Data Management And Analyses Of Hybrid Energy Storage For Wind Generators

With the rapid development of economy,the state attaches great importance to energy conservation and emission reduction.It is imperative to develop new energy resources which make it necessary to study new energy sources.This paper focuses on the data management,performance and capacity allocation of hybrid energy storage for wind power generation.The work mainly completes three aspects of the research.First of all,based on J2 EE layered architecture combined with open source framework Hibernate and Struts2,the hybrid storage data management system for wind power generation is designed by using JavaBean,Servlet,JSP and MySQL database.And the AES technology is used to encrypt the data.The system not only realizes the effective management of the data,but also makes the management operation achieved just by accessing the browser with users.Secondly,according to the characteristic analysis of the wind turbine,the hybrid energy storage system with battery supercapacitor is presented combined with the characteristics of battery supercapacitor,And then the equivalent model of the battery supercapacitor are established including the equivalent circuit model of the direct parallel connection and parallel connection with inductance.According to the data provided by the data management system and data collected in real time,the two equivalent models are analyzed when the load power is pulsating.The results show that not only the capacity of the system's output power is improved and the process of battery discharge is optimized,but also the reliability of the system is heightened in the hybrid energy storage system of parallel connection with inductance.Finally,the constraint conditions of capacity configuration optimization are determined after detailed analysis of the optimization strategy of hybrid energy storage system.The whole life cycle cost of the hybrid energy storage system with battery supercapacitor for wind generators is selected as the optimization goal by using the constraint conditions and the data provided by the data management system.The optimization goal is solved with standard particle swarm optimization(PSO)algorithm and weight improved PSO algorithm and the acceleration factor improved PSO algorithm.It can be seen from the solving graphs and results that the acceleration factor improved PSO algorithm is more effective compared with standard PSO algorithm and weight improved PSO algorithm,where the convergence rate of the superposition is faster and the life cycle cost is less.Experiments and practical results show that the design of the data management system and the capacity allocation optimization meet the requirements of data management and configuration optimization by using the particle swarm optimization algorithm in this paper,which also meet the needs of the design and have practical application value.