| 引用本文: | 李正明,钱露先,李加彬.基于统计特征与概率神经网络的变压器局部放电类型识别[J].电力系统保护与控制,2018,46(13):55-60. |
| LI Zhengming,QIAN Luxian,LI Jiabin.Type recognition of partial discharge in power transformer based on statistical characteristics and PNN[J].Power System Protection and Control,2018,46(13):55-60 |
|
| 摘要: |
| 针对变压器局部放电类型识别问题，提出了基于统计特征参数与概率神经网络的局部放电模式分类方法。所提方法首先在局部放电类型三维谱图中构建二维分布图谱，然后在二维分布谱图上提取统计特征参数，接着将统计特征参数以特征向量的形式作为概率神经网络的输入量，最后利用概率神经网络对放电类型进行识别。在试验中，利用电晕放电、沿面放电、气隙放电三种放电类型的数据，将所提分类方法与典型局部放电类型分类方法进行比较。实验结果表明，所提分类方法的识别准确率较高、识别时间开销较少。 |
| 关键词: 变压器 局部放电 类型识别 统计特征参数 概率神经网络 |
| DOI：10.7667/PSPC170962 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(51477070) |
|
| Type recognition of partial discharge in power transformer based on statistical characteristics and PNN |
|
LI Zhengming,QIAN Luxian,LI Jiabin
|
|
(School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)
|
| Abstract: |
| A partial discharge pattern recognition algorithm based on statistical characteristics parameters and probability neural network is designed for the transformers. Two-dimensional diagrams are constructed from the phase resolved partial discharge. Then, the statistical characteristics parameters are extracted from the two-dimensional diagrams. The statistical characteristics parameters are regarded as the input of probability neural network. The proposed algorithm is compared to the typical algorithms of partial discharge type recognition on corona discharge, surface discharge and air-gap discharge in experiment. It is indicated by the experimental results that the accuracy recognition rate of the proposed algorithm is higher, and the time cost is smaller. This work is supported by National Natural Science Foundation of China (No. 51477070). |
| Key words: transformer partial discharge type recognition statistical characteristics parameters probability neural network |
