Currently, a small-scale wind turbine can be connected to the Power Conditioning System (PCS) of the solar power system by simulating the technical characteristics of the solar panels to enhance the efficiency of the PCS on cloudy or rainy weather days. However, the working efficiency of the entire system has not been optimized because the use of traditional control techniques. P-I control has several problems of high starting overshoot, and slow response to sudden disturbances. Besides, P&O MPPT control has fluctuations in output power and slow response time at peak solar cell power due to weather effects. Therefore, this research proposes a novel control system including Artificial Neural Networks (ANN) MPPT control and digital slide mode control (DMSC) for the power conversion circuit to connect small-scale wind turbine power with the PCS of the solar power system in the parallel mode. The novel control solution in this study can minimize the disadvantages of PI control and P&O MPPT control. The study results showed that DSMC controller has better performance than the PI controller when controlling the PVCS in parallel mode with more 17.5 % power, no static error, 20 % less chattering of the power value of PVCS in the steady state, and 20 % less static error of power. Besides, the ANN MPPT controller also works better when changing solar radiation and has a harvested power of about 0.6 % higher than the power generated by the P&O MPPT controller.