Different from the conventional synchronous generator, the virtual inertia and damping control parameters for the inverter-based virtual synchronous generator (VSG) provide more flexibility for stable operation and dynamic performance optimization. However, the operation control principle is still unclear regarding how to coordinate virtual inertia and damping considering both frequency stability and transient synchronization stability. In this paper, an Adaptive Inertia and Damping Coordination (AIDC) control strategy is proposed for grid-forming VSGs to improve transient stability. The proposed AIDC strategy adaptively adjusts the virtual inertia and damping coefficients based on real-time conditions of operation frequency deviation and its rate of change of frequency (RoCoF). The virtual inertia is designed to dynamically increase in the accelerated area and decrease in the decelerated are, and the virtual damping coefficient is designed to increase and enlarge the positive virtual damping effect during the whole accelerated/decelerated transient process. In addition, the proposed AIDC strategy is realized through the practical arctan-function control method with limited boundaries, which can assist engineers. The effectiveness of the proposed AIDC strategy is validated through hardware-in-the-loop experiments.

Grid-forming (GFM) converters are regarded as the most promising solution for grid-connected converters of renewable energy due to their robustness against weak grids. However, attributable to their voltage source characteristics, GFM converters may experience overcurrent issues during large disturbances. The virtual impedance (VI) method is an effective method to solve this problem. Nevertheless, there exists a contradiction between the demands for VI posed by current limitations and transient stability. Firstly, the analytical equations of virtual impedance for limiting the fault steady-state current of a GFM converter with different depths of grid voltage sag are solved. On that basis, an adaptive method based on virtual impedance is proposed to limit the fault current. Moreover, the analytical equation of the output active power of the converter when using adaptive virtual impedance to limit the fault current is solved. On this basis, the effect of the virtual impedance ratio on the transient stability is investigated. Finally, an adaptive virtual-impedance-based current-limiting method with the functionality of transient stability enhancement for a grid-forming converter is innovatively proposed. The enhancement effect of the method is verified by the equal area criterion method and phase portrait method. Finally, the efficacy of the proposed method in limiting fault currents and enhancing transient stability is validated through hardware-in-the-loop (HIL) experiments.

With the intensive integration of photovoltaic (PV) sources into the low-voltage distribution networks (LVDN), the nodal voltage limit violations and fluctuation problem cause concerns on the safety operation of a power system. The intermittent, stochastic, and fluctuating characteristics of PV output power leads to the frequent and fast fluctuation of nodal voltages. To address the voltage limit violation and fluctuation problem, this paper proposes a distributed nodal voltage regulation method based on photovoltaic reactive power and on-load tap changer transformers (OLTC). Using the local Q/V (Volt/Var) feedback controller derived from the grid sensitivity matrix, the voltage magnitude information is adopted to adjust the output of PV systems. Moreover, in order to share the burden of voltage regulation among distributed PV systems, a weighted distributed reactive power sharing algorithm is designed to achieve the voltage regulation according to the rated reactive power. Theoretical analysis is provided to show the convergence of the proposed algorithm. Additionally, the coordination strategy for distributed PV systems and OLTC is provided to reduce the reactive power outputs of PV systems. Five simulation case studies are designed to show the effectiveness of the proposed voltage regulation strategy, where the voltage regulation and proportional reactive power sharing can be achieved simultaneously.

With the large-scale integration of distributed photovoltaics, the morphology and characteristics of distribution networks are becoming increasingly complex. Accurate and efficient modeling of distributed photovoltaic aggregation is of significant importance for the planning and operation of new distribution networks. In this study, we first evaluated the similarity of different photovoltaic generation units by integrating the output characteristics of photovoltaic systems and the sensitivity indicators of grid connection points, proposing a distributed photovoltaic clustering method based on the K-means algorithm. Secondly, the output of the photovoltaic aggregation cluster was calculated using the capacity-weighting method. An equivalent network connecting various photovoltaic clusters was constructed based on the node elimination of the admittance matrix, and a modeling method for distributed photovoltaic aggregation was developed. Finally, the effectiveness of the method proposed in this paper was verified through the IEEE-33 test system. The results show that the distributed photovoltaics aggregation modeling method proposed in this paper can effectively simplify network topologies and improve the accuracy of the calculation results while also improving the efficiency of power flow analysis.

The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.

Cyberattacks such as DoS and DDoS can block or delay data communications in microgrid networks, thereby impairing the control of power electronic converters. These attacks motivate the measurement of heavy hitters and super-spreaders in microgrid network traffic. Both measurement tasks commonly rely on sketches, a class of probabilistic data structures. The two tasks nevertheless impose conflicting requirements: super-spreader detection ignores duplicate items in data stream statistics, while heavy hitter detection retains flows with many duplicate items. Consequently, an existing sketch-based scheme generally specializes in one task even though a data stream may contain both traffic classes. DualMeasure Sketch (DMS) is proposed to resolve this conflict. DMS distinguishes super-spreaders from heavy hitters dynamically and maintains them separately, allowing both to be detected accurately at the same time. CPU and OVS platform implementations are used for performance evaluation. Experimental comparison with the state-of-the-art sketches shows concurrent support for both tasks. On CAIDA19 with 1 MB memory, DMS improves super-spreader F1-score by up to 40% and reduces heavy-hitter error by up to 80% compared with dedicated state-of-the-art sketches. In joint deployment on CAIDA19 with 1 MB total memory, DMS improves throughput by about 12% over Combine Sketch.