An Improved Beta Method With Autoscaling Factor for Photovoltaic System

TLDR: In this article, the authors proposed an improved version of the beta method to further improve the overall performance, especially for practical applications, by using an autoscaling factor instead of manually tuning key parameters such as the range of the β parameter and scaling factor.

Abstract: Maximum power point tracking (MPPT) is essential to improve the energy yield of solar energy systems. However, conventional MPPT algorithms show obvious problems such as the conflict of the steady-state oscillations and dynamic speed, and the clash of high computational burden and accuracy. Originated from the beta method, which shows the advantages of fast tracking speed in the transient stage, small oscillations in the steady-state, and medium complexity of implementation, this paper proposed an improved beta method to further improve the overall performance, especially for practical applications. Instead of manually tuning key parameters such as the range of $\beta$ parameter and scaling factor $N$ for different operating conditions, an autoscaling factor is used, which make the method easier in practical implementation and suitable for wider conditions. The meteorological data of two distinct locations are used to verify that the $\beta$ parameters derived from photovoltaic (PV) modules are valid for one whole year under different environmental conditions. A PV system with the proposed MPPT method was built in MATLAB/Simulink, and different indices such as the rise time, the setting time, and the tracking energy loss are used to evaluate the performance of various MPPT algorithms. Finally, two experimental tests were carried out, including the indoor test with solar array emulator and the outdoor test with an actual PV module, respectively, to show the effectiveness of the proposed MPPT algorithm.