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The purpose of this study was to develop a maximum-power-point tracking (MPPT) method for photovoltaic (PV) module arrays using a cerebellar model articulation controller (CMAC). Eighteen Sharp NT-R5E3E modules were used in a 9 series 2 parallel configuration. The power generation data at different sunlight intensities and array temperatures were used for obtaining training data. The slope of the power–voltage (P–V) characteristic curves was used for tuning the weight of the CMAC during MPPT. This allowed the quantification and adjustment of the system and improved the tracking accuracy. The CMAC uses an algorithm that mimics the neural structure of the human cerebellum, and it has the ability to learn quickly, associate, and summarize. The time required for training was therefore relatively short, and this can increase the power generation of the PV module array. Some test results showed that tracking with this maximum-power-point (MPP) CMAC was perfectly feasible over a range of different temperatures and sunshine intensities.

Smart Maximum-power-point Tracking Method for Photovoltaic Module Arrays Using Irradiation and Temperature Sensors