<![CDATA[Integrating photovoltaic (PV) systems into power grids presents notable challenges related to power quality, especially concerning total harmonic distortion (THD) caused by power electronic converters, which do not comply with IEEE 519 and IEC 61000 standards. This study introduces an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller designed to optimize energy extraction while concurrently mitigating harmonic distortion in three-phase grid-connected PV systems. Unlike conventional IC-MPPT methods, which compromise the quality of the power to monitor performance, the proposed ANFIS-MPPT strategy uses intelligent modulation index adjustment to achieve a dual goal. A comparison of the four irradiation levels (450-1000 W/m²) shows a higher performance: ANFIS-MPPT achieves 0.26% THD at 1000 W/m² compared to 0.44% at IC-MPPT (40.9% improvement), and 0.80% versus 1.12% at 450 W/m² (28.6% reduction). The five-layer ANFIS architecture, trained on temperature and radiation data, shows faster convergence and lower oscillation than the conventional approach. The results confirm that MPPT based on ANFIS is an effective solution to improve the energy quality of grid-integrated photovoltaic installations while maintaining optimal energy conversion efficiency.]]>
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