基于时间转移多尺度增量和CatBoost的变压器振动故障诊断方法的研究
Haikun Shang1, Tao Huang1, Zhiming Wang1
1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
本研究引入了一种新的方法来诊断变压器振动故障,使用时移多尺度增量 (TSMIE) 和CatBoost. 该方法提高了识别变压器故障的准确性和稳定性.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 变压器机械振动分析对于安全运行至关重要.
- 现有的多尺度法在粗粒化过程中可能会丢失信息.
- 准确的故障诊断对于防止变压器故障至关重要.
研究的目的:
- 为变压器机械振动提出一种新的故障诊断方法.
- 为了提高变压器故障诊断的准确性和稳定性.
- 引入时移多尺度增量 (TSMIE) 作为一种有效的特征提取技术.
主要方法:
- 开发了时移多尺度增量 (TSMIE),以克服传统方法中的信息丢失.
- 从各种工作条件下的变压器振动信号中提取TSMIE特征.
- 利用CatBoost模型进行模式识别和故障分类.
主要成果:
- 拟议的TSMIE与CatBoost相结合显示出更高的诊断准确性和稳定性.
- 模拟和实验结果验证了新方法的有效性.
- 该方法比现有的变压器振动故障诊断技术有了显著的改进.
结论:
- 新型TSMIE功能提取与CatBoost相结合,为变压器振动故障诊断提供了有效的工具.
- 这种方法提高了诊断准确性和操作稳定性.
- 它代表了在确保功率变压器可靠运行方面取得的宝贵进展.
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