Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification
The proportion of new energy sources, such as wind, photovoltaic and hydropower, in the power grid is increasing year by year. In addition, a large number of nonlinear loads are connected to the grid, resulting in frequent power quality disturbances (PQDs), which pose challenges to the stability and...
Saved in:
Main Authors: | Shuangquan Yang, Tao Shan, Xiaomei Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/18/2/231 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion
by: Junwei Ma, et al.
Published: (2025-01-01) -
A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System
by: Jiahui Cheng, et al.
Published: (2024-01-01) -
A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships
by: Babak Amiri, et al.
Published: (2025-01-01) -
Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation
by: Yih-Lon Lin, et al.
Published: (2025-01-01) -
Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN
by: Lei Wang, et al.
Published: (2025-01-01)