Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders
Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learni...
Main Authors: | Haosen Yang, Robert C. Qiu, Houjie Tong |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | Journal of Modern Power Systems and Clean Energy |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9275597/ |
Similar Items
-
Proposing Multimodal Integration Model Using LSTM and Autoencoder
by: Wataru Noguchi, et al.
Published: (2016-12-01) -
An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
by: Wangyang Wei, et al.
Published: (2019-07-01) -
Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder
by: Elvan Duman, et al.
Published: (2019-01-01) -
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
by: Hakan Gunduz
Published: (2021-04-01) -
Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability
by: Zhaoyi Xu, et al.
Published: (2021-01-01)