This paper addresses the use of fuzzy neural networks (FNN) for predicting the nonlinear network traffic. Through training the fuzzy neural networks with momentum back-propagation algorithm (MOBP) and choosing the...
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This paper addresses the use of fuzzy neural networks (FNN) for predicting the nonlinear network traffic. Through training the fuzzy neural networks with momentum back-propagation algorithm (MOBP) and choosing the appropriate activation function of output node, the traffic series can be well predicted by these structures. From the effective forecasting results obtained, it can be concluded that fuzzy neural networks can be well applicable for the traffic series prediction. In addition,the performance of the FNN was particularly discussed and analyzed in terms of prediction ability compared with solely neural networks. The effectiveness of the oroBosecl FNN is demonstrated through the simulation.
随着传统能源的消耗和环境的不断恶化,国内外对新能源的需求达到了前所未有的高度,伴随着光储并网发电技术的发展,光储发电系统逐渐成为电力系统中重要的分支。利用储能单元能量密度高的优势,把光伏系统与储能单元相互结合,不仅改善光伏功率不均衡的问题,还起到了直流母线稳压的作用。而光伏并网逆变器作为并网过程中的核心部件,通过优化控制方法改善在并网过程中出现电能质量差的问题。以此,着重研究储能单元的稳压性能及并网逆变器的模糊神经网络控制算法,弥补在并网的过程中出现光伏功率不足,实现高性能稳定可靠的并网运行。文章的总体研究如下:首先从光伏发电原理入手,通过等效电路建立其相关的数学模型,并对其光伏特性做了相应的仿真分析,阐述了光伏电池中的最大功率点跟踪(Maximum Power Point Tracking,MPPT)的基本原理,并且对比分析了目前常用的三种跟踪控制算法,搭建仿真模型分析,验证了扰动观察法的优越性。其次建立以镍氢电池为单元的储能系统,分析了镍氢电池的等效电路模型,采用了DC/DC变换电路,设计了以恒压控制和基于蓄电池SOC下垂控制的储能系统能量管理策略,控制储能系统的能量吸收与释放,验证了控制策略的可靠性。最后针对正常模式的并网逆变器,在传统的电压电流双闭环的控制中,加入了模糊神经网络的优化算法,该方法通过光伏输出电压电流与并网电压电流的误差,让模糊神经网络集中优化自学习,来预测最优的PID控制参数,从而使得并网电压、电流快速同频同相位。从结果看出,设计的控制算法的准确度和响应速度效果更佳。
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