Grid-forming inverter technology is a novel and evolving concept for systems with high penetration of renewables. The integration of these grid-independent units with the existing conventional generators and gradually replacing them should contribute to stabilizing the power grid. Several control strategies are proposed in order to design grid-forming inverters to emulate the kinetic energy and the self-synchronization features of synchronous machines with robust control on voltage and frequency to deal with the dynamic issues of the grid. In this paper, a PV-based grid-forming inverter with a modified virtual synchronous machine control in parallel with a battery supported inverter with an enhanced droop control has been considered to operate under non-ideal grid voltage conditions and in the isolated mode of operation. The control methodology encloses a photovoltaic synchronous generator, along with the nonlinear feedback linearization current-limiting control with voltage ridethrough capabilities. They enable the grid-forming PV-inverter and the grid-following battery inverter to provide active and reactive power to the load during unbalanced grid conditions seamlessly. A hardware co-simulation environment in Simulink using the software tool of Piecewise Linear Electrical Circuit Simulation (PLECS) and System Generator from Xilinx has been applied to evaluate the operation of the controller and verify its flexibility and effectiveness in different case studies.

In many pilot research and development (R&D) microgrid projects, engine-based generators are employed in their power systems, either generating electrical energy or being mixed with the heat and power technology. One of the critical tasks of such engine-based generation units is the frequency regulation in the islanded mode of modernized microgrid (MMG) operation; MMGs are microgrids equipped with advanced controls to address more emerging scenarios in smart grids. For having a stable and reliable MMG, we need to synthesize an optimal, robust, primary frequency controller for the islanded mode of MMG of the future. This task is challenging because of unknown mechanical parameters, occurrence of uncertain disturbances, uncertainty of loads, operating point variations, and the appearance of engine delays, and hence nonminimum phase dynamics. This article presents an innovative primary frequency control for the engine generators regulating the frequency of an islanded MMG in the context of smart grids. The proposed approach is based on an adaptive optimal output-feedback control algorithm using adaptive dynamic programming (ADP). The convergence of algorithms, along with the stability analysis of the closed-loop system, is also shown in this article. Finally, as experimental validation, hardware-in-the-loop (HIL) test results are provided in order to examine the effectiveness of the proposed methodology practically.

In this article, we investigate the impact of potential state-dependent false data injection cyber-attacks on frequency synchronization and active power management in islanded ac microgrids. One potential way of affecting microgrid reliability is by forcing a generation outage. Thus, the attacker could potentially aim to desynchronize inverter-based resources in microgrids by manipulating their frequency with malicious injections. The attack signals are injected to manipulate control input channels, sensor nodes, reference values, and the information exchanged through communication networks. In order to mitigate the adverse impacts of such cyber-attacks, first, the conventional distributed consensus-based secondary control approach is modified and complemented in the presence of cyber-attacks. Second, a resilient cooperative distributed secondary control scheme is proposed by utilizing the concept of a virtual layer interconnected with the main network layer. Third, theoretical stability, resilience analysis, and design considerations of interconnection matrices are also provided. Finally, simulations through MATLAB/Simulink and experimental results are presented in order to illustrate the robust performance of the proposed control scheme.

This paper presents an enhanced current controller for improving the performance of a class of single-phase grid-connected inverters operating in weak and distorted grid conditions. An inverter designed to operate at normal (strong or stiff and clean) grid conditions may not perform satisfactorily during weak and distorted grid conditions. One major reason is the interfering dynamics of the synchronization or phase-locked loop (PLL). This paper proposes an enhanced control structure for a popular class of single-phase inverters to address this problem. The proposed idea is to include the PLL state variables into the main inverter controller thereby minimizing the undesirable interactions of the PLL with the other components. A method for optimally designing the controller gains is also proposed. Compared to the conventional one, the proposed controller is shown to have a more robust performance over a substantially wider range of weak and distorted grid conditions. Extensive simulation and experimental results are presented to validate the proposed controls.

In this article, we propose a novel adaptive dynamic programming (ADP) algorithm, named hybrid iteration (HI), to solve the cooperative, optimal output regulation problem (CO $^{2}$ RP) for continuous-time, linear, multiagent systems. Unlike the traditional ADP algorithms, i.e., policy iteration (PI) and value iteration (VI), HI does not need an initial stabilizing control policy required by PI. At the same time, it maintains a faster convergence rate compared with VI. First, a model-based HI algorithm is proposed to solve the CO $^{2}$ RP. Based on the proposed HI algorithm, a data-driven, adaptive, optimal controller is developed to solve the cooperative, adaptive, and optimal output regulation problem without using any information about the physics of the system. Instead, the states/input information collected along the trajectories of the dynamic system is employed. The proposed data-driven HI is applied to the adaptive, optimal secondary voltage control (also known as voltage restoration control) of an islanded modern microgrid based on the inverter-based resources. Compared with the VI and PI algorithms, comparative simulation results demonstrate that the proposed HI approach is significantly able to save the convergence time of the central processing unit (also known as CPU) deployed, reduce the number of learning iterations, and remove the requirement of the initial stabilizing control policy. Comparative experiments reveal the practicality and superiority of the proposed methodology.