A systematic comparison of data-driven and robust DC-link voltage control for photovoltaic–battery energy storage systems

Grid-connected photovoltaic systems with battery energy storage require tight direct-current-link voltage regulation to preserve converter stability and grid compliance, while practical operation also favors high efficiency and strong self-consumption. This paper compares super-twisting sliding mode control, model-free adaptive control, extreme learning machine control, and online recursive least squares with feedforward for a 100 kW photovoltaic plant coupled to a 200 kWh lithium-ion battery energy storage system. Year-long simulations over 8,784 h are driven by irradiance and ambient-temperature data from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources dataset for Hanoi, Vietnam (21.03°N, 105.85°E). The controllers are evaluated using direct-current-link voltage tracking accuracy, frequency quality, total harmonic distortion, power factor, system efficiency, self-consumption ratio, and a weighted multi-criteria score. Quantitatively, model-free adaptive control achieves the lowest voltage root-mean-square error of 4.18 V, online recursive least squares with feedforward obtains the highest efficiency of 98.9 %, and extreme learning machine control gives the highest self-consumption ratio of 83.6 % at the expense of larger voltage deviations. All controllers maintain the direct-current link within 630–770 V, with total harmonic distortion not exceeding 1.52 % and power factor above 0.991. The TOPSIS-based composite ranking is MFAC (0.829) > ORLS-FF (0.722) > ELM-Control (0.470) > ST-SMC (0.445), providing quantitative guidance for selecting control laws according to voltage-quality, efficiency, and energy-autonomy priorities. Under out-of-distribution grid disturbances (voltage sag −15 %, frequency step + 0.3 Hz), MFAC achieves 30 × lower DC-link RMSE than ST-SMC (0.47 V vs. 14.19 V), confirming superior out-of-distribution robustness without any retraining.