Liu Deliang, Lu Biao, Wu Wenping, Zhou Wei, Liu Wansu, Sun Yiye, Wu Shilong, Shi Guolong, Yuan Leiming
School of Information and Engineering, Suzhou University, Suzhou 234000, China.
College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China.
Sensors (Basel). 2024 Nov 24;24(23):7485. doi: 10.3390/s24237485.
Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky-Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters ( and ). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems.
准确评估变压器油纸绝缘的老化状态对于确保电力系统的安全可靠运行至关重要。本研究开展了室内加速热老化实验，以模拟变压器内油浸式油纸绝缘的老化过程。获得了一系列反映不同老化状态的样品，并将其分为六个不同的组。采用拉曼光谱分析技术来表征油浸式油纸绝缘不同老化状态的信息。对原始拉曼光谱进行处理，使用非对称加权惩罚最小二乘法校正基线漂移，采用Savitzky-Golay（S-G）平滑法消除波动噪声，并通过主成分分析（PCA）提取主成分以降低数据维度。开发了一种支持向量机（SVM）分类器来区分拉曼光谱和类别标签。使用网格搜索、粒子群优化（PSO）和遗传算法（GA）对SVM参数进行优化，得到最优参数（C和gamma）。值得注意的是，网格搜索方法在识别SVM参数的最佳组合方面表现出很高的效率。对SVM分类器中不同数量主成分的比较分析表明，纳入PCA特征的最优子集可使外部验证样本的分类准确率达到最高的94.44%，仅有八个样本被误分类到相邻类别。本研究为有效评估变压器油纸绝缘的老化状态提供了技术支持和理论基础，有助于推动电力系统状态监测和维护策略的发展。
