Research On Improved Tracking Strategy Of Photovoltaic Maximum Power Point Considering Influence Of Module Temperature

Solar energy is an important component of the new energy, large-scale use of solar energy has a huge promoting effective in alleviating the shortage of fossil energy and improving the environment. The solar panels are the core part of the photoelectric conversion, plays an important role in the conversion of energy. However, the output of the solar cell has a linear characteristic, output will be influenced by the external environment. To maximize gain more power, maximum power point tracking is necessary to guarantee the cell always output maximum power.This paper first analyzes output characteristics of solar cell, the results presents that the technical parameters of solar cell will be quite different under different light and temperature circumstance. Meanwhile the cell’s output characteristic curve is not only influenced by the light, but also influenced by temperature, it presents different peak and range under different temperatures. Similar to the situation of complex illumination, the output characteristics of the solar cell appears multiple peaks under complex temperature. And the peak’s number and distribution will be varied by the numbers and arrangement of solar cells.Then the paper proposes a method of calculating the module temperature with fit function. To solve the problem of cells’ environmental factors are different, this paper proposed by using solar panels’ output power to reflect the difference of external environment. The paper uses measured data to determine the function between power difference and the various environmental factors. Thus the environmental factors can be obtained by using solar panels power difference. This makes the data more accurate. Next, the neural network’s nonlinear approximation is used to strengthen the method’s weak applicability problem. The neural network’s output is set as function coefficients. The results will obtain optimal under different circumstance when fitting function uses those coefficients to calculate temperature.On the basis of obtaining accurate light and module temperature, this paper studies maximum power point tracking strategy. The paper first analyzes the output characteristic curve of peak distribution when photovoltaic cells in the event of partial shading. Then points out that in order to find the real maximum power point it must ensure that the operating voltage can skip local minima and enter into a single peak region centered the global optimum. Therefore, an improved tracking strategy is proposed. First, the neural network based on the PV cell’s light and temperature to obtain output voltage. Then the incremental conductance method is used for accurately adjusted so that the output power is more accurate.