Enhanced Transient Stability in Hybrid DC/AC Microgrids: Robust Composite Control Strategy With Virtual Capacitors Integration Using ANFIS-Optimized Control Gain Parameters

Abstract

Fluctuations in renewable energy generation due to unpredictable weather pose major challenges to power balance in hybrid DC/AC microgrids (HDAMGs). The inclusion of bio-renewable energy sources further complicates operational stability. This paper proposes a robust composite control strategy integrating a non-singular integral terminal sliding mode controller with a nonlinear backstepping controller. The scheme is enhanced by an adaptive fractional-order reaching law, ensuring dynamic stability, chattering elimination, and finite-time convergence. To maximize renewable energy utilization, an artificial neural network-based global power point tracking algorithm optimizes energy extraction from solar PV and wind turbines. An adaptive neuro-fuzzy inference system further tunes control parameters in real time. A virtual capacitor is employed to enhance inertia, transient response, and power-sharing accuracy. System stability is validated through control Lyapunov functions and the complete strategy is implemented on the Simulink platform. Extensive simulations demonstrate that the proposed method eliminates overshoots and improves settling time by 72% compared to the same controller without the virtual capacitor. Compared to existing controllers, it achieves up to 86% overshoot reduction and 78% faster settling. Under $\pm$15% parameter variation, it maintains robustness, delivering 58–76% improved settling time and 75–81% overshoot reduction, thereby ensuring reliable HDAMG performance under dynamic conditions.