Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods
In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone...
Main Authors: | Qinghua Wang, Yuexiao Yu, Hosameldin O. A. Ahmed, Mohamed Darwish, Asoke K. Nandi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/16/4438 |
Similar Items
-
Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method
by: Qinghua Wang, et al.
Published: (2021-06-01) -
Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission
by: Hosameldin O. A. Ahmed, et al.
Published: (2022-01-01) -
Voltage Correlation Based Single Pole-to-Ground Fault Detection of MMC-HVDC Transmission Line
by: Na An, et al.
Published: (2021-01-01) -
Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault
by: Qing Huai, et al.
Published: (2018-06-01) -
New Sub-Module with Reverse Blocking IGBT for DC Fault Ride-Through in MMC-HVDC System
by: Ui-Jin Kim, et al.
Published: (2021-03-01)