Hybrid microgrids (HMGs) have shown great potential for the efficient integration of distributed energy resources and supporting reliable, flexible operation in standalone scenarios. In such systems, the secondary control is essential for restoring voltage and frequency deviations and ensuring accurate power sharing among distributed generators (DGs). While centralized control enables simple and cost-effective coordination, its vulnerability to controller or communication link failures can compromise global power sharing and system stability. This paper presents a resilient centralized secondary control architecture for standalone HMGs that preserves one-way broadcast coordination in normal operation while providing communication-free fault operation through locally deployed, online-trained artificial neural networks (ANNs). The ANNs are deployed at the DGs and the bidirectional interlinking converter (BIC) to predict point-of-common-coupling voltages using local measurements. These predicted values enable local controllers to emulate the behavior of the central secondary controller during system faults. This maintains accurate global power sharing during full centralized system failure and frequency/voltage regulation during isolated BIC operation, without introducing peer-to-peer coordination or additional distributed messaging. Furthermore, the ANNs are trained online during normal operation, allowing continuous adaptation to system changes. Real-time validation is carried out using a high-fidelity field-programmable gate array (FPGA)-based simulation platform with detailed power switching models. Results confirm the effectiveness of the proposed method in both seen and unseen scenarios, demonstrating improved resilience while retaining the low-overhead communication structure of centralized secondary control.

The increasing demand for efficient and resilient renewable-based energy systems is driving research towards hybrid ac/dc microgrids (HMG), offering a practical pathway to future dc-dominant grids by directly integrating a wide range of dc-operated resources. This paper proposes a model-free real-time control strategy for coordinating distributed generators (DGs) to achieve dynamic global power sharing in an islanded HMG. Conventional model-based controllers are replaced with a deep reinforcement learning agent based on the deep deterministic policy gradient (DDPG) algorithm. Unlike most existing studies that rely on offline training through computer simulations, the proposed agent is trained online during normal operation, without requiring predefined system models or manual controller tuning. The effectiveness of the method is validated through hardware-in-the-loop (HIL) experiments under different load variation scenarios, demonstrating robust dynamic response and accurate global power sharing.

The introduction of the three-phase dual-purpose dc-ac/dc power converter (PC), a recent addition to the universal converter family, holds significant promise for improving the reliability and flexibility of modern microgrids (MGs). This paper presents a novel energy management strategy for a flexible hybrid ac/dc MG architecture featuring dual-purpose dc-ac/dc power converters. These converters offer the capability of both dc/ac and dc/dc power conversion, allowing for connection to either the ac bus or the dc bus through simple reconfiguration, thereby providing enhanced modularity and operational adaptability to various MG conditions. Leveraging model-free reinforcement learning (RL) algorithms, the proposed strategy optimizes the operation mode of the dual-purpose converters to minimize the interlinking converter utilization based on historical MG operational data. The feasibility of the proposed MG architecture and the performance of the RL-based energy management strategy are verified by simulation results.

This paper presents the design and implementation of a grid-forming control for a dual-purpose dc-dc/ac PWM modular power converter tailored for three-phase three-wire ac conversion in nanogrid applications. The study emphasizes the integration of grid-forming and droop control mechanisms to ensure system stability and controlled power sharing. Extensive simulations and analyses demonstrate the effectiveness of the proposed control strategy, yielding swift and precise responses to dynamic power demand changes. The research offers valuable insights into the operation and functionality of this power converter topology within ac nanogrids, contributing to enhanced grid flexibility and reliability in renewable energy systems.

As power systems move towards decentralized models based on renewable energy sources with distributed generation, the role of power inverters in microgrids becomes increasingly important. In this paper, a three-level T-type quasi-impedance source inverter (3L-T-type qZSI) with a grid-forming control structure is studied for the first time in an islanded ac microgrid composed by three prosumers, supplying residential loads. The power sharing among prosumers is achieved through the well-known droop control method with virtual impedance, and proportional-resonant (PR) controller-based voltage and current control loops. A proportional-integral (PI) based control for regulating the dc-link voltage of the T-Type inverter was implemented making use of the boost ability of the qZS network. The proper operation of the microgrid has been tested by simulation with PLECS, demonstrating a robust behavior of the overall system and a good dynamic performance of the proposed control structure for the 3L-T-Type qZSI in different conditions.