A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges
A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges
复制标题
基于人工神经网络的水轮发电机定子绕组局部放电自动分类系统
DOI：
10.1590/2179-10742017v16i3854
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发表时间：
2017
影响因子：
--
通讯作者：
F. S. Brasil
中科院分区：
文献类型：
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作者：
R. D. Oliveira;Ramon C. F. Araújo;F. Barros;Adriano Paranhos Segundo;R. Zampolo;Wellington Fonseca;V. Dmitriev;F. S. Brasil
局部放电监测被广泛应用于旋转电机的定子绕组绝缘状况评估，但其大规模应用需要开发能够自动处理这些测量数据的智能系统。本文提出了一种基于神经网络的水轮发电机定子绕组局部放电自动分类方法。该数据库是由水轮发电机在实际设置在线PD测量形成的。对这些数据应用了噪声滤波技术。然后，基于图像投影的概念，从滤波后的样本中提取新的特征。这些特征被用作训练几个神经网络的输入。使用统计程序获得的最佳性能网络的识别率为98%。
Partial discharge (PD) monitoring is widely used in rotating machines to evaluate the condition of stator winding insulation, but its practice on a large scale requires the development of intelligent systems that automatically process these measurement data. In this paper, it is proposed a methodology of automatic PD classification in hydro-generator stator windings using neural networks. The database is formed from online PD measurements in hydro-generators in a real setting. Noise filtering techniques are applied to these data. Then, based on the concept of image projection, novel features are extracted from the filtered samples. These features are used as inputs for training several neural networks. The best performance network, obtained using statistical procedures, presents a recognition rate of 98%.