本文在气体绝缘组合电器(gas insulated switchgear, GIS)实体模型中分别放置了针-板、悬浮金属颗粒和绝缘子表面固定金属颗粒放电模型,用超声波传感器采集到其放电波形。对放电波形提取的特征向量进行局部线性嵌入(local linear embedding, LLE)算法降维处理,用降维后的向量作为输入对BP_Adaboost分类器进行训练和测试类型识别。识别结果表明,用这样方法进行GIS绝缘缺陷类型识别可以在减少计算量的同时保持较高的识别率,说明了其在局部放电模式识别应用中的有效性。
英文摘要:
Needle-plate, suspended metal particles and metal particles fixing on insulating surface discharge models were placed in gas insulated switchgear(GIS) entity model. The corresponding discharge waveforms were detected by ultrasonic sensor. The dimension of feature vectors extracted from discharge waveforms were reduced by local linear embedding(LLE) algorithm. The processed vectors were used as input to train and test BP_Adaboost classifier. Recognition results show that, GIS insulating defects recognition with this method can reduce the calculation and maintain a high recognition rate at the same time. This shows its effectiveness in the application of partial discharge pattern recognition.
