基于知识减少的T型三级逆变器的开放电路故障诊断
Xiaojuan Chen1, Zhaohua Zhang1
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了一种智能故障诊断方法,用于T型三级逆变器,使用遗传算法和极端学习机器. 这种方法提高了准确性和通用性,以实现可靠的逆变器操作.
科学领域:
- 电气工程 电气工程
- 电力电子 电力电子 电力电子
- 人工智能的人工智能
背景情况:
- 对于功率转换至关重要的多层逆变器具有复杂的结构,有许多固态开关.
- 故障诊断对于确保这些逆变器的可靠和高效运行至关重要.
研究的目的:
- 开发一种智能故障诊断方法,用于T型三级逆变器的多管开放电路故障.
- 通过结合智能方法来提高故障诊断准确性和概括能力.
主要方法:
- 对故障特征的分析和逆变器输出功率和和元件的提取.
- 应用遗传算法-二进制颗粒矩阵知识减少方法来识别最小区分属性集.
- 使用缩小属性集构建极端学习机器子分类器.
主要成果:
- 与单个子分类器相比,在属性减少后,拟议的方法实现了更高的分类准确性.
- 证明了属性减少的优势和不同智能诊断技术的互补性.
- 混合智能诊断方法与现有方法相比,显示出更高的准确性和概括性.
结论:
- 开发的故障诊断方法为T型三级逆变器提供了一种新且有效的方法.
- 突出结合遗传算法,知识减少和极端学习机器的潜力,以实现先进的故障诊断.
- 为动力电子中的混合智能诊断系统提供了一个新的途径.
相关概念视频
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