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This article presents a linear ϵ error insensitive support vector machine (ϵ-SVM) learned from the model predictive control (MPC) with very limited training samples. Unlike the existing learning-based MPC, which takes the same inputs as the MPC, the ϵ-SVM learns from historical data as well. Such structure is found to have both feedforward and feedback elements that improve performance. Besides, using the ϵ error-insensitive tube also increases the noise cancellation capability. The experiment is carried out on a three-phase inverter with an L-C filter and shows that the linear ϵ-SVM–MPC not only has better performance than the artificial neural networks (ANNs) MPC, but also outperforms the online MPC in both low THD values and small tracking errors regardless the load conditions. Moreover, the use of the linear kernel significantly reduces the time complexity to O(1), which is much less than O(2n) in either online MPC or ANN–MPC. Therefore, the linear ϵ-SVM–MPC has provided an effective way to implement the MPC and it is preferable for the DSP. This article is accompanied by a video demonstrating the real-time operation.

This paper proposes a new isolated multiport dc–dc converter for simultaneous power management of multiple renewable energy sources, which can be of different types and capacities. The proposed dc–dc converter only uses one controllable switch in each port to which a source is connected. Therefore, it has the advantages of simple topology and minimum number of power switches. A general topology of the proposed converter is first introduced. Its principle and operation are then analyzed. The proposed converter is applied for simultaneous maximum power point tracking (MPPT) control of a wind/solar hybrid generation system consisting of one wind turbine generator (WTG) and two different photovoltaic (PV) panels. The experimental results are provided to validate the effectiveness of using the proposed converter to achieve MPPT simultaneously for the WTG and both PV panels.