以图像为基础的方法应用于三相感应电机的负载扭矩估计
Cleber Gustavo Dias1, Jhone Fontenele1
1Informatics and Knowledge Management Graduate Program, Nove de Julho University-UNINOVE, Sao Paulo 01525-000, Brazil.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种新方法,通过将空隙流量信号转换为AI分析图像来估计感应电机的负载扭矩. 这种方法可以准确地预测扭矩,而不需要电机参数,从而实现低误差率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 准确的负载扭矩估计对于控制三相感应电机至关重要.
- 传统方法通常需要详细的电机参数,限制了它们的适用性.
- 非侵入式传感技术对于实时运动诊断是可取的.
研究的目的:
- 开发一种新的,无参数的方法来估计感应电机的负载扭矩.
- 利用空隙流量测量和卷积神经网络 (CNN) 来进行扭矩预测.
- 在松鼠引诱机上实验验验证拟议的方法.
主要方法:
- 使用电机内的霍尔效应传感器测量空气间隙流量密度.
- 将时间域流量信号转换为灰度图像.
- 使用初始类型的CNN进行基于图像的负载扭矩估计.
- 在模型培训和验证中使用三个优化器.
主要成果:
- 拟议的方法成功估计了负载扭矩在广泛的操作范围 (1.5%到93.9%的额定负载).
- 四个模型配置的平均绝对百分比误差 (MAPE) 达到3.7%或更低.
- 具体的模型实现的MAPE低至3.0% (40x50像素),3.5% (40x25像素) 和3.3% (50x80像素).
- 实验验证是在一个7.5千瓦的松鼠引入机上进行的.
结论:
- 开发的方法提供了一个有效的,无参数的方法,用于感应电机的负载扭矩估计.
- 将流量信号转换为图像用于CNN分析是运动诊断的一个可行的策略.
- 该技术在各种负载条件下显示出高精度和广泛适用性.
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