Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

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This research work explores the conceptualization and evaluation of a hybrid microgrid that taps into the potential of solar photovoltaic systems, wind energy conversion systems, and battery energy storage systems. In practice, nonlinear loads pose power quality issues that significantly impact the performance of hybrid microgrids. To tackle these challenges, an adaptive multi-generalized integrator (MGI) filter is proposed. This filter extracts the fundamental signal from the distorted utility grid voltages. It features a pre-filter with a DC-off set rejection loop, effectively minimizing the DC-offsets from the distorted grid voltages. This facilitates the distribution of active power generated by the hybrid microgrid's sources while simultaneously addressing various power quality problems. In addition, a dynamic power management control approach enhances grid resilience by operating the hybrid microgrid in grid-connected and islanding modes. It provides uninterrupted and high-quality power supply to consumers during grid outages. The performance of this filter is comprehensively assessed through numerical simulations in MATLAB®/Simulink® environment. A real-time laboratory prototype is developed with WAVECT® WUC300 FPGA controller, demonstrating the practical application of the proposed filter. It ensures the minimum DC-offsets of 0.02V, fast convergence, and minimum oscillations. The grid synchronization and power quality index of the grid currents simultaneously achieve a THD of 2.82 %, complying with the IEEE-1547 and IEEE-519 standards. Importantly, the proposed adaptive-MGI filter outperforms the conventional SOGI and LMF filters in terms of nonlinear load tracking capabilities, with a response speed of less than 200μs, thereby highlighting its practical relevance and importance.