To enhance the power conversion efficiency of photovoltaic (PV) generation systems, this paper presents a distributed grid-connected PV system integrated with maximum power point tracking (MPPT). The system design involves the selection of PV modules, the construction of distributed PV cell and battery models, and the design of the inverter. On this basis, the maximum power point (MPP) of the distributed grid-connected PV system is determined using an MPPT algorithm. A controller is implemented to regulate the DC voltage, and an MPPT model based on a radial basis function (RBF) neural network is established to capture the nonlinear relationship between input parameters – such as ambient temperature and irradiance – and the maximum power point. Under non-uniform irradiation conditions, the ant colony algorithm is employed for global optimization, and an iterative control mechanism is used to enhance the overall power generation performance, thereby optimizing PV power conversion efficiency and accomplishing the design of the MPPT function. Experimental results demonstrate that the proposed system can generate 90 W of power, accurately track the MPP of the PV grid-connected system under varying irradiance conditions, and achieve a nearly 100% fit between the estimated and actual MPP values across different time points. The maximum power point tracking error is controlled within 2.5%, indicating high tracking accuracy and improved power generation capability of the distributed grid-connected photovoltaic system.
Yang et al. (2026) studied this question.