Load frequency control using genetic-algorithm based fuzzy gain scheduling of PI controllers

被引：46
|
作者：
Chang, CS [1 ]
Fu, WH [1 ]
Wen, FS [1 ]
机构：
[1] Natl Univ Singapore, Dept Elect Engn, Singapore 119260, Singapore
来源：
ELECTRIC MACHINES AND POWER SYSTEMS | 1998年 / 26卷 / 01期
关键词：
D O I：
10.1080/07313569808955806
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：
This paper describes the application of fuzzy gain scheduling on the proportional-integral (PI) load frequency control for a multi-area interconnected power system. To improve the performance of the power system, an appropriate optimization method namely a refined genetic algorithm (RGA), has been used to tune the membership functions and rule sets for the fuzzy control. The control methodology adopts a formulation for the area control error which always guarantees zero steady-state values for both the time error and inadvertent energy. The proposed control has been designed for a two-area interconnected power system with control deadbands and rate constraints. Simulation results confirm the designed control performance of the proposed control.
引用
收藏
页码：39 / 52
页数：14
相关论文
共 50 条
  • [41] Single Area Load Frequency Control using Fuzzy-Tuned PI Controller
    Mohammed, Tigist
    Momoh, James
    Shukla, Anup
    2017 NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2017,
  • [42] Autonomous helicopter control using fuzzy gain scheduling
    Kadmiry, B
    Bergsten, P
    Driankov, D
    2001 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS, 2001, : 2980 - 2985
  • [43] The Investigation of Fuzzy Logic-PI Based Load Frequency Control of Keban HEPP
    Tasar, Beyda
    Guler, Hasan
    Ozdemir, Mehmet
    CONTROL ENGINEERING AND APPLIED INFORMATICS, 2015, 17 (04): : 71 - 80
  • [44] Reduction of the number of rules in fuzzy controllers using genetic algorithm
    Belarbi, K
    Filali, S
    Talbi, N
    Boutamina, B
    2004 IEEE International Conference on Industrial Technology (ICIT), Vols. 1- 3, 2004, : 1112 - 1115
  • [45] Learning rule structures of fuzzy controllers based on genetic algorithm
    Xiong, N.
    Systems Science, 2000, 26 (02): : 69 - 84
  • [46] Neuro genetic fuzzy system for Gain Scheduling adaptive control design
    Serra, Ginalber L. O.
    Bottura, Celso P.
    ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, 2006, : 942 - 947
  • [47] A Gain-Scheduling PI Control Based on Neural Networks
    Tronci, Stefania
    Baratti, Roberto
    COMPLEXITY, 2017,
  • [48] Design of load frequency controllers using genetic algorithm for two area interconnected hydro power system
    Aditya, SK
    Das, D
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2003, 31 (01) : 81 - 94
  • [49] A multiobjective fuzzy PI controller based on genetic algorithm
    Serra, GLO
    2003 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1 AND 2, 2003, : 672 - 677
  • [50] Optimal DTC control strategy of DFIG using variable gain PI and hysteresis controllers adjusted by PSO algorithm
    Amer M.
    Miloudi A.
    Lakdja F.
    Periodica polytechnica Electrical engineering and computer science, 2020, 64 (01): : 74 - 86
12345