Name
Title | ||
|---|---|---|
Embedded Hardware Artificial Neural Network Control for Global and Real-Time Imbalance Current Suppression of Parallel Connected IGBTs |
Abstract | ||
|---|---|---|
A global and real-time control with embedded hardware artificial neural network (ANN) for imbalance current suppression of parallel connected insulated gate bipolar transistors (IGBTs) is first proposed in this paper. This method focuses on control strategy and control execution. The former one is realized by porting the ANN-based PID (ANN-PID) strategy in the control loop to yield the real-time and adaptive characteristics without IGBT quantity limitation. The latter one is realized by designing the IGBT gate quantity of charge regulator (GQR) to execute the command from ANN-PID controller. The evaluation of ANN-PID algorithm results 0.023% mean error in IGBT current control that reveals the feasibility of the proposed method. A full prototype with FPGA-based hardware accelerator for ANN-PID computing, including the designed GQR circuit, has been built for realization and qualification in a buck converter with parallel connected IGBTs. The experimental results show that the performance of the proposed method in imbalance current suppression is improved about 3.5–5.5 times as the load increase from low to high with the advantage of immunity to load change and the current imbalance can be suppressed within 4%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIE.2019.2905825 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Insulated gate bipolar transistors,Real-time systems,Logic gates,Sensors,Current measurement,Artificial neural networks,Gate drivers | Control theory,Logic gate,PID controller,Control theory,Field-programmable gate array,Electronic engineering,Insulated-gate bipolar transistor,Hardware acceleration,Control system,Engineering,Buck converter | Journal |
Volume | Issue | ISSN |
67 | 3 | 0278-0046 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
8 | ||