Design of PSS for multi-machine system using extreme learning machine algorithm

作     者：Suman, M. Rao, M. Venu Gopala Veerendra, A. S. Mopidevi, Subbarao Marquez, Fausto Pedro Garcia 

作者机构：Vignans Lara Inst Technol & Sci Dept Elect & Elect Engn Vadlamudi 522213 India Prasad V Potluri Siddhartha Inst Technol Dept Elect & Elect Engn Kanuru 520007 India Manipal Inst Technol Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal 576104 India Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India Univ Castilla La Mancha Ingn Res Grp Albacete Spain 

出 版 物：《MEASUREMENT》 (Meas J Int Meas Confed)

年 卷 期：2025年第247卷

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：Ministerio de Ciencia e Innovacion (Spain) European Regional Development Fund, under the Research Grant WindSound project [PID2021-125278OB-I00] 

主　　题：Power System Stabilizer Power System Stability Fuzzy Controller Back Propagation Algorithm Extreme Learning Machine Algorithm 

摘      要：A novel Extreme Learning Machine algorithm is used to train the neural network for Power System Stabilizer (PSS) to minimize low-frequency oscillations. The use of rapid-acting exciters, the interconnection of various power systems, and disturbances like faults and load changes all contribute to the generation of low-frequency oscillations. If sufficient damping is not provided, these oscillations generate and sustain, and eventually cause the power system to shut down entirely. The lead-lag power system stabilizer is a conventional device used but it is slow in operation and can apply to linear systems only. Artificial intelligence techniques like, fuzzy and neural networks are used to overcome the bottlenecks. The neural networks are trained using backpropagation and extreme learning algorithms. The operation of the designed power system stabilizers is verified on a 7-machine, 29-bus system, 4-machine, 11-bus system, and SMIB System. The proposed controller has been providing better damping performance compared to other controllers in terms of Integral Time Squared Error (ITSE) and Integral Squared Error (ISE). The proposed system is designed and validated through the R2023b MATLAB/Simulink Software environment.