Improving The Particle Swarm Algorithm And Its Application In Multi-Power Grid Connection

With the continuous development of science and technology and the increasing demand for energy,people’s demand for fossil energy,such as oil,coal and natural gas,is also increasing.This brings about a series of problems,such as environmental pollution,the consumption of fossil energy,and so on.Therefore,the research and development of new and clean alternative energy sources is of great significance.With the rapid increase of the installed capacity of wind power and photovoltaic power generation,the access of new energy makes the problem of multiple power grid connection faced with severe challenges.For the problem of multiple power grid connection,the particle swarm algorithm solution has strong optimization and convergence,so it has received wide attention.To overcome the uncertainty of initializing the population in the PSO,in this paper an improved PSO algorithm is proposed.Initialize the population with the chaotic map Logistic map.Using the ergodicity and non-repeatability of chaos,the initial solution generated by the PSO is closer to the global optimal solution,and then the solution speed of the PSO is improved.The inertial weight of the particle swarm algorithm is an important parameter,which can adjust the global and local optimization ability of the algorithm.The inertial weight of the particle swarm algorithm is automatically adaptive,and the inertial weight of the algorithm is large,so that the inertial weight is gradually reduced to adapt to the ability of local search.The test function is used for comparison experiments.The experimental results show that the improved algorithm is closer to the minimum value and the convergence is better than other comparison algorithms,so it is proved that the improved strategy can significantly improve the optimization ability of the particle swarm algorithm.In order to solve the problem that PSO can easily fall into local optimum in the late stage of calculation,a mixed PSO is proposed on the basis of the above algorithm.On the basis of the original search mechanism of the particle swarm algorithm,the mixed differential evolution algorithm uses the search mechanism of the particle swarm or the differential evolution algorithm through a certain probability.The particle swarm algorithm can effectively maintain the population diversity of the particle swarm algorithm.The introduction of a Gaussian disturbance mechanism into the PSO algorithm ensures that the mixed PSO increases the possibility of a global search under the premise of effective local search.Finally the mixed particle swarm algorithm under the same iteration number of comparison experiment,mixed particle swarm algorithm in the experiment can be minimum in multiple test function,in the target value iteration figure can be concluded that the convergence of mixed particle swarm algorithm is significantly improved,better than other improved particle swarm algorithm,confirmed that the mixed particle swarm algorithm has a stronger optimal search and search ability.In view of the optimization of multi-power grid,this paper establishes the optimization output model of multi-power grid based on fire scenery.And under the condition of multiple objectives and multiple constraints,a reasonable demand side load is set.On the premise of maximizing the use of wind and solar energy,the output of thermal power units is optimized,so that the output can meet the load demand under the minimum fluctuation of the power grid.And compare experiments with other improved particle swarm algorithms at the same setting.By applying the hybrid PMS to the multi-power grid optimization problem,the simulation results show that the improved hybrid PMS algorithm is50% less than the comparison algorithm.It can be concluded that under the premise of effectively controlling the environmental pollution,the hybrid particle swarm algorithm can effectively reduce the interactive power of the multiple power grid connection model and the power grid,and save nearly half of the power generation cost.In summary,this paper proposes a mixed particle swarm algorithm and demonstrates its superior optimization ability through comparative experiments.The hybrid particle swarm algorithm is applied to the multi-power grid-connected power generation model to obtain the better solution results.It effectively reduces the interaction volatility between the multi-power grid-connected model and the power grid,reduces the power generation cost by nearly half,ensures the volatility and economy of the multi-power grid-connected power generation model,and has considerable optimization effect.