Modeling and Simulation of an Intelligent Photovoltaic Controller Based on Variable Step Algorithm of Versoria

الملخص EN

As a green and renewable energy source, photovoltaic power is of great significance for the sustainable development of energy and has been increasingly exploited.

The photovoltaic controller is the key component of a photovoltaic power generation system, and its central technology is the maximum power point tracking technology.

In this paper, a mathematical model of photovoltaic cells is firstly established, the output characteristics of photovoltaic cells are analyzed, the main factors that affect the output efficiency of photovoltaic cells are obtained, and it is proved that the most important factor that affects the output power is the light intensity.

Therefore, in the design, the maximum power point of the photovoltaic cell is tracked by the control algorithm and can maximize the use of photovoltaic output power fast charging.

The key to the design of a photovoltaic controller is the design of control algorithm.

So, an improved fuzzy control algorithm is proposed to overcome the shortcomings of the traditional maximum power point tracking (MPPT) algorithm.

The algorithm can consider tracking both speed and convergence, but the algorithm requires high input and output fuzzy domain parameters, and although the tracking speed is fast, the stability of convergence is poor.

For the limitation of fuzzy control algorithm, considering the property of the Versoria function, an MPPT design method for an intelligent controller based on the Versoria variable step algorithm is further proposed.

According to the output characteristics of photovoltaic cells, three parameters, α, β, and γ, are set to solve the tracking speed and tracking stability.

In order to reduce the static error, a genetic factor is proposed to sum up the historical error to effectively improve the tracking stability.

The simulation results show that the algorithm can track the maximum power point quickly and has good tracking speed and stability.

This algorithm can be used in engineering practice effectively.