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.
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页数：5
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