| 相对变换主元分析的变压器油击穿电压预测 |
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| 引用本文： | 唐勇波,彭 涛,熊印国,江风云.相对变换主元分析的变压器油击穿电压预测[J].仪器仪表学报,2015,36(7):1640-1645. |
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| 作者姓名： | 唐勇波 彭 涛 熊印国 江风云 |
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| 作者单位： | 1.宜春学院物理科学与工程技术学院宜春336000；2.中南大学信息科学与工程学院长沙410083 |
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| 基金项目： | 国家自然科学基金（61273169）项目资助 |
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| 摘 要： | 针对变压器油击穿电压在线测量困难,数据噪声和孤立点影响支持向量机预测精度的问题,提出了基于相对变换(RT)主元分析(PCA)的变压器油击穿电压预测方法。首先,通过相对变换将原始数据空间变换到相对空间,抑制噪声和孤立点对模型精度的影响;然后在相对空间进行主元分析,降低相对空间维数,使提取的主元特征更具有代表性和更大的变化度;最后,将提取的主元作为最小二乘支持向量机(LSSVM)的输入,建立变压器油击穿电压的最小二乘支持向量机预测模型。与LSSVM、RT-LSSVM和PCA-LSSVM的对比实验结果表明,本文提出的方法具有较优的预测精度和泛化能力。
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| 关 键 词： | 击穿电压 预测 相对变换 主元分析 最小二乘支持向量机 |
Breakdown voltage prediction mathod for transformer oil based on relative
transformation principal component analysis |
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| Tang Yongbo,Peng Tao,Xiong Yinguo,Jiang Fengyun.Breakdown voltage prediction mathod for transformer oil based on relative
transformation principal component analysis[J].Chinese Journal of Scientific Instrument,2015,36(7):1640-1645. |
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| Authors: | Tang Yongbo Peng Tao Xiong Yinguo Jiang Fengyun |
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| Affiliation: | 1.School of Physical Science and Engineering, Yichun University, Yichun 336000, China;2.School of
Information Science and Engineering, Central South University, Changsha 410083, China |
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| Abstract: | Aiming at the problems that it is difficult to measure the breakdown voltage of transformer oil in real time and the data noise and isolated point affect the prediction precision of support vector machine, a new prediction method for the breakdown voltage of transformer oil based on relative transformation (RT) principal component analysis (PCA) is proposed. Firstly, the original data space is converted into the relative data space with relative transformation, which suppresses the influence of data noise and isolated point on prediction model precision; Secondly, PCA is performed in relative space to reduce the dimension of the relative space, which makes the extracted principal component features have more variability and representation in the relative space; At last, the extracted principal components are used as the input of the least squares support vector machine (LSSVM), and the LSSVM prediction model for the breakdown voltage of transformer oil is established. Compared with LSSVM, RT LSSVM and PCA LSSVM, the experiment results show that the proposed prediction method has better prediction precision and generalization ability. |
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| Keywords: | breakdown voltage prediction relative transformation principal component analysis least squares support vector machine |
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