Abstract
This paper presents an overview of the maximum power point tracking (MPPT) methods for photovoltaic (PV) systems used in the Micro Grids of PV systems. In the PV system, the output varies nonlinearly with temperature and radiation, and the point at which power is maximized appears accordingly. The MPPT of the PV system can improve output by about 25%, and it is very important to operate at this point at all times. Various methods of tracking the MPP of the PV system have been studied and proposed. In this paper, we discuss commonly used methods for the MPPT of PV systems, methods using artificial intelligence control, and mixed methods, and present the characteristics, advantages, and disadvantages of each method.
Highlights
The photovoltaic (PV) system is receiving much attention because it is an infinite, eco-friendly energy source
In the perturbation and observation (P&O) method, PV module power is calculated by the measured voltage and current, with the maximum power point tracked by changing the reference value (Vmpp ), which is the maximum power point according to the power and voltage change, by a certain size
When maximum power point tracking (MPPT) control using a neural network is classified according to the type of input value, it can be divided into a method of using an electrical signal, a method of not using an electrical signal, and a method of mixing the two signals
Summary
The photovoltaic (PV) system is receiving much attention because it is an infinite, eco-friendly energy source. The power of a PV system has a characteristic of changing nonlinearly with temperature and radiation, and there is a point where power is maximized under certain conditions. The P&O method is a method wherein voltage or current is perturbed and the power is observed to control the direction in which power increases, whereas the IncCond method tracks the maximum power point by comparing the instantaneous conductance and incremental conductance. Methods for improving the maximum power point tracking performance using artificial intelligence control techniques such as fuzzy control [35–44] and neural networks were studied [45–62]. This paper introduces various methods for the MPPT control of the PV system, which is receiving much attention as an alternative energy source and is preferred for constructing smart grids and Micro. The rest of this paper is organized as follows: Chapter 2 deals with solar cell modeling; Chapter 3 discusses various methods for MPPT control; Chapter 4 presents the conclusion
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