Meixuan Jade Li

Dynamic task allocation for unmanned aerial vehicles (UAVs) is often constrained by stale and asymmetric information. Variants of the Consensus-Based Bundle Algorithm (CBBA) can waste bandwidth by transmitting every state or bid update without distinguishing assignment-altering information from irrelevant perturbations. This paper proposes Assignment-Consistent Dynamic Multi-UAV Task Allocation (AC-DMTA), a decision-centric framework in which agents communicate only when local uncertainty may invalidate the current assignment. AC-DMTA represents stale records with uncertainty sets and converts point bids into interval bids, enabling task-winner and bundle-stability certificates to suppress assignment-irrelevant messages. These certificates provide local sufficient conditions for certifying the stability of task-winner and bundle decisions within the modeled uncertainty envelopes and candidate sets, allowing assignment-irrelevant communication to be skipped. Timestamp and recency records further resolve asynchronous conflicts without broad resynchronization. Across four benchmark environments and 20 paired seeds, AC-DMTA sends fewer bytes and directed messages than periodic CBBA, Event-Driven Consensus-Based Bundle Algorithm (ED-CBBA), Clustered CBBA, and Two-Level Clustered Consensus-Based Bundle Algorithm (TLC-CBBA), achieving byte reductions of 10.4–91.1% across baseline–scenario pairs while maintaining competitive reward and task-completion rates. Under the tested delay and packet-loss stress conditions, AC-DMTA also achieves the lowest number of bytes per completed task among the compared methods.

The size of an unmanned aerial vehicle (UAV) swarm and the capabilities of each individual in the swarm are critical determinants of mission effectiveness in multi-target encirclement. This paper proposes a framework for strategic force allocation during mission execution. A lightweight estimator is designed to predict the required scale of UAV teams based on target difficulty indicators in the tested settings, including the number of hunted targets and their mobility. Owing to the computational efficiency of the Experience Library, the proposed framework can rapidly re-optimize UAV allocation when target difficulty changes or the available UAV budget is reduced, thereby enabling adaptive swarm resizing and resource redistribution under dynamic disturbances and constraints. Meanwhile, system performance under different force allocations is evaluated from two dimensions: mission success rate and time-to-target. The aim is to achieve mission lightweighting and avoid redundant resource consumption.

In scenarios, such as disaster response, remote area surveillance, and military reconnaissance, where stable communication infrastructure may be unavailable, uncrewed aerial vehicles (UAVs) are often deployed to physically carry data. Ensuring data reliability in such scenarios, especially in adversarial environments, requires data backup mechanisms for UAV swarms. This article addresses the critical yet often overlooked challenge of data recovery difficulty in UAV swarm networks. We introduce data recovery entropy (DRE) to quantify the spatial dispersal of backed-up data across the swarm. Based on this metric, we propose two data backup strategies: one enables adjustable level of DRE through a tunable parameter and the other prioritizes minimizing DRE. The proposed data backup strategies are tailored to the unique characteristics of UAV swarms, focusing particularly on their dynamic communication topologies. Finally, we vary system parameters, including node deployment parameters, communication parameters, and mobility parameters, to validate the proposed approach.

Power systems around the world exhibit diverse topological characteristics depending on historical and geographical factors. In this paper, we aim to acquire insights into the relationship between the topological characteristics of power systems and their robustness through controlled experiments. To achieve this, we generate synthetic networks that incorporate the generation and load settings of practical power systems. We test various aspects, such as topological properties of regional power grids, interconnections among regional power grids, network sizes, and power balance of regional power grids. We derive inspiring results by performing cascading failure trials in test networks. Specifically, a wide and compact power grid can be more robust than a narrow and elongated power grid. Moreover, increasing the capacity of interconnection links among regional power grids may result in a less robust power system concerning cascading failures.

In this paper, we demonstrate the interdependence among buses in power systems that experience voltage instability events by deriving interaction graphs through successive transition probability matrices and the Markov transition matrix. A quasi-stationary convergence has been observed in the probability transition matrices, suggesting a pattern in voltage instability failure. We propose algorithms to identify the pattern, referred to as fault chains, by tracing the interaction graphs. The interaction graphs and fault chains can be used to formulate strategies for enhancing network robustness, including the assignment of new photovoltaic buses, load rescheduling, and equipping droop controllers. Numerical experiments performed on the IEEE 118-and 300-bus systems demonstrate that equipping droop controllers at selected buses is an effective approach for improving network robustness.

This paper studies cascading failure propagation in power systems and presents methods for the quantification of important properties of failure propagation. First, the topological properties of cascading failure propagation are examined. This includes an analysis of the electrical distance between consecutive failures, shedding light on the spatial spread of failures. Additionally, the formation of islands in cascading failure processes is explored to understand their topological characteristics. Second, the paper measures the evolution of a power system during cascading failure processes, considering both the topological changes of the overall system and the propagation rates of system loss. This analysis provides a measurable comprehension of how the system evolves and adapts as failures propagate. Third, this study investigates system loss and analyzes the contributions of various failure mechanisms to the overall system loss. Numerical experiments yield valuable insights into the propagation of cascading failures, leading to several significant conclusions. The findings from this research can inform the development of effective strategies for resilience enhancement and risk mitigation.

In this study, we evaluate the impact of frequency control and the penetration of inverter-based resources (IBRs) on cascading failures in power systems. The modeling of controllers for IBRs requires obtaining realistic state information during cascading failure processes. However, cascading failure events can alter system topologies and the power flow Jacobian, leading to unsolvable power flow even when an equilibrium point exists. To address this, we propose a model that tracks the steady-state voltage profiles across successive cascade generations. This model enables the incorporation of droop controllers for IBRs into the cascading failure model. Additionally, it allows for capturing the effects of the interaction between local primary frequency control and system-level frequency control on cascading failures. Numerical experiments conducted on the IEEE 118- and 300-bus systems demonstrate the effectiveness of increasing primary frequency control reserve in mitigating power outages. However, increasing the droop control coefficients, which aim to enhance system robustness, results in larger power outage sizes.

In this paper, we investigate the influence of the locations of inverter-based resources (IBRs) on the synchronization performance of power networks. We propose two indexes to measure the distribution of inertia in power networks, considering the distribution of control parameters and the topological factors jointly. The first index is the inertia clustering coefficient, which measures how densely the neighbors of each node are connected in a power network. The second index is the inertia centrality coefficient, which captures whether the distribution of inertia in a power network is centralized or peripheral. We characterize synchronous generators (SGs), grid-following inverters (GFLs), and grid-forming inverters (GFMs) by their damping and inertial properties. We evaluate the synchronization performance of the system after disturbances by adopting the settling time and hertz-sec metric. Both the frequency response and the trajectories of eigenvalues show that the location of IBRs has a significant impact on the synchronization performance of the system. Monte Carlo simulations are conducted on two test networks, the results of the IEEE 30-, 57-, and 118-bus systems demonstrate a strong correlation between synchronization performance and the two inertia distribution indexes.