<![CDATA[To optimize the electrical output of a photovoltaic system, maximum power point tracking (MPPT) methods are commonly employed. These techniques work by operating the photovoltaic system at its maximum power point (MPP), which varies based on environmental factors like solar irradiance and ambient temperature, thereby ensuring optimal power transfer between the photovoltaic system and the load. In this paper, an artificial neural network (ANN) is selected as an MPPT technique. The main contribution of the work is to introduce a softplus function trained artificial neural network-based maximum point tracking (SP-ANN MPPT). The proposed method is then compared with a sigmoid function trained artificial neural network-based maximum point tracking (SM-ANN MPPT). The simulation and experimental results show that SP-ANN MPPT is able to track high power than SM-ANN MPPT in different conditions.]]>
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