项目名称: 分布式多元供能系统数学建模与优化控制策略研究
项目编号: No.61473174
项目类型: 面上项目
立项/批准年度: 2015
项目学科: 自动化技术、计算机技术
项目作者: 刘海
作者单位: 山东大学
项目金额: 81万元
中文摘要: 本课题拟研究分布式多元供能系统的数学建模与优化控制策略,利用时间序列预测技术、神经网络、混沌理论与技术等,建立分布式多元供能系统优化配置与控制的模型体系与理论框架。具体从以下三个方面展开深入研究:1)研究分布式多元供能系统输出功率混沌预测模型,实现可再生能源与传统能源转换的最大功率跟踪;2)利用相空间重构技术建立分布式多元供能系统的混沌动力学模型,通过对数学模型的研究与分析确定影响系统行为的关键参数,实现系统最优化参数选择以及系统的集成优化设计;3)研究分布式多元供能系统的模糊神经网络自适应控制策略,利用模糊逻辑实现基于知识的控制规则表达问题,利用神经网络自适应学习功能对系统相关参数进行学习和调整,实现系统的优化控制。本课题的研究将深化对分布式多元供能系统内部作用规律的理解,实现分布式多元供能系统的数学建模、定量分析及优化控制。
中文关键词: 分布式供能;数学建模;优化配置;最优控制;混沌
英文摘要: This project intends to give a method of mathematical modeling and optimal control for distributed generation system. The aim is to set up the model system and the theoretical framework for optimal allocation and control of distributed generation system. The technology employed in this project include time sequence prediction technology, neural network, chaos theory and technology and so on. The main works and contributions are summarized as follows: 1) A prediction model of distributed generation system power is established. The maximum power point is tracked,which is the condition of transition between renewable and traditional energy sources. 2) The chaotic kinetic model is established for distributed generation system based on phase space reconstruction technology. The key parameters related to the system behavior are given based on the analysis of the mathematical model. The optimal parameter and configuration of the system are chosen. 3) A method of adaptive fuzzy neural network control is presented to realize optimal control for distributed generation system. The control rules based on the knowledge are expressed by fuzzy logic. The related parameters are adjusted based on the adaptability of the neural network. The research of this project will make deep thoughts of the internal action law of distributed generation system. The scientific analysis of distributed generation system is performed, including mathematical modeling, quantitative analysis and optimal control.
英文关键词: Distributed Generation;Mathematical Modeling;Optimal Configuration;Optimal Control;Chaos