Maximum Power Point Tracking (MPPT) algorithms mainly govern the performances of solar photovoltaic (PV) array of smart grid systems. To optimize systems' output power in both steady-state and transient situations...
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Maximum Power Point Tracking (MPPT) algorithms mainly govern the performances of solar photovoltaic (PV) array of smart grid systems. To optimize systems' output power in both steady-state and transient situations of the tracking process, a framework using an individualized sparse-aware time-adjusting stepsize adaptation technique for the traditional MPPT method is presented. The objective of this framework is to adaptively predict the produced power of a PV system instead of utilizing the currently estimated power to achieve better performance with the improved tracking abilities. This framework is based on predicting the power using the previously estimated values of the PV output voltage. In addition, the adaptation process is based on a time-varying stepsize that adjusts in accordance with the value of PV voltage. This approach presents a superior performance compared to the conventional method by providing an outstanding tracking ability and maintaining a stable performance under rapid alterations of atmospheric circumstances. (c) 2020 Elsevier Ltd. All rights reserved.
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