基于小波能量占比与时序预测的换流变压器机械状态智能诊断技术

Intelligent Diagnosis Technology of Mechanical State of Converter Transformer Based on Proportion of Wavelet Energy

换流变压器运行过程中其内部绕组承受交直流电压的共同作用,导致振源振动响应非常复杂。为关注换流变压器在长期振动作用下的机械状态变化,文中首先基于换流变压器绕组振动机理及其振动特性开发了振动在线监测系统,针对现场某换流变压器的振动信号进行了长期带电监测,并提取了不同时刻下振动信号的小波能量占比,最后以小波能量占比等振动特征组成的时间序列作为数据样本,结合时间序列预测模型提出了适用于现场换流变压器的机械状态智能诊断技术。分析结果表明,振动在线监测系统在现场能够对换流变进行长期带电监测,小波能量占比特征量可以在一定程度上反映换流变压器的运行规律和内部机械状态。通过时间序列预测模型对小波能量占比序列的处理,最终得到的预测值与期望值达到了较高的相似度,为现场换流变压器的状态评估和潜在缺陷诊断提供了更加及时、有效的技术途径。

The internal winding of converter transformer in operation is subjected to the combined action of AC and DC voltages,which leads to the complicated vibration response of vibration source.In order to pay attention to the mechanical state variation of converter transformer under long term vibration,an on-line monitoring system of vibration is firstly developed based on the vibration mechanism and characteristic of the winding of converter transformer in this paper.The long term live monitoring of the vibration signal of one converter transformer at site is performed,the proportion of wavelet energy of vibration signal of converter transformer at a converter station is extracted.At last,the mechanical state intelligent diagnosis technology which is suitable for converter transformer at site is proposed with the time series consisting of such vibration characteristic as the proportion of wavelet energy as the data sample and in combination with time series.The analysis results show that the the vibration on-line monitoring system at site can be able to perform long term monitoring of converter transformer and the characteristic of the proportion of wavelet energy can,to a certain extent,reflect the operation and internal mechanical mechanical state of converter transformer.It is concluded that the higher similarity between the predicted and expected value is reached through the treatment of the proportion of wavelet energy by the time series prediction model,which provides a more timely and effective technical way for state assessment and potential defect diagnosis of the converter transformer.