选择最优的特征来对PD信号进行分类,PD信号是由电机的多个绝缘缺陷产生的
Waqar Hassan1, G Amjad Hussain2, Abdul Wahid3
1School of Electrical & Electronics Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia. waqar.hassan@usm.my.
Scientific reports
|October 8, 2024
概括
本研究介绍了一种混合最大化相关性和最小化冗余性 (mRMR) 和随机森林 (RF) 方法,用于精确的部分放电 (PD) 缺陷分类在电机中. 该方法实现了99.875%的准确性,改善了绝缘诊断.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 部分放电 (PD) 是电气设备绝缘降解的关键指标,影响运行寿命.
- 准确的PD信号分类对于有效监测和诊断电机 (EM) 绝缘是必不可少的.
研究的目的:
- 开发和验证多重缺陷的EM中PD信号的混合特征选择和分类方法.
- 提高PD参数分析的准确性和可视化,以改善EM绝缘诊断.
主要方法:
- 建议采用混合方法,将最大限度的相关性和最小限度的冗余性 (mRMR) 结合起来,用于特征选择和随机森林 (RF) 进行分类.
- 在电磁绝缘中人工创建了四种类型的PD缺陷,并使用IEC-60,270标准获得了800个PD信号.
- 将mRMR技术与其他特征选择算法 (ReliefF,基尼指数,信息获取) 相比较,并使用RF,SVM和k-NN分类器进行验证.
主要成果:
- 混合的mRMR和RF方法实现了99.875%的分类准确度,用于EMS中的PD绝缘缺陷.
- 这种精度明显优于其他测试的特征选择和分类技术.
- 该研究表明,在缺陷严重程度评估中,选择的PD特征参数的有效性.
结论:
- mRMR和RF的组合在EMS中对PD信号的最佳特征选择和分类方面非常有效.
- 这种方法为电机绝缘监测和诊断提供了一个强大的解决方案,在更广泛的电力系统中具有潜在的应用.
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