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<p>Grid-connected renewable energy systems with high penetration rates exacerbate frequency fluctuations in microgrids, making traditional control methods ill-suited for random source-load variations. This paper proposes an adaptive fuzzy RBF neural network control architecture: dynamically allocating multi-energy priorities through a fuzzy inference layer, enhancing robustness via adaptive scaling of the RBF kernel function and training with Levy noise, and utilizing NSGA-II optimization to balance frequency regulation accuracy with equipment losses. OPAL-RT hardware-in-the-loop experiments validate that under ±50% disturbances, the maximum frequency deviation drops to 0.29&#xa0;Hz (a 40.8% reduction compared to adaptive RBF control), and stable control is maintained even with 20% communication packet loss. This architecture provides a robust dynamic frequency regulation solution for high-renewable-penetration microgrids.</p>

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Adaptive control optimization in multi energy synergy scenarios robustness verification of fuzzy RBF neural network in microgrid frequency regulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Lexin Yang,
  • Rongbin Yao

摘要

Grid-connected renewable energy systems with high penetration rates exacerbate frequency fluctuations in microgrids, making traditional control methods ill-suited for random source-load variations. This paper proposes an adaptive fuzzy RBF neural network control architecture: dynamically allocating multi-energy priorities through a fuzzy inference layer, enhancing robustness via adaptive scaling of the RBF kernel function and training with Levy noise, and utilizing NSGA-II optimization to balance frequency regulation accuracy with equipment losses. OPAL-RT hardware-in-the-loop experiments validate that under ±50% disturbances, the maximum frequency deviation drops to 0.29 Hz (a 40.8% reduction compared to adaptive RBF control), and stable control is maintained even with 20% communication packet loss. This architecture provides a robust dynamic frequency regulation solution for high-renewable-penetration microgrids.