The distributed control strategy has been widely employed, facilitating flexible and scalable coordination of different units in DC microgrids. However, the communication networks employed make them cyber-physical systems that are susceptible to cyberattacks. In this paper, a novel cybersecurity vulnerability identification and detection framework based on adversarial learning is developed for DC microgrids. Specifically, a multi-agent reinforcement learning (MARL) algorithm is used to emulate intelligent attackers that exploit system vulnerabilities by generating attacks capable of bypassing the existing detection mechanisms. In response, a data-driven attack detector is developed to complement the detection scheme, enhancing the overall detection capability. The framework utilizes an iterative adversarial learning process, wherein attacker and defender models continuously challenge and improve each other. This dynamic interaction enables the identification of a wider range of potential attacks, resulting in a more robust and adaptable detection mechanism for DC microgrids.

Finite-set model predictive control (FS-MPC) appears to be a promising and effective control method for power electronic converters. Conventional FS-MPC suffers from the time-consuming process of weighting factor selection, which significantly impacts control performance. Another ongoing challenge of FS-MPC is its dependence on the prediction model for desirable control performance. To overcome the above issues, we propose to apply reinforcement learning (RL) to FS-MPC for power converters. The RL algorithm is first employed for the automatic weighting factor design of the FS-MPC, aiming to minimize the total harmonic distortion (THD) or reduce the average switching frequency. Furthermore, by formulating the incentive for the RL agent with the cost function of the predictive algorithm, the agent learns autonomously to find the optimal switching policy for the power converter by imitating the predictive controller without prior knowledge of the system model. Finally, a deployment framework that allows for experimental validation of the proposed RL-based methods on a practical FS-MPC regulated stand-alone converter configuration is presented. Two exemplary control objectives are demonstrated to show the effectiveness of the proposed RL-aided weighting factor tuning method. Moreover, the results show a good match between the model-free RL-based controller and the FS-MPC performance.

Reinforcement learning (RL) has gained popularity in power electronics due to its ability to handle nonlinearities and self-learning characteristics. When properly configured, an RL agent can autonomously learn the optimal control policy by interacting with the converter system. In particular, similar to conventional finite-control-set model predictive control (FCS-MPC), the RL agent can learn the optimal switching strategy for the power converter and achieve desirable control performance. However, the alteration of closed-loop dynamics by the RL controller poses challenges in ensuring and assessing system stability. To address this, the article proposes formulating a Lyapunov function to guide the agent in learning an optimal control policy that enhances desirable control performance while ensuring closed-loop stability. Additionally, the practical stability region of the system is quantified by deriving a compact set regarding the convergence of voltage control error. Finally, the proposed Lyapunov-guided RL controller is validated through a demonstration framework with a practical experimental setup. Both simulation and experimental results confirm the effectiveness of the proposed method.

Finite control set model predictive control (FCS-MPC) is widely researched for converter control, as it incorporates the control objective and system constraints straightforwardly into the control. To address the drawbacks of conventional FCS-MPC, data-driven predictive control schemes, such as reinforcement learning (RL), have been proposed to tackle issues related to parameter sensitivity and unmodeled dynamics. Another challenge with FCS-MPC is the computational burden associated with complex converter systems and longer prediction horizon optimization problems. For FCS-MPC with a longer prediction horizon, the computation cost increases exponentially, rendering it impractical for real-time implementation. Additionally, RL controllers have not been explored for achieving long prediction horizon predictive control in power converters. To bridge this gap, this paper proposes introducing prediction horizons into RL for optimal power converter control, leveraging the nonlinear mapping capability and self-learning characteristics of RL. Thus, a multi-step RL controller is developed to enhance steady-state performance without increasing the computational burden. Both simulation and experimental results confirm the effectiveness of the proposed method.

This paper concerns the control problem of the active and harmonic power sharing caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit for power sharing based on the neighbor's state. With the distributed philosophy, the central controller is not required. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

The modular multilevel converter (MMC) holds promise as a power supply solution for AC-AC conversion in railway traction systems. This paper introduces a indirect finite control set model predictive control (FCS-MPC) approach aimed at enhancing the dynamic performance of the converter. A discrete-time domain state space model is developed to forecast the behavior of both the three-phase side currents and circu-lating currents one step ahead. A cost function is defined to determine optimal insertion indices in each phase, facilitating rapid current tracking. Additionally, controls for phase and arm module capacitor voltages are implemented to further improve performance. Simulation results validate the effectiveness of the proposed strategy, highlighting its superior performance compared to traditional control methods.