Smart controller based on heuristic evolution techniques (case study: BGPS – Iraq)

Many Artificial Intelligent (AI) techniques are introduced in designing different smart controllers. The main objective of this work is to improve the design of a smart controller for power system stabilizer in a power generation station. It also investigates the performance of the suggested controller when subjected to different disturbances such as small changes in the load and parameters variation that may leads to unstable system. Two types of AI techniques; genetic algorithms (GA) and particle swarm optimization (PSO) techniques have been adopted in designing a smart control unit for a power station. The dynamic parameters of the power system stabilizer (PSS) have been tuned and optimized based on the smart and conventional controllers; the performances for both are compared. The simulation results indicate the performance of the PSO based controller power system stabilizer is much better than that based on the genetic algorithm. The power station is modelled and tested by series of model simulations carried by MATLAB and its Power system analysis toolbox (PSAT) verified that the suggested smart control model for the PSS and the parameter tuning method are effective and robust.