###### Abstract  
The main purpose of this paper is to conduct design and implementation on three-phase smart inverters of the grid-connected photovoltaic system, which contains maximum power point tracking (MPPT) and smart inverter with real power and reactive power regulation for the photovoltaic module arrays (PVMA). Firstly, the piecewise linear electrical circuit simulation (PLECS) power electronic real-time control system was applied to construct the simulation and actual test environment for the three-phase mains parallel photovoltaic system, where the KC200 GT photovoltaic module was used to form a 1600 W system for conducting the simulation. For enabling the PVMA to output the maximum power in terms of both insolation and ambient temperature, where the perturbation and observation (P&O) method was used for MPPT. Then, the voltage-power control technology was added to the grid-connected photovoltaic inverter. When the grid voltage p.u. value is between 1.0 and 1.03, the smart inverter starts voltage-power regulation, reducing the real power output to 1440 W, and absorbing the system's reactive power to 774 VAr. The power factor of the grid system end is controlled to 0.9 (lagging), and the grid voltage is reduced to nominal value 220 V. If the grid voltage p.u. value is between 0.97 and 1.0, the smart inverter starts voltage-power regulation, controlling the output real power to 1440 W and the reactive power to the system to 774 VAr, so that the power factor of the system end is controlled to 0.9 (leading), and the grid voltage is increased to normal value 220 V. Finally, the results from the simulation and actual test were used to demostrate the effectiveness of the regulation performance of the smart inverter. photovoltaic system; maximum power point tracking (MPPT); photovoltaic grid-connected smart inverter; voltage-power regulation +
Footnote †: journal: Applied Sciences  
also mitigate the power company's need to increase power generators due to only a few hours of daily supply in peak power consumption. At the same time, the CO\({}_{2}\) produced from coal burning can also be reduced. The advantages of a grid-connected photovoltaic system lay in the system simplicity, ease of maintenance, and the conversion efficiency of the photovoltaic inverter can reach 98%. The grid-connected photovoltaic inverter can convert the photovoltaic DC voltage output from the maximum power tracker into sine AC voltage and supply power to the mains grid. The magnitude, frequency, and phase of the output voltage are controlled to be consistent with the mains grid, where the power factor is controlled at 1. However, should a massive amount of the photovoltaic power generation system connect with the mains system in parallel, the characteristics of changing along with insolation and module temperature will cause tremendous variation in the mains voltage and reduce the power quality of the mains system. Therefore, the smart inverter needs to be utilized to control the real power and reactive power for steady voltage in the power supply. In order to enhance the control performance of smart inverters to improve the quality of grid power, many scholars have conducted numerous research [1; 2; 3] on the subject over recent years. If the real power unlimitedly feeds into the power grid, it may cause severe voltage fluctuation. Therefore, the literature [1] conducted research on control methods for grid-connected inverters, where the control strategy is based on a deep neural network (DNN). The voltage of the grid-connected point for the inverter was trained regularly, so the control performance of the smart inverter could be enhanced. Under such a method, since training samples were utilized to achieve the DNN control strategy; thus, the requirements of hardware performance were higher and caused an increase in cost. The literature [2] optimized the control methods of the photovoltaic smart inverters.