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Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control

Author

Listed:
  • Jian Xue

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Jingran Ma

    (Beijing Shougang Mining Investment Co., Ltd., Beijing 100041, China)

  • Xingyi Ma

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Lei Zhang

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

  • Jing Bai

    (College of Electrical and Information Engineering, Beihua University, Jilin 132021, China)

Abstract

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.

Suggested Citation

  • Jian Xue & Jingran Ma & Xingyi Ma & Lei Zhang & Jing Bai, 2024. "Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control," Energies, MDPI, vol. 17(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5528-:d:1514448
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    References listed on IDEAS

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    1. Emiyamrew Minaye Molla & Cheng-Chien Kuo, 2020. "Voltage Quality Enhancement of Grid-Integrated PV System Using Battery-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(21), pages 1-16, November.
    2. Ali Moghassemi & Sanjeevikumar Padmanaban, 2020. "Dynamic Voltage Restorer (DVR): A Comprehensive Review of Topologies, Power Converters, Control Methods, and Modified Configurations," Energies, MDPI, vol. 13(16), pages 1-38, August.
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