Research On MPPT Based On SVM Optimized By Intelligent Water Drops Algorithm

The exploitation and use of fossil energy,causing a serious impact on the ecological environment,in order to better protect the ecological environment of the earth,the use of green energy and renewable energy to achieve sustainable development is imminent.Solar energy is a very popular clean energy source and has broad prospects for development.photovoltaic power generation is the main way of development and utilization of solar energy,has the advantages of safe and reliable,flexible application form,easy installation and maintenance,has broad prospects for development.But the current photovoltaic power generation has low power conversion rate,high cost,and the current widely used maximum power tracking control algorithm(MPPT),is an important means to improve the efficiency of its conversion.Firstly,the methods and techniques and significance of the research on the output prediction of photovoltaic power generation are introduced.the output characteristics of photovoltaic cells are analyzed,and the necessity and importance of MPPT are introduced.Then,a new swarm intelligence optimization algorithm-Water Drops Intelligent algorithm(IWD)is introduced,which is proposed for discrete optimization problems,and the optimization of SVM parameters is a continuous optimization problem,at the same time,the traditional intelligent water drop algorithm is prone to stagnation and blocking in the selection of nodes.In view of the above problems,this paper puts forward the improvement of the traditional intelligent water drop algorithm,then using the traditional standard test function optimization algorithm to test the improved intelligent water drop algorithm,and compared with the traditional ant colony algorithm and standard intelligent water drop algorithm.The experimental results show that the proposed algorithm is used for the function optimization problem,and has good convergence and accuracy.Secondly,the basic principle of support vector machine(SVM)algorithm is introduced,Optimization of support vector machine parameters using intelligent water drop algorithm is oproposed.Then use the standard test set provided by UCI website to test the performance of the algorithm,and compared with the traditional ant colony algorithm and particle swarm algorithm,the results show that the optimization of support vector regression can achieve higher prediction accuracy of intelligent drop algorithm.Finally,a photovoltaic power plant as the research object,eExperimental data ofphotovoltaic power station is used in the experiment.The parameter optimization results of genetic algorithm,particle swarm optimization and intelligent water drop algorithm were compared and analyzed.The simulation model of MPPT based on IWD-SVM in matlab/simulink environment is presented.The results show the feasibility of using intelligent water drop to optimize the SVM model in the maximum power tracking of PV system.