Genetic algorithms for modelling and optimisation
Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
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A new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GAs) is proposed for solution of the Load frequency Control (LFC) problem in a deregulated power system. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, in the proposed GA based HF (GAHF) controller the membership functions are tuned automatically using a modified GA's based on the hill climbing method. The aim is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantages of GAs and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed method is tested on a three-area deregulated power system under different operating conditions in comparison with the robust mixed H2/H∞ controller through some performance indices to illustrate its robust performance.