High-Fidelity Real-Time Simulation of Power Electronics Converters via FPGA-Accelerated Dynamic Connectionist Neural Network

The precise modeling of power electronic switches is essential for accurate real-time simulation of power converter systems, particularly under high-frequency and dynamic operating conditions. Existing simulation techniques often rely on ideal switch models or simplified behavioral models, which cannot accurately capture transient behaviors like voltage and current spikes, electromagnetic interference (EMI), and switching losses. To address these limitations, this study proposes a novel connectionist neural network-based transient switch modeling approach that integrates time-node coupling mechanisms for enhanced dynamic behavior prediction. A high-quality dataset is generated through extensive offline simulations using a verified physicsbased IGBT model. The connectionist neural network model leverages feedback connections across time steps, significantly improving its ability to describe transient waveforms under various operating conditions. Additionally, a dynamic neuron adjustment strategy is introduced to reduce hardware resource usage by dynamically allocating neurons based on the complexity of different time nodes. This ensures high modeling accuracy while alleviating computational burden on FPGA platforms.