Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

被引：10
作者：
Upadhyaya S. [1 ]
Mohanty S. [1 ]
Bhende C.N. [2 ]
机构：
[1] Department of EE, NIT Rourkela, Rourkela
[2] School of Electrical Science, IIT Bhubaneswar, Bhubaneswar
来源：
Journal of Control, Automation and Electrical Systems | 2015年 / 26卷 / 5期
关键词：
Classification accuracy (CA); Maximal overlap discrete wavelet transform (MODWT); Power quality (PQ); Power quality disturbances (PQD); Random forest (RF); Second-generation wavelet transform (SGWT);
D O I：
10.1007/s40313-015-0204-4
中图分类号：
学科分类号：
摘要：
In this paper, recently developed variants of wavelet transform, namely the maximum overlapping discrete wavelet transform and the second-generation wavelet transform, are used for detection of ten types of the power quality (PQ) disturbance signals. Further, the features of PQ signal disturbances are extracted using these wavelet transforms. Those extracted features are then used to classify various PQ disturbances. Random forest (RF) classifier is presented in this paper. The RF is constructed with multiple trees for classification of large number of classes simultaneously. In order to represent realistic situation, the proposed technique is tested with noisy data. © 2015, Brazilian Society for Automatics--SBA.
引用
收藏
页码：556 / 566
页数：10
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