Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

自适应变步长最大风能捕获算法

风能具有随机性、不稳定性的特点,为了提高风力发电系统中风能的利用效率,在比较各种最大风能捕获算法的基础上,分析了爬山搜索法和叶尖速比法的不足,提出了自适应变步长搜索算法来捕获最大风能.通过改进爬山搜索法的变步长策略,明显加快了搜索速度,通过引入初始估计叶尖速比值,大大缩小了搜索范围.该算法不需要实时检测准确风速,不依赖风力机最佳功率曲线,有效地降低了成本,提高风力发电的效率.文中重点分析了算法的自适应性和变步长策略,仿真结果表明,该算法能够使风力机更快速到达最大功率点,动态响应快,收敛性好.     

基于神经网络的聚类方法研究

针对常用聚类方法不能有效处理噪声数据的问题,本文结合神经网络具有自适应性的特点,提出基于神经网络的聚类(NN_Cluster)模型,并设计了基于自适应共振理论的神经网络聚类模型(ARTNN_Cluster)和基于自组织特征映射的神经网络聚类模型(SOMNN_Cluster)。标准数据集上的实验结果表明,与传统的K_means聚类方法相比,本文提出的基于神经网络的聚类模型有效地克服了传统方法的噪声问题,得到了较好的聚类效果。     

铁矿石境外投资风险评估的模糊神经网络模型

论文将模糊理论与神经网络结合,对铁矿石境外投资所涉及的风险因素分别从资产影响、威胁频度两方面进行分析,建立了铁矿石境外投资的风险层次化结构,并构造了各因素所对应评判集的隶属度矩阵;综合运用模糊推理算法与神经网络仿真技术,对铁矿石境外投资的风险进行评估,进而判定铁矿石境外投资风险等级。最后,通过计算铁矿石境外投资的市场风险说明了算法的应用,并将结果与模糊综合评估得到的风险值进行比较,检测了模型的有效性。     

基于CMOS工艺的无线图像传感器网络综述

无线传感器网络是无线通讯技术、网络技术、传感器技术交叉融合的新兴产物。无线传感器网络具备小型化、智能化、网络化和能够无线通讯等特点,能够满足人们在生产生活、科学研究、军事、医疗等众多领域对于实时信息获取的需求。由于人类获取信息的80%以上是通过视觉获取的,因此,无线图像传感器网络作为人类获取视觉图像信息的重要工具在无线传感器网络中占有重要的地位。无线图像传感器网络要想实现小型化、智能化、集成化等要求,采用与标准CMOS工艺兼容的CMOS图像传感器技术将是最为理想的选择。随着CMOS图像传感器工艺和设计水平的进步,CMOS图像传感器在成像质量、生产成本、小型化、智能化等方面显示出巨大的优势,同时由于与VLSI电路兼容的天然特性,基于CMOS图像传感器的无线图像传感器网络拥有巨大的发展潜力和广阔的市场前景。基于CMOS的无线图像传感器网络目前呈现多元化的发展趋势,高分辨率、高速、低功耗、智能化等方向是基于CMOS的无线图像传感器网络未来发展的方向。     

基于被动测量的高速网络管理系统

文章介绍了,面向大规模宽带IP网络的运维和管理需求,以大规模网络流量测量和知识挖掘为基础,研究基于分布式流量测量的新一代宽带网络管理架构,采用网络被动模式测量和主动采集技术手段,实现对高速网络的性能、状态和行为的管理,并对网络测量技术的发展及现状、系统的架构、关键技术和应用情况作了详细阐述,明确下一步研究工作的目标。     

IPv6网络安全的实现与测试

IPv4地址日趋枯竭,IPv6协议替代IPv4协议势在必行。与IPv4协议相比,IPv6协议在地址空间、路由聚合、服务质量和网络安全等方面带来了很大的发展和提升空间,但IPv6的网络安全仍然存在。本文对IPv6的防火墙和入侵检测系统进行了测试,并对其结果进行了分析。为正确部署防火墙和定位入侵检测系统提出了系列建议。     

可视化基站发电管理系统

针对以前的发电管理系统或只是数据呈现,提供市电及发电相关数据信息,或只是提供出车轨迹,主要实现对出车的管理,不能全面反应发电全过程且不便于对发电事件进行分析的情况,提出可视化发电管理系统的概念。在简要介绍了LBS系统、动力环境监控系统后,讲述了可视化基站发电管理系统的组成以及通过LBS定位管理、短信互动、实时监测数据等进行系统实现。可视化发电管理系统通过可视化的界面帮助发电管理人员进行管理,直观、便捷。