Machine Learning Methods for Feedforward Power Flow Control of Multi-Active-Bridge Converters
Machine Learning Methods for Feedforward Power Flow Control of Multi-Active-Bridge Converters
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DOI：
10.1109/tpel.2022.3215459
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发表时间：
2023-02
影响因子：
6.7
通讯作者：
Mian Liao;Haoran Li;Ping-Jian Wang;Tanuj Sen;Yenan Chen;Minjie Chen
中科院分区：
文献类型：
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作者：
Mian Liao;Haoran Li;Ping-Jian Wang;Tanuj Sen;Yenan Chen;Minjie Chen
控制多有源桥（MAB）变换器中的多路功率流对于实现高性能和复杂功能是重要的。MAB转换器控制的传统前馈方法依赖于精确的集总电路模型。本文提出了一种机器学习（ML）方法的前馈潮流控制的MAB转换器没有精确的电路模型。一个前馈神经网络的开发，以捕捉非线性特性和预测所需的阶段，以实现目标的潮流。神经网络是用大量的数据训练的，这些数据是用一组已知的相位角收集的。该训练好的网络用于预测相位，以实现目标潮流。构建并测试了一个六端口MAB转换器，以验证该方法并演示“机器学习在环”实现。迁移学习被证明可以有效地减少获得准确ML模型所需的训练数据的大小。基于ML的前馈潮流控制可以实现与传统的基于模型的方法相当的精度，并且可以在没有MAB转换器的精确集总电路元件模型的情况下起作用。
Controlling the multiway power flow in a multi-active-bridge (MAB) converter is important for achieving high performance and sophisticated functions. Traditional feedforward methods for MAB converter control rely on precise lumped circuit models. This article presents a machine learning (ML) method for the feedforward power flow control of an MAB converter without a precise circuit model. A feedforward neural network was developed to capture the nonlinear characteristics and predict the phases needed to achieve the targeted power flow. The neural network was trained with a large amount of data, collected with a set of known phase angles. This trained network was used to predict the phases to achieve the targeted power flow. A six-port MAB converter was built and tested to validate the methodology and demonstrate the “machine-learning-in-the-loop” implementation. Transfer learning was proven to be effective in reducing the size of the training data needed to obtain an accurate ML model. ML-based feedforward power flow control can achieve comparable accuracy as traditional model-based methods and can function without a precise lumped circuit element model of the MAB converter.