Abstract
Solar water pumping systems provide cost-effective water supplies whenever the mains supply is unreliable. This green solution well matches with synchronous reluctance motor drives, characterised by high efficiency and lighter ecological footprint, compared to the rare-earth permanent magnet counterpart. The challenge is to drive the water pump extracting the maximum power from the photovoltaic panel, at varying sunlight conditions. The proposed algorithm is based on an accurate model of the synchronous reluctance motor, obtained by the offline training of a low-complexity neural network, specifically developed to the purpose. The maximum power point tracking is obtained by modifying the speed of the water pump impeller, and the working point of the solar panel accordingly, to meet the instantaneous solar radiation conditions.
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