Quantum mechanics

Control (management)

Feedforward neural network

Voltage

Engineering

Control engineering

Bridge (graph theory)

Power (physics)

Half bridge

Computer science

Capacitor

Physics

AC power

Power flow

Electric power system

Power control

Feed forward

Electronic engineering

Internal medicine

Artificial intelligence

Medicine

Control theory (sociology)

Artificial neural network

Power electronics

Converters

Electrical engineering

IEEE Transactions on Power Electronics

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.

Machine Learning Methods for Feedforward Power Flow Control of Multi-Active-Bridge Converters