired reinforcement learning technique, biologically inspired adaptive intelligent secondary control, MGs, cyber imperfection, emotional learning-based secondary control structure, system disturbances
In this study, the authors investigate the secondary control of microgrids (MGs) in the presence of cyber imperfections such as delay and/or noise, and system disturbances. The existence of cyber imperfections and disturbance could bring in system uncertainty that will seriously degrade the effectiveness of most existing secondary control such as proportional–integral–derivative (PID), etc. To tackle these issues, a biologically-inspired reinforcement learning technique has been proposed which adjusts its parameters to the perturbed system setpoints generated by the cyber imperfections and system disturbances. The learning capability and low computational complexity of the proposed controller make it a promising approach to take cyber imperfections and system disturbances into account, where traditional control methodologies are not suitable due to their vulnerability to the cyber imperfections. First, an emotional learning-based secondary control structure is proposed, where the impacts of cyber imperfection and disturbance have been captured efficiently. Then, the real-time update laws are developed for generating the proper emotional signals (ESs) to stabilize the frequency and voltage. Ultimately, using the generated ESs, the secondary control of MGs is achieved. The Lyapunov analysis has been provided to prove the stability of the proposed design. Moreover, MATLAB/Simulink-based simulations demonstrate the effectiveness of the proposed algorithm.