Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer

被引：57
|
作者：
Talaat, Hossam E. A. [1 ]
Abdennour, Adel [1 ]
Al-Sulaiman, Abdulaziz A. [1 ]
机构：
[1] King Saud Univ, Dept Elect Engn, Coll Engn, Riyadh 11421, Saudi Arabia
来源：
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 2010年 / 32卷 / 07期
关键词：
Decentralized control; Neuro-fuzzy control; Genetic Algorithms; Power system stabilizer; Multi-machine lab model; LOGIC; NETWORK;
D O I：
10.1016/j.ijepes.2010.01.011
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The aim of this research is the design and implementation of a decentralized power system stabilizer (PSS) capable of performing well for a wide range of variations in system parameters and/or loading conditions. The framework of the design is based on Fuzzy Logic Control (FLC). In particular, the neuro-fuzzy control rules are derived from training three classical PSSs; each is tuned using GA so as to perform optimally at one operating point. The effectiveness and robustness of the designed stabilizer, after implementing it to the laboratory model, is investigated. The results of real-time implementation prove that the proposed PSS offers a superior performance in comparison with the conventional stabilizer. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：751 / 759
页数：9
相关论文
共 50 条
  • [41] Neuro-fuzzy adaptive strategies applied to power system stabilization
    Barra, W
    Barreiros, JAL
    DEVELOPMENTS IN SOFT COMPUTING, 2001, : 151 - 158
  • [42] Neuro-fuzzy based modeling for photovoltaic power supply system
    Mellit, A.
    Kalogirou, S. A.
    FIRST INTERNATIONAL POWER & ENERGY CONFERENCE (PECON 2006), PROCEEDINGS, 2006, : 88 - 93
  • [43] AI in AM: An experimental investigation using adaptive neuro-Fuzzy interface system as a prediction tool
    Achyut, Trivedi
    Gurrala, Pavan Kumar
    MATERIALS TODAY-PROCEEDINGS, 2022, 62 : 7142 - 7147
  • [44] GA-optimized feedforward-PID tracking control for a rugged electrohydraulic system design
    Sarkar, B. K.
    Mandal, P.
    Saha, R.
    Mookherjee, S.
    Sanyal, D.
    ISA TRANSACTIONS, 2013, 52 (06) : 853 - 861
  • [45] Design of a simplified neuro-fuzzy system based on CMAC Architectures
    Bordon, ME
    Avolio, E
    Da Silva, IN
    De Souza, AN
    WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL 1, PROCEEDINGS: INFORMATION SYSTEMS DEVELOPMENT, 2001, : 1 - 6
  • [46] Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant
    Espitia, Helbert
    Machon, Ivan
    Lopez, Hilario
    APPLIED SCIENCES-BASEL, 2022, 12 (02):
  • [47] The Design of Neuro-fuzzy Control System Based on Data Fusion
    Fu Xiaorong
    ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING III, 2014, 678 : 406 - 409
  • [48] CMOS design of analog neuro-fuzzy system with improved circuits
    Wang, WZ
    Jin, DM
    2004: 7TH INTERNATIONAL CONFERENCE ON SOLID-STATE AND INTEGRATED CIRCUITS TECHNOLOGY, VOLS 1- 3, PROCEEDINGS, 2004, : 1591 - 1594
  • [49] Renewed investigation on Power System Stabilizer design
    Ma Jin
    Wang HaoJing
    Zhang Pu
    SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2011, 54 (10) : 2687 - 2693
  • [50] Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system
    Janarthanan, R.
    Refaee, Eshrag A.
    Selvakumar, K.
    Hossain, Mohammad Alamgir
    Soundrapandiyan, Rajkumar
    Karuppiah, Marimuthu
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2022, 19 (08) : 8132 - 8151
12345