A New Self-Tuning Deep Neuro-Sliding Mode Control for Multi-Machine Power System Stabilizer

Chan Gu (), Encheng Chi, Chujia Guo, Mostafa M. Salah () and Ahmed Shaker
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Chan Gu: School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Encheng Chi: School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Chujia Guo: School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Mostafa M. Salah: Electrical Engineering Department, Future University in Egypt, Cairo 11835, Egypt
Ahmed Shaker: Engineering Physics and Mathematics Department, Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt

Mathematics, 2023, vol. 11, issue 7, 1-18

Abstract: In order to increase the accuracy and improve the performance of the power system stabilizer (PSS) controller compared to the methods presented in other studies, this paper presents a new method for tuning sliding mode control (SMC) parameters for a PSS using a deep neural network. This controller requires fast switching which can create unwanted signals. To solve this problem, a boundary layer is used. First, the equations of a multi-machine power system are converted into the standard form of sliding mode control, and then the sliding surfaces are determined with three unknown parameters. Calculating and determining the optimal values (at any moment) for these parameters are fundamental challenges. A deep neural network can overcome this challenge and adjust the control system regularly. In the simulation, a power system with 4 machines and 11 buses is implemented and both phase-to-ground and three-phase errors are applied. The simulation results clearly show the good performance of the proposed method and especially the importance of the deep neural network in the SMC structure compared to other methods.

Keywords: deep neural network; sliding mode control; power system stabilizer; faults (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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