Hu Feng, Hu Jian, Dai Rongying, Guan Yuqi, Shen Xianfeng, Gao Bo, Wang Kun, Liu Yu, Yao Xiaokang
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Mar 5;288:122140. doi: 10.1016/j.saa.2022.122140. Epub 2022 Nov 23.
As the core component of the power system, the accurate analysis of its state and fault type is very important for the maintenance and repair of the transformer. The detection method represented by the transformer oil dissolved gas has the disadvantages of complicated processing steps and high operation requirements. Here, laser induced fluorescence (LIF) spectroscopy was applied for the analysis of transformer oil. Specifically, the slime mould algorithm (SMA) was used to select the characteristic wavelengths of the transformer oil fluorescence spectrum, and on this basis, a transformer fault diagnosis model was constructed. First, samples of transformer oil in different states were collected, and the fluorescence spectrum of the transformer oil was obtained with the help of the LIF acquisition system. Then, different spectral pretreatments were performed on the original fluorescence spectra, and it was found that the pretreatment effect of Savitzky-Golay smoothing (SG) was the best. Then, SMA was used to screen the characteristic wavelengths of the fluorescence spectrum, and 137 characteristic wavelengths were screened out to realize the accurate identification of the fluorescence spectrum of the transformer oil. In addition, the advantages of SMA for feature wavelength screening of transformer oil fluorescence spectra were demonstrated by comparing with traditional feature extraction strategies using principal components analysis (PCA). The research results show that it is effective to use SMA to screen the characteristic wavelengths of the LIF spectroscopy of transformer oil and use it for transformer fault diagnosis, which is of great significance for promoting the development of transformer fault diagnosis technology.
作为电力系统的核心部件，对其状态和故障类型进行准确分析对于变压器的维护和检修非常重要。以变压器油中溶解气体为代表的检测方法存在处理步骤复杂、操作要求高的缺点。在此，将激光诱导荧光（LIF）光谱技术应用于变压器油分析。具体而言，采用黏菌算法（SMA）选取变压器油荧光光谱的特征波长，并在此基础上构建变压器故障诊断模型。首先，采集不同状态下的变压器油样本，借助LIF采集系统获取变压器油的荧光光谱。然后，对原始荧光光谱进行不同的光谱预处理，发现Savitzky-Golay平滑（SG）预处理效果最佳。接着，利用SMA筛选荧光光谱的特征波长，筛选出137个特征波长，实现了对变压器油荧光光谱的准确识别。此外，通过与使用主成分分析（PCA）的传统特征提取策略进行比较，证明了SMA在变压器油荧光光谱特征波长筛选方面具有优势。研究结果表明，利用SMA筛选变压器油LIF光谱的特征波长并用于变压器故障诊断是有效的，这对推动变压器故障诊断技术的发展具有重要意义。
