Photovoltaic power generation is one of the most widely used and mature technologies in new energy ships. By applying photovoltaic power generation technology to traditional ship power systems, the energy utilization rate can be improved and the current energy efficiency and emission problems can be solved. During the operation of the system, it is necessary to switch the operation mode of the inverter, including the islanded mode and the grid-connected mode. This paper takes the photovoltaic inverter parallel diesel generator power system as the research object, adopts a seamless switching control strategy based on model prediction, and realizes voltage control of the inverter in islanded mode and maximum power tracking in grid-connected mode. At the same time, it achieves a smooth transition between the two modes, with small impact on the load and the generator. Finally, the effectiveness of the control strategy is verified through simulation.

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.