自组织映射与统计特征提取的混合方法用于准确高效的局部放电识别和聚类

Z. Bohari, M. Isa, P. Soh, A. Z. Abdullah, M. F. Sulaima, M. Nasir
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引用次数: 0

摘要

局部放电是影响电力变压器健康运行的现象。识别的延迟问题将恶化变压器的绝缘状况,最终降低电网的安全性和可靠性。本文提出了一种将峰顶统计特征与自组织方法相结合的混合方法来代替传统的局部放电识别聚类方法。总体而言,该方法以较快的计算时间(小于10秒)获得了较好的聚类结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Method of Self Organizing Maps with Statistical Feature Extraction for Accurate and Efficient Partial Discharge Recognition and Clustering
Partial discharge is the phenomena that affecting the health of power transformer. The problem with delay in identifying will deteriorate the transformer insulation condition and ultimately reduced the network security and reliability. In this paper, author proposed a hybrid method combining pinnacle statistical features with self organizing method for partial discharge recognition and clustering to replace the conventional way. Overall, the proposed method achieved decent clustering result with fast computation time (less than 10 seconds)
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