Abstract:The output characteristics of photovoltaic (PV) arrays vary with the change of environment, and maximum power point (MPP) tracking (MPPT) techniques are thus employed to extract the peak power from PV arrays. Based on the analysis of existing MPPT methods, a novel incremental conductance (INC) MPPT algorithm is proposed with an adaptive variable step size. The proposed algorithm automatically regulates the step size to track the MPP through a step size adjustment coefficient, and a user predefined constant is unnecessary for the convergence of the MPPT method, thus simplifying the design of the PV system. A tuning method of initial step sizes is also presented, which is derived from the approximate linear relationship between the open-circuit voltage and MPP voltage. Compared with the conventional INC method, the proposed method can achieve faster dynamic response and better steady state performance simultaneously under the conditions of extreme irradiance changes. A Matlab/Simulink model and a 5 kW PV system prototype controlled by a digital signal controller (TMS320F28035) were established. Simulations and experimental results further validate the effectiveness of the proposed method.
In order to ensure that photovoltaic (PV) systems work at the maximum power point (MPP) and maximize the economic benefits, maximum power point tracking (MPPT) techniques are normally applied to these systems. One of the most widely applied MPPT methods is the incremental conductance (INC) method. However, the choice of the step size still remains controversial. This paper presents an improved variable step size INC MPPT algorithm that uses four different step sizes. This method has the advantages of INC but with the ability to validly adjust the step size to adapt to changes of the PV's power curve. The presented algorithm also simultaneously achieves increased rapidity and accuracy when compared with the conventional fixed step size INC MPPT algorithm. In addition, the theoretical derivation and specific applications of the proposed algorithm are presented here. This method is validated by simulation and experimental results.
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