Solar Maximum Power Point Tracking Algorithm Based on Wavelet Neural Network

Abstract

With the development of human civilization, the shortage of resources and environmental pollution are increasing. Renewable energy is the only way to solve the problem of harmonious coexistence between human civilization and the environment. More and more of them have come into our lives to reduce the burden of the earth and benefit mankind. Nowadays, due to the increasing gap of traditional power supply, solar energy has become a mainstream renewable energy, with more and more applications, to provide us with green energy and improve our lives. The research on the application of solar energy systems is of great academic value. This paper mainly studies the solar maximum power point tracking (MPPT) algorithm based on wavelet neural network (WNN). This paper analyzes several common maximum power tracking control methods, describes the structure design of WNN maximum power tracking control algorithm, and describes the structure and working principle of photovoltaic cells. In this paper, the output power of MPPT mode is analyzed and compared by simulating the use of solar photovoltaic systems. In the meantime, the output voltage of the solar charging panel in the charging process is studied. The experimental results show that the output power of the power supply in MPPT mode is kept at 0.2, and the output power of the solar panel is improved at every moment with the change of the external environment. At 9 o'clock, the output voltage of the solar panel is 3.6, and at 11 o'clock, the output voltage is 4.11.