Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.19918

On the Mechanism of Multi-Cluster System Separation Under Extreme Events

Nikolaos Sofos·Georgios Tzounas·Federico Milano
View original source ↗Read source document ↗Export metadata ↓

Abstract

Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nikolaos Sofos, Georgios Tzounas, Federico Milano. 2026-10-14. On the Mechanism of Multi-Cluster System Separation Under Extreme Events. https://arxiv.org/abs/2610.19918

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Constrained coalition games for USV swarm confrontation

This paper formulates unmanned surface vehicle (USV) swarm confrontation as a constrained coalition game, capturing cooperation within swarms and competition between swarms. Local task costs are aggregated into coalition objectives to account for tradeoffs among target approach, interception, and formation maintenance, subject to individual geographic constraints and supply line limits shared within each swarm. For prescribed formation sizes and task assignments, explicit linear matrix inequalities in the task weights are derived to establish strong monotonicity of the coalition pseudogradient, thereby guaranteeing the existence of a unique Nash equilibrium (NE) whenever the joint feasible set is nonempty. A distributed robust NE seeking strategy is then developed by integrating gradient tracking and action estimation with projected primal-dual dynamics using local information and neighbor communication. Adaptive compensation addresses unknown hydrodynamic parameters and bounded time-varying disturbances. The disturbance compensation gains are adjusted through local error feedback without requiring disturbance measurements, prior knowledge of disturbance bounds, or a prescribed disturbance generation model. Under the derived weight conditions, the stated dynamics and communication assumptions, and suitable gain conditions, the physical states of all USVs are shown to converge asymptotically to the unique feasible NE. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

eess.SY↗
arXiv2026

Distributed Optimization with Nesterov Momentum and Finite-Time Quantized Consensus

In modern large-scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. In this paper, we consider an unconstrained distributed optimization problem in which information exchange among nodes is governed by a directed communication graph. In our setup we focus on two key challenges. The first is the zigzag phenomenon caused by the objective functions of individual nodes having significantly different curvature along different directions. The second is that the communication channels among nodes are subject to limited bandwidth, which motivates the use of compressed (quantized) messages. To address both challenges simultaneously, we propose QANM, a distributed optimization algorithm that combines Nesterov momentum with a distributed finite-time quantized consensus protocol. Under strong convexity and smoothness assumptions, we show that our proposed algorithm converges linearly to a neighborhood of the optimal solution. Finally, we evaluate QANM on a distributed sensor-fusion problem. With each method's step size tuned separately, QANM exhibits the fastest initial error reduction among the tested methods, including QAGD, Acc-DNGD-SC, and MSDA. Comparisons with QAGD under the same quantized averaging framework illustrate the empirical benefit of momentum, while experiments at two quantization levels demonstrate quantization-dependent error floors.

eess.SY↗
arXiv2026

Bearing-based Circumnavigation with Collision Avoidance in Time-varying Graphs under Limited Target Information

In this paper, we study distributed circumnavigation of a stationary target by a heterogeneous team of agents. Each agent is modelled as a disk rather than a point mass to account for its physical dimensions. The target location is assumed to be accessible only to a small subset of agents, called leaders. The rest, called followers, therefore use only local information available from their designated out-neighbour in the interaction graph characterised by the selection of nearest neighbours. By controlling only angular speeds, we develop a distributed guidance law to circumnavigate a stationary target. The proposed guidance law works for both static and time-varying interaction graphs. Inter-agent collision avoidance is enforced through a logarithmic Barrier Lyapunov (BLF) Function, which guarantees forward invariance of the collision-free set. We show that every follower converges to circumnavigation about the same target as the leader at the end of its directed path in the interaction graph, provided the initial conditions are admissible. Numerical simulations illustrate the effectiveness of the proposed method for both static and time-varying topologies.

eess.SY↗