Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control

• Published in: Electric power systems research 2004-12, Vol.72 (3), p.203-212
• Main Author: Ghoshal, S.P.
• Format: Article
• Language: English
• Subjects: Applied sciences; Automatic generation control; Constriction factor approach (CFA); Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Genetic algorithm; Inertia weights approach (IWA); Particle swarm optimizations (classical and hybrid); Power electronics, power supplies; Selection ratio; Simulated annealing; Sugeno fuzzy logic; Thermal power plants
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2004.04.004

In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.