Optimal Wavelet Based Feature Extraction and Classification of Power Quality Disturbances Using Random Forest

被引：0
|
作者：
Markovska, Marija [1 ]
Taskovski, Dimitar [1 ]
机构：
[1] Ss Cyril & Methodius Univ Skopje, Fac Elect Engn & Informat Technol, Skopje, North Macedonia
来源：
17TH IEEE INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES - IEEE EUROCON 2017 CONFERENCE PROCEEDINGS | 2017年
关键词：
Power quality; disturbances; wavelets; random forest; classification; feature extraction; TRANSFORM; FOURIER; SYSTEM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The increasing number of polluting loads requires higher power quality (PQ) in the generation, transmission and distribution systems. In order to improve the power quality, the power disturbances should be monitored continuously. Power quality monitoring and analysis must be able to detect and classify the disturbances on the electrical system. A new method for optimal features selection and classification using a wavelet based random forest (RF) classifier is proposed in this paper. The classification results are compared with some previously published results, obtained from similar works. The experiments have shown that the proposed method has better classification accuracy.
引用
收藏
页码：855 / 859
页数：5
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