Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-Based Approach

Abstract

This paper proposes a distributed intelligent secondary control (SC) approach based on brain emotional learning-based intelligent controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC controller is able to learn quick-auto and handle model complexity, nonlinearity, and uncertainty of the MG. The proposed controller is fully model-free, indicating that the voltage amplitude and frequency deviations are regulated without previous knowledge of the system model and parameters. This approach ensures low steady-state variations with higher bandwidth and maintains accurate power-sharing of the droop mechanism. Furthermore, primary control is realized with a robust finite control set-model predictive control (FCS-MPC) in the inner level to increase the system frequency bandwidth and a droop control in the outer level to regulate the power-sharing among the distributed generations. Finally, experimental tests obtained from a hardwarein-the-loop testbed validate the proposed control strategy for different cases.

Intelligent Power Control of Inverter Air Conditioners in Power Systems: A Brain Emotional Learning-Based Approach

“…Also, it doesn't necessitate an additional iterative process for training [27]. The BEL is growingly being used in control engineering [28], electrical motors [29], and AC microgrids [30]. It is demnostrated in [31] that the BEL provides superior performance over neural network and fuzzy logic in stabilizing the power systems under fault.…”

“…The S and E are the A network inputs. Section III-B discusses the selection process for S and E. The A network output is represented by [30], [38]:…”

“…Theorem 1. Given the controller's weight adjustments as in (23) to (31), there is a synthesis of S signal, α and β parameters such that [30], [38]…”

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

“…In MGs control, the steady state error cannot be completely eliminated when droop designs are used in the primary stage. Consequently, an auxiliary control system is implemented to restore voltage profiles and manage system frequency fluctuations, thereby enhancing the system's adaptability at the secondary stage [54–56]. The objectives of the auxiliary control level are to ensure robustness and minimize frequency and voltage deviations.…”

A Distributed Two-Layer Frequency Compensation for Islanded Microgrids Based on Q-learning and PI Controllers

“…In [14], a deep reinforcement learning technique with multiple intelligences was proposed to achieve secondary compensation of frequency for complex control environments with multiple regions. In [15], the authors proposed an improved reinforcement learningbased scheme i.e., a brain emotional learning-based intelligent controller (BELBIC), for secondary control.…”