Identification and Classification of Microgrid Disturbances in a Hybrid Distributed Generation System Using Wavelet Transform

被引：0
|
作者：
Puthenpurakel, Sleeba Paul [1 ]
Subadhra, P. R. [1 ]
机构：
[1] Govt Engn Coll, Dept Elect & Elect Engn, Trichur, Kerala, India
来源：
2016 INTERNATIONAL CONFERENCE ON NEXT GENERATION INTELLIGENT SYSTEMS (ICNGIS) | 2016年
关键词：
Distributed Generation (DG); Wavelet Transform (WT); Machine Learning(ML); Artificial Neural Networks(ANN); Total Harmonic Distortion(THD); ISLANDING DETECTION; HARMONIC DISTORTION; VOLTAGE UNBALANCE; POWER-SYSTEM; INVERTERS;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, Islanding and Power Quality(PQ) Issues in Hybrid Distributed Generation(DG) System consists of Photovoltaic(PV) system and Wind Power Plant connected to grid through a Point of Common Coupling(PCC), are detected and classified, using Wavelet Transform (WT) and Artificial Neural Networks (ANN). A new effective approach is proposed using WT indices and loading of DG on detection and classification of disturbances in the system. WT indices extracted from the Negative Sequence component of the voltage signal at PCC is used to detect the disturbances. WT indices and loading of DG system are then used to train ANN. The results demonstrate the advantages of WT over THD in classification of disturbances in the system and robustness in application of Machine Learning (ML) Classifier.
引用
收藏
页码：348 / 352
页数：5
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