Transient Model and Real-time Simulation Method for Power Electronic Switches Based on Connectionist Neural Networks

WENG Haowen; WANG Can; PAN Xuewei

doi:10.7500/AEPS20250114004

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Transient Model and Real-time Simulation Method for Power Electronic Switches Based on Connectionist Neural Networks

Application Research | 更新时间：2025-09-25

- Transient Model and Real-time Simulation Method for Power Electronic Switches Based on Connectionist Neural Networks
- Automation of Electric Power Systems Vol. 49, Issue 17, Pages: 198-207(2025)
- 作者机构：
  
  哈尔滨工业大学（深圳）机器人与先进制造学院，广东省深圳市 518055
- 作者简介：
- 基金信息：
  
  Guangdong Provincial Basic and Applied Basic Research Foundation(2022A1515240078);Shenzhen Key Project of Basic Research(JCYJ20220818102416036)
- DOI：10.7500/AEPS20250114004
  CLC：
- Received：14 January 2025，
  
  Revised：2025-04-06，
  
  Published Online：20 June 2025，
  
  Published：10 September 2025
- 稿件说明：
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翁浩文,王灿,潘学伟.基于联结神经网络的电力电子开关暂态模型及实时仿真方法[J].电力系统自动化,2025,49(17):198-207.

WENG Haowen,WANG Can,PAN Xuewei.Transient Model and Real-time Simulation Method for Power Electronic Switches Based on Connectionist Neural Networks[J].Automation of Electric Power Systems,2025,49(17):198-207.
翁浩文,王灿,潘学伟.基于联结神经网络的电力电子开关暂态模型及实时仿真方法[J].电力系统自动化,2025,49(17):198-207. DOI： 10.7500/AEPS20250114004.

WENG Haowen,WANG Can,PAN Xuewei.Transient Model and Real-time Simulation Method for Power Electronic Switches Based on Connectionist Neural Networks[J].Automation of Electric Power Systems,2025,49(17):198-207. DOI： 10.7500/AEPS20250114004.

摘要

随着电力电子技术在新能源和电动汽车领域的广泛应用，其动态特性对系统性能的影响日益显著，传统基于理想开关模型的实时仿真技术难以满足高精度需求。为此，文中提出一种基于联结神经网络的开关暂态建模与仿真方法，结合系统级与暂态模型的协同建模策略，通过引入时间节点联结机制增强模型的时间序列预测能力。系统级采用伴随离散电路模型与节点分析法进行电路网络求解，暂态模型利用数据驱动方式构建联结神经网络模型，以精确描述开关器件暂态过程。在自主搭建的现场可编程门阵列(FPGA)硬件平台上实现了实时仿真系统，通过双向升降压变换器和三相逆变器的实时仿真算例进行验证，实验分别采用不同的开关器件。通过与离线仿真结果对比表明，该暂态模型和实时仿真方法在多拓扑和多工况下均具有较高的精度和稳定性。

Abstract

With the wide application of power electronic technologies in the fields of renewable energy and electric vehicles

their dynamic characteristics increasingly influence the system performance. Traditional real-time simulation technologies based on ideal switch models are difficult to meet high-precision demand. To this end

a switch transient modeling and simulation method based on connectionist neural networks is proposed. A co-modeling strategy combining both system-level and transient models is employed to enhance the time-series prediction capability of the model through the introduction of time-node coupling mechanism. At the system level

the circuit network is solved using the associated discrete circuit model and nodal analysis method

and the transient model utilizes a data-driven approach to construct a connectionist neural network model to accurately describe the transient process of the switching device. The real-time simulation system is implemented on a field programmable gate array (FPGA) hardware platform

and verified through real-time simulation cases of bidirectional buck-boost converters and three-phase inverters

in which various types of switching devices are used. The comparison with offline simulation results shows that the transient model and the real-time simulation method have high accuracy and stability under multi-topology and multi-case operating conditions.

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references

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