Identifying super spreaders is a core task in data stream processing. For this purpose, probabilistic data structures called sketches are widely used because they trade memory for estimation accuracy. Many existing sketch-based methods detect super spreaders inside fixed-length intervals known as epochs, clearing the sketch when each epoch ends. However, flushing the sketch in this manner can introduce a transient rebuilding phase during which the accumulated statistics have not yet stabilized. A super spreader that emerges within this interval may therefore be missed. On the other hand, if the stream is measured continuously without ever resetting the sketch, outdated entries build up over time and can reduce accuracy. To address this trade-off, we introduce CycleSketch, a sketch that performs continuous super spreader detection while purging stale data. Concretely, CycleSketch combines a sliding window with a circular queue to keep track of candidate super spreaders and cyclically overwrites aged entries by resetting the slot associated with each new epoch—without globally clearing the count part. To gauge its practical effectiveness, we build and benchmark CycleSketch on both conventional CPU hardware and the OVS virtual switch. On the evaluated traffic traces and under the tested settings, CycleSketch achieves higher measured precision and F1 score than the compared sketch-based detectors, with improvements of 50% and 125%, respectively.

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.

With the increasing deployment of EV charging infrastructure, large numbers of power electronic converters are being integrated into distribution networks, making small-signal stability a key issue for converter-intensive charging facilities. In practical EV charging stations, grid-following (GFL) and grid-forming (GFM) converters may coexist and operate in parallel. Due to their different synchronization mechanisms and control structures, the stability of such mixed systems is affected not only by grid strength but also by EV charging power levels and power sharing among converters. This paper investigates the impedance characteristics and small-signal stability of three representative parallel configurations: all-GFL, all-GFM, and mixed GFL/GFM systems. Unified dq-domain impedance models are established for both converter types, and the critical short-circuit ratio (CSCR) boundaries are determined through eigenvalue-based analysis. The effects of charging power level and power-sharing ratio on the stability boundaries are further analyzed. The results show that the all-GFL configuration becomes unstable under low-SCR conditions due to phase-locked-loop-dominated dynamics, whereas the all-GFM configuration loses stability under high-SCR conditions due to reactive-power-loop dynamics. In contrast, the mixed GFL/GFM configuration exhibits both lower and upper stability boundaries and remains stable only within an intermediate SCR range. These findings reveal the combined influence of converter control type, grid strength, and charging-power operating conditions, providing guidance for the planning and stable operation of converter-intensive EV charging facilities.

With the increasing penetration of electric vehicles (EVs) and distributed energy resources, source-load uncertainty, spatiotemporal variability, and nonlinear coupling make rapid and accurate assessment of operating states more difficult in active distribution networks. This paper proposes a Physics-Informed Graph Attention Network (PIGAT) surrogate model for power flow prediction. PV output is represented by a Beta distribution, and EV charging loads are generated by category-based Monte Carlo sampling. The graph attention mechanism captures the topological relationships among buses. A physics-informed loss combines nodal power balance and system active power conservation constraints to improve the physical consistency of the predictions. The model maps source-load inputs to bus voltage magnitudes, voltage phase angles, and system active power loss. Tests on the IEEE 33-bus and 69-bus systems evaluate prediction accuracy, physical consistency, inference time, performance under light- and heavy-load conditions, and performance with limited training data. On the IEEE 33-bus system, the MAE and RMSE of bus voltage magnitude predictions are below 5 × 10⁻⁴ p.u.; the MAE and RMSE of system active power loss predictions are 0.635 and 0.712 kW, respectively; and the MAE of the active power conservation deviation is 0.862 kW. The pure forward-pass inference time is 1.58 ms, compared with 8.19 ms for MATPOWER; including the optional physics-residual check, the total evaluation time is 2.64 ms.