基于在可变负载条件下持续训练LSTM的转换器电容器温度估计
Xiaoteng Dai1, Yiqiang Chen2, Jie Chen2
1School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种使用长短期内存 (LSTM) 算法进行精确电容器温度估计的新方法,用于功率转换器. 该技术适应不断变化的负载,提高系统可靠性.
科学领域:
- 电力电子 电力电子 电力电子
- 人工智能的人工智能
- 状态监控 状态监控
背景情况:
- 电容器在功率电子转换器中至关重要,但容易因操作压力而损坏.
- 精确的电容器温度监测对于确保功率转换器可靠性和防止故障至关重要.
研究的目的:
- 开发一种用于精确估计电容芯温度的新方法.
- 为了提高温度估计算法在变量负载条件下的适应性.
主要方法:
- 使用长短期记忆 (LSTM) 神经网络算法进行温度预测.
- 实施持续培训机制,使LSTM模型适应动态负载变化.
主要成果:
- 提出的基于LSTM的方法在估计电容器核心温度方面实现了高精度.
- 持续培训机制在适应可变负载条件方面表现出强性.
结论:
- 新的LSTM方法提供了一个可靠的解决方案,用于实时监测功率电子转换器中的电容器温度.
- 这种方法有助于改进功率转换器诊断和运行安全.
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