ZERO CROSSING POINT DETECTION IN A DISTORTED SINUSOIDAL SIGNAL USING DECISION TREE CLASSIFIER

被引：2

作者：

Veeramsetty, Venkataramana ^{[1
]}

Jadhav, Pravallika ^{[2
]}

Ramesh, Eslavath ^{[2
]}

Srinivasula, Srividya ^{[2
]}

Salkuti, Surender Reddy ^{[3
]}

机构：

[1] SR Univ, Ctr AI & Deep Learning, Warangal 506371, India

[2] SR Univ, Sch Engn, Dept Elect & Elect Engn, Warangal 506371, India

[3] Woosong Univ, Dept Railroad & Elect Engn, 171 Dongdaejeon, Daejeon 34606, South Korea

来源：

ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING | 2022年 / 20卷 / 04期

关键词：

Decision tree; distorted sinusoidal signal; harmonics; noise; zero-crossing point;

D O I：

10.15598/aeee.v20i4.4562

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Dis-torted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this pa-per, a decision tree classifier is used to predict the zero crossing point in a distorted signal based on input fea-tures like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classifier model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classifier is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals.

引用

页码：444 / 477

页数：34

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