当前位置: X-MOL 学术IEEE Sens. J. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Hyperbolic Window S-Transform Aided Deep Neural Network Model-Based Power Quality Monitoring Framework in Electrical Power System
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2021-04-08 , DOI: 10.1109/jsen.2021.3071935
Kiron Nandi , Arup Kumar Das , Riddhi Ghosh , Sovan Dalai , Biswendu Chatterjee

With the fast development of power grid the usage of electrical equipments is increased which led to importance of power quality disturbance sensing for reliable and smooth operation. In this paper, a deep neural network has been designed using stacked autoencoder (SAE) for deep feature extraction from time-frequency spectrum of single and combined PQ disturbances in electrical power system network. For this purpose, synthetic PQ signals are analyzed in time-frequency domain through hyperbolic window stockwell transform (HWST). Thereafter, PQ signal converted HWST time-frequency matrix has been grouped into time-frequency blocks and subsequently fed as input to 3-layer stacked autoencoder model (SAE) for deep feature learning. Finally, the extracted deep features are classified through several machine learning classifier. The results indicate that proposed framework using XGboost classifier can classify 18 different single and combined PQ event with a 99.86% accuracy. The proposed framework also yields satisfactory outcome with real life PQ data. Therefore, proposed framework can be implemented for Power quality monitoring in electrical power system.

中文翻译:

双曲线窗口S-变换辅助的基于深度神经网络模型的电力系统电能质量监测框架

随着电网的快速发展,电气设备的使用增加,这导致电能质量扰动检测对于可靠和平稳运行的重要性。在本文中,使用堆叠自编码器 (SAE) 设计了一种深度神经网络,用于从电力系统网络中单个和组合 PQ 扰动的时频频谱中进行深度特征提取。为此,通过双曲窗斯托克韦尔变换 (HWST) 在时频域中分析合成 PQ 信号。此后,经过 PQ 信号转换的 HWST 时频矩阵被分组为时频块,随后作为输入馈送到 3 层堆叠自动编码器模型 (SAE) 以进行深度特征学习。最后,通过几个机器学习分类器对提取的深度特征进行分类。结果表明,使用 XGboost 分类器提出的框架可以以 99.86% 的准确率对 18 个不同的单个和组合 PQ 事件进行分类。拟议的框架也产生了令人满意的结果与现实生活 PQ 数据。因此,建议的框架可以实现电力系统中的电能质量监控。
更新日期:2021-06-15
down
wechat
bug