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| Large-Signal Stability Analysis Based on Distributed Learning Composite Lyapunov Functions for DC Microgrids Clusters |
| Liu Sucheng1,2, Li Long1,2, Luan Li1,2, Zhou Taohu1,2, Liu Xiaodong1,2 |
1. School of Electrical and Information Engineering Anhui University of Technology Maanshan 243032 China;
2. Key Lab of Power Electronics & Motion Control Anhui University of Technology Maanshan 243032 China |
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Abstract A DC microgrid cluster (DCMGC) is a complex network formed by the interconnections of multiple adjacent small DC microgrids, which can enhance power supply reliability and economic benefits through flexible inter-network power flow control. However, the stable operation of DCMGCs faces significant challenges. On one hand, the system features numerous power electronic interfaces between distributed energy resources (DERs), loads, and the DC bus. Among these, controlled load converters act as constant power loads (CPLs) with negative impedance characteristics from the bus perspective, which may cause voltage instability and power oscillations. The intermittent fluctuations of DERs caused by variable wind speed and solar irradiance, coupled with load-side demand changes and source-side topology variations (e.g., microgrid connection/ disconnection), endow DCMGCs with ‘weak-grid’ characteristics of low inertia and high impedance, severely challenging their large-signal stable operation. On the other hand, traditional Lyapunov-based large-signal stability analysis methods have limitations in constructing appropriate energy functionals as a general approach, especially for the high-order and nonlinear dynamics of DCMGCs. Meanwhile, existing ‘decomposition- aggregation’ distributed modeling methods suffer from high conservatism, leading to overly restrictive stability conditions.
This paper proposes a novel method for large-signal stability analysis based on a distributed learning composite Lyapunov function. First, the DCMGC system is modeled as interconnected microgrid subsystems, and distributed equivalent large-signal circuit models are established to reflect the dynamics and interactions of these subsystems. Then, the Lyapunov stability condition is used to customize the loss function, and the Lyapunov function of each subsystem is constructed via distributed deep neural network parallel learning. Subsequently, a composite Lyapunov function for the DCMGC is formed by integrating subsystem functions. Through case studies on a specific DCMGC topology, the impacts of circuit parameter changes, topology variations, and an increase in the number of microgrids on large-signal stability are analyzed. Finally, hardware-in-the-loop (HIL) and all-physical hardware experiments verify the effectiveness of the proposed method.
The following conclusions can be drawn. (1) The proposed distributed learning composite Lyapunov method combines data-driven with Lyapunov function theory and considers the distributed characteristics of the DCMGCs model. It enables parallel block-solving of subsystem Lyapunov functions in local state space, demonstrating excellent solvability. (2) Compared with the T-S method based on region of attraction (ROA) ‘domain’ information and Braton-Moser’s mixed potential function method based on criterion derivation, the proposed method offers significantly lower analytical conservatism, enabling more accurate and less restrictive stability assessment for DCMGC design and operation.
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Received: 24 March 2025
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2026, Vol. 41 
