Historically, the power system has relied on synchronous generators (SGs) to provide inertia and maintain grid stability. However, because of the increased integration of power-electronics-interfaced renewable energy sources, the grid’s stability has been challenged in the last decade due to a lack of inertia. Currently, the system predominantly uses grid-following (GFL) converters, built on the assumption that inertial sources regulate the system stability. Such an assumption does not hold for the low-inertia grids of the future. Grid-forming (GFM) converters, which mimic the traditional synchronous machinery’s functionalities, have been identified as a potential solution to support the low-inertia grids. The performance analysis of GFM converters for small-signal instability can be found in the literature, but large-signal instability is still an open research question. Moreover, various topologies and configurations of GFM converters have been proposed. Still, no comparative study combining all GFC configurations from the perspective of large-signal stability issues can be found. This paper combines and compares all the existing GFM control schemes from the perspective of large-signal stability issues to pave the way for future research and development of GFM converters for large-signal stability analysis and stabilization of the future low-inertia grids.

The hierarchical control of the DC microgrid regulates the terminal voltages of the interfacing converter to achieve proportional load sharing and good voltage regulation at the DC bus. In doing so, the difference of the voltage at different nodes increases which results in higher circulating current and leads to higher losses. In this paper, a Reinforcement Learning Based Integrated Control (RLIC) is proposed which will minimize the circulating current and power losses in the transmission. The proposed RLIC consists of a primary and secondary controller. The primary controller is a robust sliding mode controller which receives the voltage references from secondary controller and regulates the terminal voltage and source current accordingly. The secondary control consists of a proportional integral control (PI), and a Deep Neural Network (DNN) surrogate model with implementation of Q-Learning as reinforcement method. A novel DNN based surrogate model uses the droop value from the PI controller and estimates the power loss and the local and global loading difference for a particular node, for a set of operating condition. This surrogate model is used by the Q-Learning based reinforcement technique which adjusts the droop constants and provide the voltage reference to primary controller to maintain load sharing and reduce the power losses, there-by leading to an improved overall efficiency. The proposed control structure is verified to improve efficiency while maintaining the load sharing and bus voltage regulation.

Inverter connected single-phase AC loads cause second-order oscillations in source currents and DC bus voltage. These oscillations degrade the efficiency and reliability of the DC microgrid. In this paper, an adaptive sliding mode control based output impedance shaping (ASMC-OIS) methodology is proposed for voltage regulation, proportional load sharing, and second-order ripple management in a DC microgrid. By using the proposed control method, the magnitude of the output impedance of the source interfacing converter is increased at 2f _ac programmably, which results in the reduction of second-order ripple currents propagating through the converter. Instead, it is propagated to the DC-link capacitor or towards the nodes which consists of some ripple absorption active or passive filter. This leads to an increase in the energy density of the ripple filters. The dynamic consensus-based secondary control is incorporated to ensure proportional load current sharing. A graph theoretical analysis is presented to analyze per unit load sharing among all the nodes. Stability of the proposed controller is analyzed considering multiple source nodes using Lyapunov's approach. A DC microgrid consisting of parallel-connected DC-DC boost converters, DC load, and inverter load is simulated to verify the proposed control strategy. The proposed ASMC-OIS methodology is validated through experimentation.

Grid-connected converters (GCCs) are used extensively for the integration of DC power sources with AC power sources. However, since it is a complex topic, there are many possibilities for regulating grid-injected currents, as well as different modulation techniques for generating full-bridge PWM voltages. The control techniques are directly related to the type of output filter, as well as to the topology of the converter, since a complex plant can require more sophisticated controllers to keep the system stable, and with good regulation performance. Furthermore, a discussion of the applicability of these converters in weak and very weak grids with high inductance content has recently been growing, which adds a greater degree of complexity to the control structure of the converter. In this brief overview are outlined some topics about topologies, output filters, and control, focusing on the current regulation of grid-connected converters. In addition, a discussion of the main challenges and critical areas in operating on weak and very weak grids is also presented.

The ac loads cause the dc-bus voltage and the source current to oscillate at twice the ac supply frequency ( $f_{\mathrm{ ac}}$ ). These oscillations have detrimental effects on sources and converter components. A distributed control strategy is proposed to regulate the current ripples propagating to the sources, in a dc microgrid feeding both the ac and dc loads. The proposed control methodology regulates the output impedance of the source interfacing dc–dc converter dynamically at $2f_{\mathrm{ ac}}$ . The impedance is increased virtually in series with the inductor of the dc–dc converter by a dedicated controller, which regulates the ripple magnitude according to the harmonic reference. Using this, the second-order ripple currents can be mitigated or propagated to the nodes having some ripple absorbing circuits or filters. This improves energy density in ripple filters installed at a node and optimal usage of the dc-link capacitor at node terminals is achieved. The proposed second-order ripple current (SRC) regulation is analyzed in the presence of component variations and communication delay between the nodes. A low-bandwidth communication is incorporated so as to facilitate the per-unit current exchange between the nodes for the proportional dc load sharing. The proposed concept is verified through simulation and experimentation.

Second-order ripples occur in the voltage and current during any DC–AC power conversion. These conversions occur in the voltage source inverters (VSIs), current source inverters (CSIs), and various single-stage inverters (SSIs) topologies. The second-order ripples lead to oscillating source node currents and DC bus voltages when there is an interconnection between the AC and DC microgrids or when an AC load is connected to the DC bus of the microgrid. Second-order ripples have various detrimental effects on the sources and the battery storage. In the storage battery, they lead to the depletion of electrodes. They also lead to stress in the converter or inverter components. This may lead to the failure of a component and hence affect the reliability of the system. Furthermore, the second-order ripple currents (SRCs) lead to ripple torque in wind turbines and lead to mechanical stress. SRCs cause a rise in the temperature of photovoltaic panels. An increase in the temperature of PV panels leads to a reduction in the power generated. Furthermore, the second-order voltage and current oscillations lead to a varying maximum power point in PV panels. Hence, the maximum power may not be extracted from it. To mitigate SRCs, oversizing of the components is needed. To improve the lifespan of the sources, storage, and converter components, the SRCs must be mitigated or kept within the desired limits. In the literature, different methodologies have been proposed to mitigate and regulate these second-order ripple components. This manuscript presents a comprehensive review of different effects of second-order ripples on different sources and the methodologies adopted to mitigate the ripples. Different active power decoupling methodologies, virtual impedance-based methodologies, pulse width modulation-based signal injection methodologies, and control methods adopted in distributed power generation methods for DC microgrids have been presented. The application of ripple control methods spans from single converters such as SSIs and VSIs to a network of interconnected converters. Furthermore, different challenges in the field of virtual impedance control and ripple mitigation in distributed power generation environments are discussed. This paper brings a review regarding control methodologies to mitigate and regulate second-order ripples in DC–AC conversions and microgrids.