Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters
This research addresses the challenge of grid stability when integrating renewable energy, especially wind power. It focuses on enhancing transient stability in doubly fed induction generator (DFIG) wind energy systems using advanced strategies like fault current limiters and deep learning. The stud...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2024-03-01
|
| Series: | Advances in Engineering and Intelligence Systems |
| Subjects: | |
| Online Access: | https://aeis.bilijipub.com/article_193333_27a0ba07c0a9a6c4eb4b92a8629a442c.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856443447574528 |
|---|---|
| author | Yaoying Wang |
| author_facet | Yaoying Wang |
| author_sort | Yaoying Wang |
| collection | DOAJ |
| description | This research addresses the challenge of grid stability when integrating renewable energy, especially wind power. It focuses on enhancing transient stability in doubly fed induction generator (DFIG) wind energy systems using advanced strategies like fault current limiters and deep learning. The study includes a thorough analysis of fault scenarios, simulations, and solution evaluations, highlighting the crucial need for maintaining stability in renewable energy grids. As wind energy demand rises, optimizing system performance is vital. Many wind turbines rely on DFIGs, necessitating robust fault ride-through. A passive fault current limiter is introduced to enhance DFIG system transient stability. This limiter, devoid of active controllers, offers intrinsic resilience. The research introduces a novel algorithm to calculate optimal fault current limiter resistance, maintaining voltage within ±10% of the reference level. Transient stability is evaluated through simulations involving symmetric and asymmetric faults, incorporating deep learning. MATLAB/Simulink confirms the efficacy of the proposed limiter and algorithm in boosting transient stability for DFIG-based wind energy systems. The study underscores the role of fault current limiters and deep learning in seamlessly integrating renewable energy into power grids. |
| format | Article |
| id | doaj-art-9c75c30b5b28405a9a94e026f74a56f7 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-9c75c30b5b28405a9a94e026f74a56f72025-02-12T08:47:46ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-03-0100301102210.22034/aeis.2024.423904.1139193333Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current LimitersYaoying Wang0School of Mechanical Engineering, Nanyang Technological University, 639798, SingaporeThis research addresses the challenge of grid stability when integrating renewable energy, especially wind power. It focuses on enhancing transient stability in doubly fed induction generator (DFIG) wind energy systems using advanced strategies like fault current limiters and deep learning. The study includes a thorough analysis of fault scenarios, simulations, and solution evaluations, highlighting the crucial need for maintaining stability in renewable energy grids. As wind energy demand rises, optimizing system performance is vital. Many wind turbines rely on DFIGs, necessitating robust fault ride-through. A passive fault current limiter is introduced to enhance DFIG system transient stability. This limiter, devoid of active controllers, offers intrinsic resilience. The research introduces a novel algorithm to calculate optimal fault current limiter resistance, maintaining voltage within ±10% of the reference level. Transient stability is evaluated through simulations involving symmetric and asymmetric faults, incorporating deep learning. MATLAB/Simulink confirms the efficacy of the proposed limiter and algorithm in boosting transient stability for DFIG-based wind energy systems. The study underscores the role of fault current limiters and deep learning in seamlessly integrating renewable energy into power grids.https://aeis.bilijipub.com/article_193333_27a0ba07c0a9a6c4eb4b92a8629a442c.pdfpower systemsdoubly fed induction generator wind turbinefault current limiterstability enhancementdeep learning |
| spellingShingle | Yaoying Wang Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters Advances in Engineering and Intelligence Systems power systems doubly fed induction generator wind turbine fault current limiter stability enhancement deep learning |
| title | Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters |
| title_full | Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters |
| title_fullStr | Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters |
| title_full_unstemmed | Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters |
| title_short | Enhancing Transient Stability of DFIG-Based Wind Turbine Systems Using Deep Learning-Controlled Resistance-Based Fault Current Limiters |
| title_sort | enhancing transient stability of dfig based wind turbine systems using deep learning controlled resistance based fault current limiters |
| topic | power systems doubly fed induction generator wind turbine fault current limiter stability enhancement deep learning |
| url | https://aeis.bilijipub.com/article_193333_27a0ba07c0a9a6c4eb4b92a8629a442c.pdf |
| work_keys_str_mv | AT yaoyingwang enhancingtransientstabilityofdfigbasedwindturbinesystemsusingdeeplearningcontrolledresistancebasedfaultcurrentlimiters |