Abstract
A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.
Highlights
A photovoltaic (PV) system, which generates electric power purely from solar energy is an important solution towards future-generation smart grid planning, pollution reduction and sustainable global energy saving [1,2,3]
There are a number of maximum power point tracker (MPPT) strategies available in the literature, an efficient and effective MPPT algorithm design is still required to optimize PV system performance [4,5]
No mistake has been found for misidentification of 1 as
Summary
A photovoltaic (PV) system, which generates electric power purely from solar energy is an important solution towards future-generation smart grid planning, pollution reduction and sustainable global energy saving [1,2,3]. A maximum power point tracker (MPPT) is the most realistic strategy to widely apply PV systems to existing power grid designs. PV system power generation can instantly drop by 60% due to local weather changes in windy and humid regions, whereas this situation hardly happens in desert areas [7,8,9]. Local weather conditions, such as the sun’s position, wind speed, land shape, cloud density, and cloud movement, affect the solar irradiance amounts and impact the PV power output. Based on investigations of specific solar irradiance variations, for any particular PV system, the MPPT method has to be customized based on local weather conditions
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