LIF技术与ELM算法的电力变压器故障诊断研究

电力变压器油的检测分析是电力变压器故障诊断的有效方法，快速识别电力变压器油的油样对电力变压器故障诊断工作有重大意义。常规的电力变压器油的检测技术主要是气相色谱法，此方法操作比较复杂，且不适合在线检测，不能及时发现变压器的故障隐患。提出一种激光诱导荧光光谱（LIF）技术与极限学习机（ELM）算法的电力变压器故障诊断研究的方法。实验采集四种油样，分别为热性故障油、电性故障油、局部受潮油以及原油。使用激光发生器激发油样而发射荧光，获取不同油样光谱数据，采用MSC、SNV预处理算法对光谱数据进行处理，防止噪声等因素干扰。随后，利用KPCA和PCA降维，主成分个数皆取5，KPCA处理后显示MSC预处理的累计贡献率最高，为99%，经MSC预处理的PCA模型累计贡献率依然达到95%以上，Original-KPCA与Original-PCA模型的累计贡献率均达到65%以下，可以发现，采用预处理的模型，累计贡献率均有上升。最后，分别对两种降维后的数据利用ELM进行回归拟合。实验表明，KPCA、PCA两种降维方式，KPCA算法表现性能较好，处理数据时间更短，提高了模型的可靠性和效率。同KPCA降维方式下，MSC-ELM模型的拟合优度R2为0.999 41，均方误差MSE为0.074%；SNV-ELM拟合优度R2为0.999 08，均方误差MSE为0.129%；Original-ELM拟合优度R2为0.996 95，均方误差MSE为0.399%；对比可以发现MSC比SNV处理后的效果更好，MSC-KPCA-ELM模型表现效果最佳，预测值与真实值更为接近，均方根误差最小。结果证明，MSC-KPCA-ELM模型结合激光诱导荧光光谱技术更加适用于对电力变压器是否发生故障的快速诊断，精确判断为哪种故障类型，保障电力设备的运行安全。

LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research

The detection and analysis of power transformer oil is an effective method for power transformer fault diagnosis, and it is of great significance to quickly identify the oil sample of power transformer oil for power transformer fault diagnosis. The detection technology of conventional power transformer oil is mainly gas chromatography, which is complicated and not suitable for on-line detection, and it can’t find the fault hidden danger of transformer in time. A method for transformer fault diagnosis of laser-induced fluorescence spectroscopy (LIF) technology and extreme learning machine (ELM) algorithm is proposed. Four oil samples were collected: thermal fault oil, electrical fault oil, local moisture-affected oil and crude oil. The spectral data of different oil samples are obtained by using a laser generator to emit fluorescence. The spectral data are processed by MSC and SNV preprocessing algorithms to prevent noise and other factors. Subsequently, the use of KPCA and PCA dimension reduction, the main components are taken 5, KPCA processing shows that MSC pretreatment of the cumulative contribution rate of the highest, 99%, MSC pre-processed PCA model cumulative contribution rate is still more than 95%, Original-KPCA and Origin-PCA model cumulative contribution rate of less than 65%, you can find that the use of pretreatment model, cumulative contribution rate has increased. Finally, the data after the two dimensions are regression fit by ELM. Experiments show that KPCA and PCA are two kinds of dimensional reduction methods. The KPCA algorithm performs best, the processing time is short, and the reliability and efficiency of the model are improved. In the same KPCA dimension reduction mode, the fitting excellence R2 of the MSC-ELM model is 0.999 41, the mean square error MSE is 0.074%, and the SNV-ELM fit is 0.999 08, the mean square error MSE is 0.129%, The Original-ELM fitting excellence R2 is 0.996 95, the mean square error MSE is 0.399%, and the comparison can be found that MSC is better than SNV after processing. The MSC-KPCA-ELM model performs best, the prediction value is closer to the real value. The mean square root error is the smallest. The results show that the MSC-KPCA-ELM model, combined with laser-induced fluorescence spectroscopy technology, is more suitable for the rapid diagnosis of whether or not the power transformer has failed, which type of fault is accurately determined, and the operation safety of power equipment is guaranteed.