Abstract
The major drawback of photovoltaic (PV) systems is their dependence on environmental conditions, such as solar radiation and temperature. Because of this dependency, maximum power point tracking (MPPT) control methods are used in PV systems in order to extract maximum power from the PV panels. This study proposes a controller with a hybrid structure based on angle of incremental conductance (AIC) method and Interval Type-2 Takagi Sugeno Kang fuzzy logic controller (IT2-TSK-FLC) for MPPT. MPPT performance of proposed hybrid controller is evaluated via detailed simulation studies and dSPACE-based experimental study. The results validate that the proposed hybrid controller offers fast-tracking speed, high stability, and robust performance against uncertainties arising from disturbance to inputs of the PV system.
Highlights
Photovoltaic (PV) power generation has become very widespread throughout the world, because of its advantages such as lesser maintenance, absence of moving parts, which eliminates the noise effect, low cost, and increasing efficiency of the equipment in PV systems
The computational time of these sets is shorter compared to the general Interval Type-2 fuzzy logic controller (IT2-Fuzzy logic control (FLC))
Among conventional maximum power point tracking (MPPT) methods, the angle of incremental conductance (AIC) method is preferred for the operating points
Summary
Photovoltaic (PV) power generation has become very widespread throughout the world, because of its advantages such as lesser maintenance, absence of moving parts, which eliminates the noise effect, low cost, and increasing efficiency of the equipment in PV systems (such as power electronic converters). Yilmaz et al [9] improved a new MPPT method using adapting calculation based on Type-1 fuzzy logic controller. The proposed MPPT method tested by the simulations on Matlab/Simulink under variable atmospheric conditions and compared with the performance of the P&O, INC, and conventional FLC respectively. Altin [13] presented an MPPT method based on an Interval Type-2 fuzzy logic controller. The proposed hybrid MPPT method was tested under standard and changing conditions, and compared with the conventional Type-1 fuzzy logic controller and INC algorithm. Shiau et al [15] presented a comparative study on fuzzy logic-based solar power MPPT algorithms using different fuzzy input variables.
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