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With the rapid development of China high-speed railway, the low frequency oscillation (LFO) of electrical quantities appears more often recently and leads to some severe problems of train operation. To improve the traction line-side converter control and suppress the phenomenon economically and effectively, a model-based predictive direct power control (MPDPC) approach is proposed in this paper. The approach adopts a discrete-time model of traction line-side converter in d–q reference frame to predict the future values of the input active and reactive power. The optimal switching state is selected by minimizing a cost function of power to evaluate the power errors at the next sampling time. Through the theoretical analysis and simulations, the performance of MPDPC is compared with traditional transient direct current control (TDCC) that is widely adopted in China Railway High-Speed 3 electric-multiple-unit. The real-time online simulations based on Real-Time Laboratory (RT-LAB) are also realized to further validate the results. Moreover, two vehicle-grid cascade simulation systems are constructed, and the LFO suppressing capability of MPDPC and TDCC is compared. Finally, the steady-state characteristic, dynamic characteristic, and LFO suppressing capability of MPDPC are demonstrated through the analysis of key performance indexes. [ABSTRACT FROM AUTHOR]

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising. [ABSTRACT FROM PUBLISHER]