INRUSH CURRENT DETECTION USING WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

被引：0
|
作者：
Gondane, Prachi R. [1 ]
Sheikh, Rukhsar M. [1 ]
Chawre, Kajol A. [1 ]
Wasnik, Vivian V. [1 ]
Badar, Altaf [1 ]
Hasan, M. T. [2 ]
机构：
[1] Anjuman Coll Engn & Technol, Dept EE, Nagpur, Maharashtra, India
[2] Anjuman Coll Engn & Technol, Dept EXTC, Nagpur, Maharashtra, India
来源：
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC 2018) | 2018年
关键词：
Inrush current; Wavelet Transform; Artificial Neural Network; DISCRIMINATION;
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, wavelet transform and artificial neural network (ANN) is used for processing current waveforms and distinguish between inrush current, fault and normal situation. Wavelet transform is used to analyze and detect various frequency components present in the signal. ANN is a tool which is utilized for classification of data based on specific properties. Different types of power system combinations are used in simulation. Fault detection is an important part for safety of electric power system. For the synthesis of signals and the classification of current conditions, WT and ANN are used in collectively.
引用
收藏
页码：866 / 868
页数：3
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