This paper investigates the use of a virtual synchronous generator (VSG) to improve frequency stability in an autonomous photovoltaic-diesel microgrid with energy storage. VSG control is designed to emulate inertial response and damping power via power injection from/to the energy storage system. The effect of a VSG with constant parameters (CP-VSG) on the system frequency is analyzed. Based on the case study, self-tuning algorithms are used to search for optimal parameters during the operation of the VSG in order to minimize the amplitude and rate of change of the frequency variations. The performances of the proposed self-tuning (ST)-VSG, the frequency droop method, and the CP-VSG are evaluated by comparing their effects on attenuating frequency variations under load variations. For both simulated and experimental cases, the ST-VSG was found to be more efficient than the other two methods in improving frequency stability.

Distributed power generators in islanded microgrid usually adopt droop control strategy or virtual synchronous generator control strategy to simulate Q-U droop characteristic of synchronous generators for voltage support ability enhancement and reliability improvement. However, because the distributed power generators' capacities and locations are random, they cannot share reactive load proportional to their rated capacity, resulting in overload protection, and even system instability. In order to improve reactive power sharing precision, voltage control precision and system stability simultaneously, an improved reactive power sharing algorithm combining virtual impedance and virtual capacitor is proposed in this paper. Firstly, the three existing open-loop reactive power sharing strategies are summarized and the effect of their parameters on reactive power sharing error, steady-state voltage deviation and system stability are analyzed. Afterwards, based on the analysis results, a reasonable configuration of virtual impedance and virtual capacitor values are put forward to realize the control targets of reducing steady-state reactive power sharing error, improving voltage control precision and system stability meanwhile. Finally, simulation and experimental platforms are built to verify the effectiveness of the proposed strategy.

With increasing penetration of renewable energy sources into distributed power systems, multi-parallel inverters are commonly employed in the interface to the utility grid, giving rise to potential resonance problems. Aiming at this issue, common-mode resonance of single inverter and multi-parallel inverters are analyzed. First, single inverter common-mode circuits model is built. Common-mode paths are found in it and the admittances of each path are analyzed. Based on it, multi-parallel inverters equivalent circuits are modeled and analyzed. In order to investigate common-mode resonance characteristics, self-coupled and mutual-coupled paths are analyzed and compared to single inverter's path. To get more accuracy resonance characteristics, a crossover filter function is introduced and used to separate output common-mode currents at different frequencies for two-parallel inverters. The impact of parasitic capacitance on resonance is examined. Experimental results validate the developed models and the proposed resonance characteristics.

With the rapid development of renewable energy, the widespread integration of power electronic devices into power generation systems has led to increased grid impedance and reduced short-circuit ratio (SCR), causing the grid to exhibit characteristics of weak or even extremely weak grids. This has posed significant challenges to traditional grid-following control strategies, resulting in increased grid harmonics, heightened instability risks, and frequent power generation system failures. In contrast, grid-forming control strategies, which provide active voltage support, have demonstrated superior stability in weak grid conditions. However, neither grid-following nor grid-forming control strategies alone can comprehensively meet the requirements of power systems in terms of stability, economic efficiency, and grid support. To address these challenges, this paper proposes a model predictive control (MPC) strategy that integrates grid-following and grid-forming control. First, a mathematical model of the grid-connected inverter is established, and stability analyses are conducted for both grid-following and grid-forming modes. Second, model predictive control algorithms are thoroughly investigated for both modes, and an optimized control strategy is selected through comparative analysis. Finally, the effectiveness of the proposed strategy is validated using a hardware-in-the-loop (HIL) simulation platform. Experimental results demonstrate that the proposed method significantly enhances the stability of grid-connected inverters under conditions of significantly increased grid impedance and reduced SCR, ensuring reliable operation of power systems. This approach holds substantial practical application value for modern power systems.

The inductance parameter is crucial to realize high-precision model predictive control (MPC) for grid-connected inverter (GCI), while the traditional inductance estimation method is sensitive to the grid frequency deviation and cannot work normally when the active power is zero. To solve these problems, a new inductance online estimation method based on a full order sliding mode voltage observer (FSMVO) is proposed. First, the drawbacks of the traditional FSMVO-based inductance estimation method are analyzed. Second, a new inductance estimation method is proposed, which uses the error between the observed grid voltage and the actual one passed through a filter, whose transfer function is the same as the one of FSMVO. As a result, the new inductance estimation method is immune to the grid frequency deviation. Moreover, a new Lyapunov function is also designed in this article to deduce the new inductance estimation method, which can work well, no matter the active power is zero or not. Finally, by substituting the estimated inductance parameter into the MPC algorithm, the control performance of GCI can be improved. The effectiveness and accuracy of the proposed inductance estimation method are verified by experimental researches.