| 引用本文: | 高佳程,曹雁庆,朱永利,贾亚飞.基于KELM-VPMCD方法的未知局部放电类型的模式识别[J].电力自动化设备,2018,(5): |
| GAO Jiacheng,CAO Yanqing,ZHU Yongli,JIA Yafei.Pattern recognition of unknown PD types based on KELM-VPMCD[J].Electric Power Automation Equipment,2018,(5): |
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| 摘要: |
| 为了解决局部放电类型未知的样本无法被正确识别的问题，提出了一种基于核极限学习机变量预测模型(KELM-VPMCD)的未知局部放电类型的识别方法。通过KELM对已知局部放电类型的训练样本进行训练，然后对各局部放电类型已知的样本建立相应的变量预测模型。利用这些模型对测试样本进行回归预测。根据各样本的预测误差平方和，利用Otsu算法设置误差阈值，通过阈值识别各样本的局部放电类型。识别结果表明，所提方法对于未知的局部放电类型具有较高的正确识别率。 |
| 关键词: 局部放电 模式识别 核极限学习机 变量预测模型 |
| DOI：10.16081/j.issn.1006-6047.2018.05.021 |
| 分类号:TM835 |
| 基金项目:国家自然科学基金资助项目(51677072) |
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| Pattern recognition of unknown PD types based on KELM-VPMCD |
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GAO Jiacheng1, CAO Yanqing2, ZHU Yongli1, JIA Yafei1
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1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China;2.GD Power Development Co.,Ltd.,Beijing 100101, China
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| Abstract: |
| In order to solve the problem that the unknown PD(Partial Discharge) types cannot be recognized correctly, a method based on KELM-VPMCD(Kernel Extreme Learning Machine-Variable Predictive Model based Class Discriminate) is proposed to recognize the unknown PD types. The samples with the known PD types are trained by the KELM, and the corresponding VPMs(Variable Predictive Models) are constructed and used for the regression prediction of testing samples. According to the quadratic sum of regression prediction errors, the thresholds are set by Otsu algorithm to recognize the PD types of samples. The recognition results show that the proposed method can re-cognize the unknown PD types with high accuracy. |
| Key words: partial discharge pattern recognition KELM VPMCD |
