Abstract:

Aiming at the characteristics of the power system load such as randomness，timevarying and uncontinuity, an improved shuffled frog leaping algorithm (ISFLA) that is applied to the parameter identification of the static load model was proposed. On the basis of the shuffled frog leaping algorithm and particle swarm optimization, the updating strategy of SFLA is modified by introducing the variable learning factor . It is proved that this algorithm can improve the accuracy of the optimization, accelerate the convergence speed, enhance the local development capability and overcome the SFLA’s shortage that is easy to get rid of the local optimal solution by introducing the variable learning factor. The simulation results demonstrate the effectiveness and feasibility of the proposed method.

Key words: shuffled frog leaping algorithm;variable learning factor;static load model;parameter identification;power system