<![CDATA[The maximum power point tracking (MPPT) feature in solar power plants is an essential function in increasing the efficiency of electricity production. The incremental conductance (InC) algorithm controls MPPT, aiming to maximize the output power of photovoltaic (PV) panels and increase the efficiency of the solar power plant system. Even though the InC algorithm is simple and practical, this algorithm tends to lack support in precise switching speeds, is sensitive to the measurement precision level, and is inadequate to eliminate power oscillations due to tight switching cycles. The deep neural network (DNN) algorithm has the potential to answer the challenges of MPPT dynamics. DNN’s learning capabilities enable the controller to better recognize the dynamics of shifts in maximum power values, thereby providing more appropriate contact actuation. The input for the DNN is the duty ratio produced by the InC algorithm. The DNN algorithm was implemented on three DC-to-DC power converter topologies, namely buck, boost, and buck-boost, to determine MPPT performance under standard tests and actual environmental conditions. DNN has demonstrated the ability to reduce oscillation effects, speed up steady-state time, and increase efficiency. In actual environmental conditions, the results showed that the buck converter consistently produced the highest power, followed by the boost and the buck-boost converters. Regarding performance efficiency, the buck converter achieved the highest efficiency at 94.58%, followed by the boost converter at 90.79%. Conversely, the buck-boost converter had the lowest performance efficiency, with an efficiency of 79.34%.]]>
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