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

Jian Xue, Jingran Ma, Xingyi Ma, Lei Zhang and Jing Bai ()
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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

Energies, 2024, vol. 17, issue 22, 1-17

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.

Keywords: DVR (dynamic voltage restorer); voltage sag; LSTM neural network; sliding mode control; novel reaching law (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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