Electromagnetic Compatibility Estimator Using Scaled Conjugate Gradient Backpropagation Based Artificial Neural Network

被引：28
|
作者：
Khadse, Chetan B. [1 ,2 ]
Chaudhari, Madhuri A. [3 ]
Borghate, Vijay B. [3 ]
机构：
[1] Dhamangaon Educ Societys Coll Engn & Technol, Dept Elect Engn, Amravati 444709, India
[2] Visvesvaraya Natl Inst Technol, Nagpur 440010, Maharashtra, India
[3] Visvesvaraya Natl Inst Technol, Dept Elect, Nagpur 440010, Maharashtra, India
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2017年 / 13卷 / 03期
关键词：
Data acquisition; electromagnetic compatibility; neural network; real-time system; signal processing; DIMENSIONALITY REDUCTION; FEATURE-EXTRACTION; VOLTAGE DIPS; S-TRANSFORM; CLASSIFICATION; MACHINE; ALGORITHMS; FUZZY;
D O I：
10.1109/TII.2016.2605623
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an electromagnetic compatibility estimator is proposed using an artificial neural network with a scaled conjugate gradient algorithm. Neural networks are trained with the help of seven different optimization algorithms in MATLAB. Their performance is evaluated on the basis of number of neurons, desired output, and mean-squared error in offline mode in MATLAB. Among seven algorithms, scaled conjugate gradient algorithm is found to be the best choice. Hence, it is implemented in LabVIEW for online assessment of electromagnetic compatibility issues. Voltage dip, swell, and harmonics are generated with the help of an experimental setup. It consists of 230 V, 50 Hz input voltage supply, microcontroller, variac, and solid-state relays. It is interfaced to the LabVIEW software with the help of an NI USB 6361 data acquisition system. It enabled the continuous online monitoring of various signals. Along with voltage dip and swell, harmonics are also evaluated with the help of spectrum analyzer in LabVIEW. The detailed description of a hardware setup and mathematical modeling of trained network is given in this paper.
引用
收藏
页码：1036 / 1045
页数：10
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