基于机器学习的方法用于电阻估计和在感应电机中检测不平衡供应电压
Khaled Laadjal1, Acácio M R Amaral1,2, Mohamed Sahraoui1,3
1CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种可靠的机器学习方法,用于实时检测感应电机 (IM) 中的不平衡供应电压 (USV). 该方法精确估计电机阻抗,并使用相流和电压识别USV,提高工业安全和可靠性.
科学领域:
- 电气工程 电气工程
- 机器学习应用 机器学习应用
- 工业自动化 工业自动化
背景情况:
- 感应电机 (IM) 在工业环境中至关重要,但由于电源电压不平衡 (USV) 导致性能降低和故障.
- 实时检测和USV的严重程度评估对于防止故障,确保运行可靠性和保持工业设施安全至关重要.
- 摩托阻抗估计对于理解IM行为和诊断故障至关重要,因为异常表现为阻抗修改.
研究的目的:
- 开发一种可靠的在线方法,用于精确检测感应电机 (IM) 中的不平衡供应电压 (USV).
- 提出机器学习 (ML) 模型,用于估计IM定位器相阻抗和检测USV条件.
- 通过准确评估USV严重程度,提高工业操作的可靠性和安全性.
主要方法:
- 使用电压对称元件来计算负电压因子 (NVF) 作为USV检测的指标.
- 开发了两种机器学习 (ML) 模型:一种用于估计使用相流的IM定位器相阻,另一种用于使用相流和电压检测USV.
- 采用了回归决策树 (DTR) 模型和短时间最小方程 (STLSP) 技术的组合来创建数据集,特征工程和模型输入生成.
主要成果:
- 第一个ML模型仅使用相位电流成功估计了IM相位阻抗,从而消除了对额外传感器的需求.
- 第二个ML模型使用相流和电压准确估计了负电压因子 (NVF),从而实现了精确的USV检测.
- 综合STLSP和DTR方法在创建数据集和使ML模型能够估计相阻和NVF等物理量方面被证明是有效的.
结论:
- 拟议的机器学习方法提供了一种可靠和精确的方法,用于在线检测感应电机中的不平衡供应电压 (USV).
- 使用基于电流和电压数据的ML模型估计IM定位器相阻力和检测USV可以提高电机诊断和预测性维护.
- 开发的技术有助于提高可靠性,安全性和效率,可用于易受电压失衡的工业应用.
关键词:
决策树回归器 (DTR) 模型强迫转换 (FT) 是一种强迫转换.阻抗估计的阻抗估计.短时间最小方形Pronys方法 (STLSP)三阶段的IM是IM.不平衡的电源电压 (USV)负电压因子 (VNF) 是一个负电压因子.更多相关视频
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