Abstract
The increasing integration of solar photovoltaic (PV) and wind energy into smart grids requires efficient power
conversion, energy storage and adaptive energy-management strategies. Hybrid inverters provide an important
interface for coordinating renewable generation, battery storage, local loads and grid interaction, while machine
learning (ML) offers data-driven approaches for improving system operation. This review examines the
applications of ML in renewable-energy forecasting, energy management, inverter control, storage coordination
and power-quality improvement, with particular attention to PV–wind–battery hybrid inverter systems. The
reviewed literature indicates that ML has been widely applied to individual functions such as solar forecasting, PV
inverter control, energy optimisation, storage management and fault monitoring, but comparatively fewer studies
integrate these functions within a common hybrid-inverter framework. An illustrative ANN-based supervisory
control case is also discussed to demonstrate the potential of coordinated ML-based operation. The review
identifies key research gaps related to integrated system design, real-time coordination, experimental validation and
consistent performance evaluation. Overall, ML-based supervisory control shows potential for improving
renewable-energy utilisation, grid interaction, power quality and hybrid-inverter performance.
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