Automatic generation control based on improved particle swarm optimisation algorithm

Most of the load frequency control（LFC）of automatic generation control（AGC）uses the PID controller based on particle swarm optimisation to operate the AGC unit. For the traditional particle swarm optimisation algorithm, it is easy to fall into a local optimal problem. This paper proposes a block chaotic particle swarm optimisation algorithm. By dividing these swarms into several areas, the different particles within the region are limited to different small areas, so that it avoids particle swarm optimisation searching best index in a small area. At the same time, inertia weights based on linear differential decrease strategy are introduced. Different search speeds are used in the early and late stages of search. Large inertia weights are used in the early stage to improve the global search ability of the population, and later the chair group is given a smaller search speed to improve the local search ability of the population by searching near the best value. Then, the partitioned particle swarm optimisation algorithm is combined with the chaotic particle swarm optimisation algorithm to make use of the ergodicity of chaos to stabilise the particle swarm while some particles jump out of the accumulating area, which further solves the problem of the easy prematurity of the traditional particle swarm optimisation algorithm.In this paper, the block chaotic particle swarm optimisation algorithm is investigated for frequency control of a single regional power system. The studied system comprises of various autonomous generation systems such as photovoltaic power generation and diesel engine generator. The simulation experiments show that the frequency can be controlled reposefully by PID controller with block chaotic particle swarm optimisation algorithm.