Dynamic characterization and anti-consistency optimization for gas turbine swarms via network-topology-aware modeling

Gas turbines have become indispensable in modern energy systems due to their exceptional power density and operational flexibility, particularly in dynamic applications. This is especially evident in critical infrastructure such as large-scale gas pipeline networks, where multiple turbines operate collectively as an interdependent swarm. However, the complex interactions within these turbines introduce significant challenges, including safety risks and efficiency degradation that cannot be explained through conventional single-unit analysis. While extensive research has investigated individual gas turbine dynamics, the emergent swarm characteristics of interconnected turbine systems remain poorly understood. The high-order nonlinear nature of gas turbines renders traditional analytical approaches inadequate for capturing these collective behaviors. To address this critical knowledge gap, we present a novel framework for swarm characteristic analysis that combines equilibrium manifold expansion (EME) for model simplification with Hamiltonian analysis for energy dissipation mapping. Our methodology yields two innovative performance metrics: a swarm efficiency index demonstrating strong positive correlation (>0.92) with overall system efficiency, and a consistency index showing superior negative correlation (absolute value > 0.88) compared to conventional measures. The framework further reveals how network topology influences swarm behavior. Building on these insights, we developed an anti-consistency optimal control strategy that achieved 4.77 % energy savings while enhancing causal emergence by 0.05 bits. This research provides insights into energy conservation mechanisms within turbine swarms and offers practical tools for optimizing the operation of distributed energy systems. The proposed approach bridges an important gap between individual turbine dynamics and system-level performance, with significant implications for energy infrastructure management.