This paper proposes an intelligent and fast controller for the parallel DC-DC converters in the islanded DC microgrids (DCMG) in the shipboard power system applications (SPSs). The existence of constant power loads and propulsion loads in the SPS makes the voltage regulation as an important issue in the islanded DCMG. In this paper, the goal is to provide a fast response and proper voltage regulation and optimal current sharing in the stand-alone shipboard DCMG. To achieve this, a nonlinear I-V droop control is firstly designed in the primary control level to ensure the basic performance. Then, a new model-independent technique, entitled intelligent single input interval type-2 fuzzy logic controller (iSIT2-FLC) combined with sliding mode control (SMC), is suggested to further improve the voltage regulation and current sharing accuracy. In this control scheme, an extended state observer (ESO) is employed to estimate the unknown DCMG dynamics, whereas the SMC is adopted to eliminate the ESO estimation error. Moreover, the dynamic consensus algorithm (DCA) is implemented in the secondary control level to achieve coordinated control between DGs in the SPS. The effectiveness and applicability of the new suggested control approach are validated with hardware-in-theloop (HiL) experimental results.

The single-phase nonlinear loads are going to increase in the future and power quality concerns in the residential distribution grid. Since most of nonlinear loads have a dispersed nature, compensating these phenomena may be complicated. On the other hand, the increased capacity of single-phase grid-connected roof-top PV inverters in residential distribution grid can be an opportunity to engage these systems in the power quality issues as custom power devices. By implementing a proper control for roof-top PV inverters, these systems may in addition to inject the fundamental current, additionally act like a virtual harmonic resistance, and dedicate their additional current capacity to compensate the harmonics of residential distribution grid. In this paper, each roof-top PV system is a grid-harmonic supervisor, where it continually measures the PCC voltage harmonics by the sliding discrete Fourier transform algorithm and then individually compensates the measured harmonics by the proposed adaptive-harmonic compensator. In the proposed approach, a current reference is separately determined for compensating each harmonic component. Then, a specific amount of additional current capacity will be dedicated for each component to compensate by using adaptive gain. Finally, the current reference is controlled by a model predictive current controller. It has been tested in a single-phase transformerless inverter (highly efficient and reliable inverter concept) with an LCL filter at the output. Also, the effectiveness of the control scheme has been verified by MATLAB Simulink and experimental tests.

This study proposes a new decentralised non‐linear I–V droop control to achieve current sharing and voltage restoration for paralleled DC nanogrids (DCNGs) in a cluster. The performance of the conventional droop methods in current sharing among parallel converters degrades due to line impedances. The proposed method improves current sharing among the DCNGs with parallel‐connected converters by considering the effect of line impedance, which is an important issue in practical applications. By the proposed method, desired current sharing from the light‐load to heavy‐load condition is achieved. Since only local information (the output voltage and output current of the DCNGs) is used, the proposed method is fully decentralised, and no communication infrastructure is required, which improves the reliability and stability of the overall system. Furthermore, Lyapunov stability theory is applied to investigate the stability of the proposed method. Additionally, the plug‐and‐play feature is achieved as an important desired functionality in the DCNGs as well as the proposed method provides scalability and modularity for the DCNGs in a cluster. In the end, theoretical analysis and experimental results validate the effectiveness of the proposed control framework for different scenarios.