Design methodology of power electronics converters is critical to fully explore the potential of wide-bandgap power semiconductors at the converter level. However, existing design methods largely rely on complex mathematical models which significantly increases the computational time, complexity and further leads to problems including poor constraint handling capabilities, inaccurate design, difficult parameter tuning and inadequate problem dimension. These all could generate sub-optimal designs that make the whole design process meaningless. To overcome the aforementioned problems, in this paper, an artificial neural network (ANN)-based multi-objective design approach is proposed, which offers significant advantages in reducing the repetitive usage of complex mathematical models and hence the computational time of design. The computational time was reduced by up to around 78% and 67% compared to the numerical modeling and geometric program (GP) methods as validated through a hardware design process. The proposed method was implemented in MATLAB/Simulink to design a 1 kW single-phase inverter, resulting in a design with an optimized efficiency (98.4%) and power density (4.57kW/dm3)\mathbf {(\mathrm{\text{4.57}\,kW/dm^{3}})}. The accuracy of the design is verified through experimental prototyping and the measured efficiency and power density are 98.02% and 4.54kW/dm3\mathrm{\text{4.54}\,kW/dm^{3}}, respectively, so the errors of efficiency and power density are both less than 1%.

中文翻译：

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基于人工神经网络的宽带隙电力电子转换器多目标设计方法


电力电子转换器的设计方法对于充分探索宽带隙功率半导体在转换器级的潜力至关重要。然而，现有的设计方法很大程度上依赖于复杂的数学模型，这大大增加了计算时间和复杂性，并进一步导致约束处理能力差、设计不准确、参数调整困难和问题维度不足等问题。这些都可能产生次优设计，使整个设计过程变得毫无意义。为了克服上述问题，本文提出了一种基于人工神经网络（ANN）的多目标设计方法，该方法在减少复杂数学模型的重复使用以及设计的计算时间方面具有显着的优势。通过硬件设计过程验证，与数值建模和几何程序 (GP) 方法相比，计算时间减少了约 78% 和 67%。所提出的方法在 MATLAB/Simulink 中实现，用于设计 1 kW 单相逆变器，从而获得具有优化效率 (98.4%) 和功率密度 (4.57kW/dm3)\mathbf {(\mathrm{\text{ 4.57}\,kW/dm^{3}})}。通过实验原型验证了设计的准确性，实测效率和功率密度分别为98.02%和4.54kW/dm3\mathrm{\text{4.54}\,kW/dm^{3}}，因此误差为效率和功率密度均小于1%。