A novel statistically tracked particle swarm optimization method for automatic generation control

Particle swarm optimization (PSO) is one of the popular stochastic optimization based on swarm intelligence algorithm. This simple and promising algorithm has applications in many research fields. In PSO, each particle can adjust its ‘flying’ according to its own flying experience and its companions' flying experience. This paper proposes a new PSO variant, called the statistically tracked PSO, which uses group statistical characteristics to update the velocity of the particle after certain iterations, thus avoiding local minima and helping particles to explore global optimum with an improved convergence. The performance of the proposed algorithm is tested on a deregulated automatic generation control problem in power systems and encouraging results are obtained.