PSO-scaled fuzzy logic to load frequency control in hydrothermal power system

被引:23
|
作者
Jeyalakshmi, V. [1 ]
Subburaj, P. [2 ]
机构
[1] PSN Coll Engn & Technol, Tirunelveli 627152, Tamil Nadu, India
[2] Natl Engn Coll, Kovilpatti 628503, Tamil Nadu, India
关键词
Fuzzy logic controller; Load frequency control; Particle swarm optimization; PI controller; Super conducting magnetic energy storage device; Thyristor-controlled phase shifter; AUTOMATIC-GENERATION CONTROL; PARTICLE SWARM OPTIMIZATION; ENERGY-STORAGE; VARIANTS; DESIGN; TCPS; SMES;
D O I
10.1007/s00500-015-1659-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper, presents the particle swarm optimization-based fuzzy logic controller (PSO FLC) design for load frequency control in a two-area interconnected hydrothermal power system. Flexible alternating current transmission system devices and energy storage devices are being installed to improve the reliability and stability of the system under dynamic conditions. One such devices namely thyristor-controlled phase shifter (TCPS) is connected in series with the tie-line to damp out the power swings and frequency oscillations. Similarly at the terminal of one control area, a fast acting energy storage device of superconducting magnetic energy storage (SMES) is connected to meet the sudden changes in demand. The existing conventional controllers are unable to provide the satisfactory performance over a wide range of operating conditions due to system nonlinearity and plant parameter variations. To improve the dynamic performance of the system, this work proposes an intelligent tuning approach using a combination of particle swarm optimization (PSO) and fuzzy logic technique. In this work, PSO algorithm is employed for the optimal selection of membership function parameters of the proposed fuzzy PI, TCPS and SMES controllers by minimizing the time domain objective function. The simulation study is performed by the proposed PSO FLC in a two-area interconnected power system. To show the effective performance of the proposed controller, a comparative study has been made with the conventional, genetic algorithm and fuzzy logic-based optimized controller under varying load conditions.
引用
收藏
页码:2577 / 2594
页数:18
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