Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

TL;DR

Microgrids face nonlinear dynamics and disturbances that challenge voltage and frequency stability, especially during islanding. The authors propose a model-free secondary controller based on Brain Emotional Learning-Based Intelligent Controller BELBIC to achieve online adaptation with low computational burden. The approach demonstrates Lyapunov-backed convergence, decentralized potential, and superior robustness and response speed compared with PID and NN controllers in a two-generator MG case study, along with sensitivity analyses. The method enables robust, real-time voltage and frequency stabilization and has potential for extension to multi-MG networks and community-based microgrids.

Abstract

In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances. The developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic system. The learning capability of the proposed biologically-inspired intelligent controller makes it a promising approach to deal with the power system non-linear and volatile dynamics without increasing the controller complexity, and maintain the voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent secondary controller is validated in terms of the voltage and frequency absolute errors in the simulated microgrid. Simulation results highlight the efficiency and robustness of the proposed intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers.

Figures (6)

• Figure 1: Computational model of emotional learning.
• Figure 2: Proposed control architecture.
• Figure 3: The frequency and voltage outputs of the system ($SM_1$ and $SM_2$) for the proposed intelligent secondary control. In all cases, BELBIC-based intelligent controller is in Green color, NN-based intelligent controller is in Red color, the PID controller is in Blue color, the system with no secondary controller is in Cyan color, and the reference signal is shown in Black. MG will be disconnected from the main grid at $t=0.2$ second.
• Figure 4: The Frequency and Voltage Absolute Error of the system ($SM_1$ and $SM_2$) for all secondary controllers. BELBIC-based intelligent controller is in Green color, NN-based intelligent controller is in Red color, and the conventional PID controller is in Blue color.
• Figure 5: The Frequency and Voltage Mean Square Error of $SM_1$ and $SM_2$ for all secondary controllers considering the different values for Turbine gain ($K_G$) in $SM_1$. BELBIC-based intelligent controller is in Green color, NN-based intelligent controller is in Red color, the PID controller is in Blue color.