Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control
To address the issue of uncertainty in the occurrence time of voltage sags in power grids, which affects power quality, a voltage state prediction method based on LSTM neural networks is proposed for predicting voltage states. For the problem of quickly and accurately compensating for voltage sags,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/22/5528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217608746369024 |
|---|---|
| author | Jian Xue Jingran Ma Xingyi Ma Lei Zhang Jing Bai |
| author_facet | Jian Xue Jingran Ma Xingyi Ma Lei Zhang Jing Bai |
| author_sort | Jian Xue |
| collection | DOAJ |
| description | To address the issue of uncertainty in the occurrence time of voltage sags in power grids, which affects power quality, a voltage state prediction method based on LSTM neural networks is proposed for predicting voltage states. For the problem of quickly and accurately compensating for voltage sags, a DVR system based on a new approach law of sliding mode variable structure control is proposed, which significantly reduces chattering, improves response speed, and enhances the robustness of the system. The stability of the system is proven based on Lyapunov stability theory. Simulation experiments are conducted to analyze the voltage state prediction effect based on the LSTM neural network and the compensation effect of the novel reaching law of sliding mode variable structure control under different levels of voltage sag, validating the effectiveness and correctness of the proposed solution. |
| format | Article |
| id | doaj-art-2ac4e56ebb154e80a47aea849393ce06 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-2ac4e56ebb154e80a47aea849393ce062025-08-20T02:08:00ZengMDPI AGEnergies1996-10732024-11-011722552810.3390/en17225528Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure ControlJian Xue0Jingran Ma1Xingyi Ma2Lei Zhang3Jing Bai4College of Electrical and Information Engineering, Beihua University, Jilin 132021, ChinaBeijing Shougang Mining Investment Co., Ltd., Beijing 100041, ChinaCollege of Electrical and Information Engineering, Beihua University, Jilin 132021, ChinaCollege of Electrical and Information Engineering, Beihua University, Jilin 132021, ChinaCollege of Electrical and Information Engineering, Beihua University, Jilin 132021, ChinaTo address the issue of uncertainty in the occurrence time of voltage sags in power grids, which affects power quality, a voltage state prediction method based on LSTM neural networks is proposed for predicting voltage states. For the problem of quickly and accurately compensating for voltage sags, a DVR system based on a new approach law of sliding mode variable structure control is proposed, which significantly reduces chattering, improves response speed, and enhances the robustness of the system. The stability of the system is proven based on Lyapunov stability theory. Simulation experiments are conducted to analyze the voltage state prediction effect based on the LSTM neural network and the compensation effect of the novel reaching law of sliding mode variable structure control under different levels of voltage sag, validating the effectiveness and correctness of the proposed solution.https://www.mdpi.com/1996-1073/17/22/5528DVR (dynamic voltage restorer)voltage sagLSTM neural networksliding mode controlnovel reaching law |
| spellingShingle | Jian Xue Jingran Ma Xingyi Ma Lei Zhang Jing Bai Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control Energies DVR (dynamic voltage restorer) voltage sag LSTM neural network sliding mode control novel reaching law |
| title | Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control |
| title_full | Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control |
| title_fullStr | Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control |
| title_full_unstemmed | Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control |
| title_short | Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control |
| title_sort | research on voltage prediction using lstm neural networks and dynamic voltage restorers based on novel sliding mode variable structure control |
| topic | DVR (dynamic voltage restorer) voltage sag LSTM neural network sliding mode control novel reaching law |
| url | https://www.mdpi.com/1996-1073/17/22/5528 |
| work_keys_str_mv | AT jianxue researchonvoltagepredictionusinglstmneuralnetworksanddynamicvoltagerestorersbasedonnovelslidingmodevariablestructurecontrol AT jingranma researchonvoltagepredictionusinglstmneuralnetworksanddynamicvoltagerestorersbasedonnovelslidingmodevariablestructurecontrol AT xingyima researchonvoltagepredictionusinglstmneuralnetworksanddynamicvoltagerestorersbasedonnovelslidingmodevariablestructurecontrol AT leizhang researchonvoltagepredictionusinglstmneuralnetworksanddynamicvoltagerestorersbasedonnovelslidingmodevariablestructurecontrol AT jingbai researchonvoltagepredictionusinglstmneuralnetworksanddynamicvoltagerestorersbasedonnovelslidingmodevariablestructurecontrol |