Regulated Two-Dimension Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

ASIA unversity > 管理學院 > 經營管理學系 > 期刊論文 > Item 310904400/115584

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/115584

题名:	Regulated Two-Dimension Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
作者:	Chen, Cheng-I;Chen, Cheng-I;Sand, Sunneng;Berutu, Sunneng Sandino;陳永欽;CHEN, YEONG-CHIN;楊皓程;Yang, Hao-Cheng
贡献者:	資訊電機學院資訊工程學系
关键词:	power quality disturbances;signal synchronization;regulated two-dimensional deep convolutional neural network;microgrid;power quality classifier;IEEE Std. 1159
日期:	2022-04-01
上传时间:	2023-03-29 02:50:00 (UTC+0)
出版者:	亞洲大學
摘要:	Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.
显示于类别:	[經營管理學系 ] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	63	检视/开启

在ASIAIR中所有的数据项都受到原著作权保护.

数据加载中.....