基于SSI-MSVM的调相机轴承故障诊断方法

随着新能源的并网与特高压直流输电的发展,电网对无功调节的要求也逐步提高,因此大型调相机再次被投入使用。为了便于对调相机轴承进行故障诊断,提出了一种基于随机子空间识别(SSI)和多核支持向量机(MSVM)的故障诊断方法。在调相机轴承外侧表面不同的位置利用振动传感器采集振动信号,利用随机子空间模型进行特征提取,再根据高斯支持向量机和多核学习方法构造MSVM,然后将提取出的特征数据输入MSVM进行故障诊断。试验结果证明,基于SSI-MSVM的故障诊断方法能够适用于调相机轴承,且可以成功对故障进行辨识。

Fault Diagnosis Method of Synchronous Condenser Bearing Based on SSI-MSVM

With the development of new energy grid connection and ultra-high voltage (UHV) DC transmission, the requirement of reactive power regulation for power grid has gradually increased, so large synchronous condensers have been put into use again. A fault diagnosis method based on stochastic subspace identification (SSI) and multi-core support vector machine (MSVM) is proposed to facilitate the fault diagnosis of the synchronous condenser bearing. The vibration sensors are used to collect vibration signals at different positions on the outer surface of the synchronous condenser bearing, and the random subspace model is used for feature extraction. According to Gaussian support vector machine (SVM) and multi-core learning method, a multi-core SVM is constructed. Then, the extracted feature data are imported into the MSVM for fault diagnosis. The experimental results prove that the synchronous condenser bearing fault diagnosis method based on SSI-MSVM is suitable, and the fault can be successfully identified.