This study aims to develop a soft-switching interleaved converter with a high-voltage conversion ratio using a single auxiliary switch. As a step-up voltage converter, it performs zero voltage switching and zero current switching operations on main switches by means of the manipulation of the single auxiliary switch. In this manner, both the switching loss and switching stress of main switches can be reduced, giving rise to an improvement in the conversion efficiency. Placed in parallel with a converter available, merely a single resonant branch is employed in the proposed soft-switching mechanism, a simple and elegant way to reach the goal. This proposal is further applied to a fuel cell system as a high performance DC voltage booster so as to drive a high-voltage DC load. PSIM simulations are conducted and subsequent experimental validation is presented at the end of this work.

Abstract:This study proposes an islanding detection method for photovoltaic power generation systems based on a cerebellar model articulation controller (CMAC) neural network. First, islanding phenomenon test data were used as training samples to train the CMAC neural network. Then, a photovoltaic power generation system was tested with the islanding phenomena. Because the CMAC neural network possesses association and induction abilities and characteristics that activate similar input signals in approximate memory during training process, the CMAC only requires that the weight values of the excited memory addresses be adjusted, thereby reducing the training time. Furthermore, quantification of the input signals enhanced the detection tolerance of the proposed method. Finally, the simulative and experimental data verified the feasibility of adopting the proposed detection method for islanding phenomena.

This study proposed an islanding detection method for a photovoltaic (PV) power generation system based on a cerebellar model articulation controller (CMAC) neural network. First, the islanding phenomenon test data were used as training samples to train the CMAC neural network. Then, the photovoltaic power generation system was tested with the islanding phenomena. The CMAC only requires the adjustment of the weighting values of the memory addresses to be activated. Therefore, it features a reduced training time. Furthermore, because of the quantification of the input signals, the detection tolerance of the proposed method was enhanced. Finally, the islanding detection test results proved the feasibility of the proposed detection method for islanding phenomena.