Three phase power quality disturbance detection and classification by SCGB Neural Network

被引：1
|
作者：
Khadse, Chetan B. [1 ]
Chaudhari, Madhuri A. [2 ]
Borghate, Vijay B. [2 ]
Suryawanshi, Hiralal M. [2 ]
机构：
[1] MIT World Peace Univ, Sch Elect Engn, Pune, Maharashtra, India
[2] Visvesvaraya Natl Inst Technol, Dept Elect Engn, Nagpur, Maharashtra, India
来源：
2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) | 2021年
关键词：
Power Quality; Neural Network; Data Acquisition; Signal Procesing; Real Time System;
D O I：
10.1109/I2CT51068.2021.9418004
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
An artificial neural network based three phase real time power quality analysis is proposed in this paper. The scaled conjugate gradient descent algorithm is used as a learning algorithm for neural network. The training and testing data required is generated from the experimental set up of three phase power quality disturbance generator. The exprimental setup is based on the changing voltage range which is controlled by solid state relays. The solid state relays are controlled by the microcontroller. The disturbances generated are sag, swell, interruption, harmonics with and without sag/swell. These disturbances are acquired with the help of NI USB 6361 data acquisition system. The initial training and testing is done in the MATLAB with the dataset collected from disturbance generator. The mathematical model of the trained network is developed and implemented in the LabVIEW for the real time testing. The real time results of the analysis are displayed in the LabVIEW graphic user interface console.
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页数：6
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