Multi-Machine Learning Models-Based Stability Prediction and Maximum Delay Analysis in Decentralized Smart Grids

With the increasing penetration of renewable energy, power systems face spatiotemporal heterogeneity in supply-demand dynamics, challenging traditional centralized control methods due to communication delays and privacy risks. Decentralized Smart Grid Control (DSGC) offers a promising paradigm but requires precise coordination between time delay parameters (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex>) and control gains (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g$</tex>) to ensure stability. This study proposes a multi-machine learning framework to address two critical challenges: (1) predicting stability under nonlinear parameter coupling and (2) dynamically quantifying the critical maximum delay time (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> _max). Three models-Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machine (SVM)-are implemented for stability prediction. Experimental results demonstrate RF's superior performance, achieving 92.30 % accuracy, 0.8916 F1-score, and 0.9126 AUC, outperforming BPNN and SVM. Key stability rules are extracted via ensemble learning, revealing thresholds for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> (6.16s), g (0.64), and power fluctuation (3.75) that significantly influence stability. Delay analysis highlights nonlinear relationships between parameters, with low-gain scenarios showing a 51.6 % stability improvement over high-gain cases. This work provides a data-driven framework for optimizing DSGC parameters and enhancing grid resilience in decentralized energy systems.