基于神经网络的双向DC-DC变换器输入电压自适应预测方法

R. İ. Kayaalp, T. Demirdelen, M. Tümay
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引用次数: 0

摘要

在工业应用中,近年来使用了双向DC-DC转换器(bdc)。采用不同的控制方法,提高了它们的效率。人工神经网络算法是近年来研究的控制新课题之一。本文试图改善双向dc-dc变换器的动态性能。提出了一种基于SOM的人工神经网络算法的自适应输入电压控制方案。首先,采用神经网络算法(SOM、SVM、FF)对自适应输入电压进行分类,并选择了最适合的神经网络算法SOM。该算法选择最优的效率情况。然后,对电压值进行预测。该电压值用于仿真模型。因此,所提出的系统既有效又高效。理论分析和PSCAD实际工业网络模型的仿真结果验证了所提出的双向DC-DC变换器(BDC)的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Input Voltage Prediction Method Based on ANN for Bidirectional DC-DC Converter
For industrial applications, Bidirectional DC-DC converters (BDCs) are used in recent years. And also their efficiency results are improved to apply different control methods. ANN algorithms is one of the new control topic in literature. This paper attempts to improve the dynamic performance of bidirectional dc-dc converter. And it deals with a novel control scheme related with an adaptive input voltage control by using ANN algorithms based on SOM. Firstly, adaptive input voltages are classified by ANN algorithms (SOM, SVM, FF) and the best suitable and proposed ANN algorithm is SOM which is selected for this controller. The best efficiency case is selected in this algorithm. Then, the voltage values are predicted. This voltage values are used in simulation models. Thus, the proposed system works both effective and high efficiency. Theoretical analysis and simulation results obtained from an actual industrial network model in PSCAD verify the viability and effectiveness of the proposed Bidirectional DC-DC Converter (BDC).
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