使用ANFIS的感应电机精确减速扭矩的新技术
Ahmed A Shaier1, Aymen Flah2,3,4,5, Habib Kraiem6
1Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
Scientific reports
|March 13, 2025
概括
这项研究引入了适应性神经模糊推理系统 (ANFIS),用于在异常条件下精确估计感应电机 (IM) 的额定扭矩,简化了工业应用的复杂计算.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 感应电机 (IM) 面临着诸如电压不平衡和波扭曲等操作异常.
- 这些问题需要扭矩降级以确保安全高效的运行,但传统方法复杂且耗时.
研究的目的:
- 引入自适应神经模糊推理系统 (ANFIS) 作为降级扭矩估计的预测工具.
- 量化降级因素 (DFs) 并分析它们对IM绩效的影响.
- 为扭矩降级评估提供简化,实时的解决方案.
主要方法:
- 使用MATLAB/Simulink进行性能分析和降级因子的模拟.
- 开发并实施了一个集神经网络和模糊逻辑的ANFIS控制器.
- 在线训练ANFIS控制器,并与Simulink结果对其预测进行验证.
主要成果:
- 安菲斯证明了可靠和准确的降级扭矩预测,与模拟结果的偏差最小.
- 识别了电压失衡和波扭曲作为减少扭矩的主要原因.
- 展示了温度上升作为加剧功率损失和热应力的一个因素.
结论:
- 在异常条件下,ANFIS提供了一种简化,快速和精确的方法来评估IM扭矩降级.
- 该ANFIS控制器优化了电机效率,并延长了运行寿命.
- 这项研究为工业环境中降级扭矩评估和管理提供了实际框架.
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