This paper presents a new structure of a multilevel inverter with fewer components, which is suitable for renewable energy sources and industrial loads applications. The structure has three unequal input sources and ten switches that can generate a 15-level output voltage. Furthermore, it can be connected in cascade for increasing, even more, the number of levels and output voltage. The main feature of the proposed inverter is its very low harmonic distortion at the output voltage and current due to the control method, which is based on the nearest level control method for generating a high-quality output voltage. A typical application of this inverter is in solar cells and wind turbines. Both simulations in Matlab/Simulink and experimental results in a scaled-down prototype validate the proposed theoretical analysis.INDEX TERMS Multilevel inverter, cascading converters, low harmonics, losses, distortion.
Multi-port DC-DC converters are a promising solution for a wide range of applications involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) converters have attracted the interest of researchers over the past two decades due to their potential advantages such as high power density, high transfer ratio, and galvanic isolation, for example, compared to other solutions. However, the coupled power flow nature of MAB converters makes their control implementation difficult, and due to the multi-input, multi-output (MIMO) structure of their control systems, a decoupling control strategy must be designed. Various control and topology-level strategies are proposed to mitigate the coupling effect. This paper discusses the operating principles, applications, methods for analyzing power flow, advanced modulation techniques, and small signal modelling of the MAB converter. Having explained the origin of cross-coupling, the existing power flow decoupling methods are reviewed, categorized, and compared in terms of effectiveness and implementation complexity.
In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.
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