Power Quality Disturbances Classification Analysis Using Residual Neural Network

被引:3
|
作者
Abd Jamlus, Nurul Usni Iman [1 ]
Shahbudin, Shahrani [1 ]
Kassim, Murizah [1 ,2 ]
机构
[1] Univ Teknol MARA, Coll Engn, Sch Elect Engn, Shah Alam 40450, Selangor, Malaysia
[2] Univ Teknol MARA, Kompleks Al Khawarizmi, Inst Big Data Analyt & Artificial Intelligence IB, Shah Alam 40450, Selangor, Malaysia
关键词
power quality disturbances; residual neural network; deep learning methods; convolutional neural network; WAVELET;
D O I
10.1109/CSPA55076.2022.9782013
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Along with the Power Quality Disturbances (PQD) such as normal, harmonics, notch, transient, sag and swell that are due to load or electrical appliances continuously occurring in a power system, the supervised detection, and classification method is still in development progress to gain the ideal PQD classification method in order to improve the low power quality in a power system. Automatic detection and classification techniques such as deep learning algorithms are frequently preferred nowadays. Many researchers implement deep learning algorithms especially Convolutional Neural Network (CNN) architecture as a multiple PQD analysis using advanced CNN architecture namely Residual Neural Network (ResNet). To identify which ResNet architecture gives the best performance, two types of ResNet architecture; ResNet-18 and ResNet-50 are implemented. The results obtained and then compared with other CNN architectures such as basic CNN, Deep CNN (DCNN) and GoogLeNet. The results show that ResNet-18 outperforms other CNN architectures with achieved the best performance in terms of accuracy (95.77%), precision (73.73%), sensitivity (67.37%), specificity (97.29%) and F1-score (70.14%).
引用
收藏
页码:442 / 447
页数:6
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