Deep Learning Technique for Recurrence Plot-based Classification of Power Quality Disturbances

被引：3

作者：

Soni, Prity ^{[1
]}

Mondal, Debasmita ^{[1
]}

Chatterjee, Soumya ^{[2
]}

Mishra, Pankaj ^{[1
]}

机构：

[1] Birla Inst Technol Mesra, Elect & Elect Engn, Ranchi, Bihar, India

[2] Natl Inst Technol Durgapur, Elect Engn, Durgapur, India

来源：

2022 IEEE INTERNATIONAL POWER AND RENEWABLE ENERGY CONFERENCE, IPRECON | 2022年

关键词：

Power Quality Disturbances; Deep Learning; Recurrence plot; Support Vector Machine; Transfer Learning; WAVELET; TRANSFORM;

D O I：

10.1109/IPRECON55716.2022.10059470

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The classification of power quality disturbances (PQDs) is essential for the stability and reliability of the power system. A method to categorize PQD incidents using a recurrence plot (RP) is developed in this work. RP technique is used to transform 1-D PQD into 2-D graphics. PQD events were produced in compliance with IEEE standard 1159-1995 in both single and multiple forms. The 2-D graphics created using RP is fed to the deep learning architectures: Googlenet, ResNet-50 and Alexnet. The features obtained from deep learning were classified using support vector machine, which shows the correct classification of 15 classes with 99.63% accuracy.

引用

页数：5

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