通过使用基于ANN的自适应开关频率优化,延长系列直流电机驱动器中功率元件的寿命
Erkan Eren1, Hakan Kaya1, Salih Baris Ozturk2
1Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak 67100, Türkiye.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了用于电动采矿机车的人工智能 (AI) 控制,优化开关频率以减少组件磨损和延长设备寿命. 与传统方法相比,这种AI方法显著提高了系统的耐用性和效率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力电子 电力电子 电力电子
背景情况:
- 电动引系统由于电机电流波动和温度波动而面临部件退化.
- 在直流 (DC) 电机驱动器中,固定开关频率在电流和组件损失之间进行了权衡.
- 延长关键组件的寿命,如绝缘门双极晶体管 (IGBT) 和电容器,对于电池供电系统至关重要.
研究的目的:
- 为系列直流电机驱动器开发基于人工神经网络 (ANN) 的自适应开关频率控制策略.
- 为了最大限度地减少电动引力系统中的电动电流波动和绝缘门双极晶体管 (IGBT) 热波动.
- 提高电池驱动的采矿机车中功率电子元件的运行寿命.
主要方法:
- 使用来自电流传感器,温度传感器和电位计的数据来训练一个人工神经网络 (ANN).
- 为实时决策开发了一个自适应的可变开关频率控制算法.
- 通过比较ANN驱动的方法与固定频控制来评估性能.
主要成果:
- 由ANN驱动的控制器保持了平均电机电流波动率低于5%和最大波动率低于10%.
- 在IGBT和DC总线电容器的寿命中观察到显著的改进.
- 公平性指数表明,与固定频率运行相比,整体系统性能提升高达13倍.
结论:
- 嵌入式机器学习和自适应控制的整合大大提高了电力电子系统的耐用性和效率.
- 拟议的基于ANN的战略有效地管理切换频率,以减轻电动引中的降解因素.
- 这种自适应控制方法为提高工业嵌入式系统可靠性的可行解决方案.
关键词:
适应性控制算法适应性控制算法人工神经网络 (ANN) 是一个人工神经网络.电池驱动的机车,是电池驱动的机车.嵌入式系统 嵌入式系统隔热门双极晶体管 (IGBT) 隔热门双极晶体管机器学习是机器学习.采矿 采矿 采矿 采矿 采矿 采矿实时决策 实时决策基于传感器的信号处理.系列直流电机直流电机变量开关频率的变量开关频率更多相关视频
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