基于深度学习的故障诊断通过多传感器识别数据用于风力轮发电机的初始间转短路 (ITSC)
Qinglong Wang1,2, Shihao Cui1,2, Entuo Li1,2
1Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
早期发现风力轮发电机故障至关重要. 这项研究引入了使用电流,振动和磁流传感器的深度学习框架,在识别间转短路 (ITSC) 故障方面达到99.25%的准确性.
科学领域:
- 可再生能源工程可再生能源工程
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 风力轮发电机很容易发生变压器绕的间转短路 (ITSC) 故障.
- 这些故障会导致功率波动,设备损坏和维护成本增加.
- 现有的状态监测方法缺乏在复杂的风电场运行中早期故障诊断的稳定性.
研究的目的:
- 开发和评估一种新的深度学习框架,用于诊断风力轮发电机中的七种不同的ITSC故障类型.
- 将深度学习模型的性能与用于故障检测的传统机器学习算法进行比较.
- 为了确定最可靠的传感器信号,以准确地进行ITSC故障诊断.
主要方法:
- 利用传统的机器学习算法和先进的深度网络架构的组合.
- 使用来自电流,振动和轴向磁流传感器的信号进行故障诊断.
- 使用包括混矩阵,准确性,回忆,平均精度 (AP) 和平均平均精度 (mAP) 在内的指标评估模型性能.
主要成果:
- 与机器学习算法相比,深度学习模型显示出更高的精度和稳定性.
- 在识别ITSC故障时,实现了99.25%的平均平均精度 (mAP).
- 三相电流信号被证明是发电机故障检测对振动和电磁数据的最可靠指标.
结论:
- 为风力轮机的条件监测和ITSC故障诊断提供了一个强大而高效的深度学习框架.
- 建议将三相电流传感器与深度学习相结合,以精确识别初始的ITSC故障.
- 该研究提供了一种实际方法,以提高风力轮机的可靠性,寿命和智能操作.
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