Abstract: Abstract: Solar photovoltaic technology has been widely used in modern agriculture. Due to the volatility of solar power, it is hard to maximize the use of solar energy. In order to seek a way to improve the conversion rate of photovoltaic solar panels, this paper developed a new algorithm to utilize solar energy more efficiently. Since tracking solar maximum power point is a valid method to maintain the solar panel power output at a high level, at this paper, we choose ripple correlation control (RCC) to keep tracking the maximum power point of a solar photovoltaic (PV) system. Ripple correlation control is a real-time optimal method particularly suitable for power convertor control. The objective of RCC in solar PV system is to maximize the energy quantity. This paper extended the traditional analog RCC technique to the digital domain. With discretization and simpli?cations of math model, the RCC method can be transformed to a sampling problem. The control method shows that when the solar PV system reaches the maximum power point, power outputs at both maximum and minimum state should be nearly the same. Moreover, since voltage output of a system is easy to observe and directly related to power output, it is ideally appropriate for sampling and analysis. Setting the output voltage as status variable, the discrete-time RCC (DRCC) algorithm can track the optimal operating point quickly via sampling at maximum and minimum voltage moments. A DRCC Simulink model of the maximum power point tracking (MPPT) system was built in the paper. The model consists of three parts: solar PV panel module, DC-DC convertor and control module. In the control module, ripple sampler is built with trigger subsystem to get output information (voltage and current). Controller is implemented with S-function. After S-function adopts the voltage and current information, it will calculate the power difference and output duty ratio signal. The output of the controller is transformed to PWM wave to adjust the system power output. Voltage of solar PV panel is controlled by duty ratio via DC-DC convertor. When the system works at non-maximal power point, difference of power outputs at two sample points can refresh the duty ratio to make the voltage change, and finally take effects on the power output. The proposed algorithm was realized and testified in Simulink system. In the simulation, voltage of solar PV system at maximum power point was set to 17V and maximum power output is set to 25.7W. In an environment of 1000 W/cm2 and 25℃, output of the whole system finally reached a stable state of 17V and 24.8W. Power tracking accuracy was up to 96%. Under the same condition, we used mountain climbing tracking technique to run the simulation. The system power output came to 23.9W in the end, which achieved an accuracy of 93%. Another simulation was conducted by changing the environment parameter to 200 W/cm2, 25℃. The control model can also track the maximum power point. In the dynamic light intensity test which light intensity varied from 1000W/cm2 to 200W/cm2 at 0.2s during simulation, the system was able to track new maximum power point within 0.1s. The results indicated that the algorithm is capable for fast MPPT under the conditions of 1000W/cm2 and 200W/cm2, 25℃.