基于特征模型的主被动一体化隔振平台控制分析

在各类飞行器飞行的过程中,飞行器载荷会受到不同频段的外扰影响,出现拍照模糊不清楚等问题,建立主被动一体化隔振平台模型,阐述使用特征模型思想对该平台主动隔振系统进行控制,抑制台面振动。选取超磁致伸缩作动器作为主动控制元件,建立主被动一体化隔振平台模型;根据力跟踪的平台模型设计了基于位移跟踪的平台模型;建立位移跟踪隔振平台的特征模型,并将特征模型估计所得的状态变量和黄金分割控制、逻辑微分控制相结合,对隔振平台的主动隔振控制系统进行控制;通过Simulink仿真验证了控制器的有效性,并与传统的PID控制结果进行对比,证明了该控制器的优越性。国家自然科学基金(61603320;61333008;61733017;61273199

Design of pre-warning system for oil pipeline based on Beidou short message

为解决移动通信网络盲区长距离输油管道盗漏预警的问题,利用北斗FB3511模块的短报文卫星通信功能,设计了集单片机、DSP、北斗模块于一体的低功耗长距离输油管道预警系统。根据输油管道预警系统工程应用的要求与通信网络盲区无法使用移动网络链路进行数据传输的特点,提出了实时预警、地理位置定位、分布式拓扑结构及低功耗的功能要求,据此完成了整个系统的方案设计。预警系统硬件平台由信号采集与调理模块、单片机模块、DSP模块、北斗短报文模块及供电模块组成,详细介绍了整个系统的工作流程。现场应用试验结果表明,本系统可以实现移动通信网络盲区长距离输油管道盗漏的实时预警,定位精度、系统延时、平均功率等各项指标均满足系统的设计要求。该系统对于保障移动通信网络盲区长距离输油管道的运行安全具有重要的工程意义。To realize theft and leakage pre-warning on long-distance oil pipelines in the blind areas of mobile communication network, the function of Beidou FB3511 module（i.e., satellite short message communication） was used in this paper to design a long-distance oil pipeline pre-warning system with low power consumption which integrates SCM, DSP and Beidou module together. According to the requirements of engineering application on oil pipeline pre-warning system and the characteristics that mobile network link is not available for the data transmission in the blind areas of mobile communication network, the functional requirements of real-time pre-warning, geological location, distributed topology and low power consumption were put forward. And accordingly the program design of the whole system was completed. The hardware platform of pre-warning system is composed of signal acquisition conditioning module, SCM module, DSP module, Beidou short message module and power supply module. The working process of the whole system was described in detail. It is shown from its field test that this system can provide pre-warning on theft and leakage of long-distance oil pipelines in the blind areas of mobile communication network, and its indicators satisfy the design requirements, e.g. locating precision, system delay and average power. This system is of great engineering significance to ensure the operation safety of long-distance oil pipelines in the blind areas of mobile communication network.河北省科技支撑计划项目“基于北斗系统定位与授时的分布式地震勘探系统研究”,15210333;河北省科技支撑计划项目“基于北斗系统和高分遥感数据的县域集成信息服务平台关键技术研究”,16210310

Fault prediction of variable pitch system of wind turbine based on wavelet BP neural network

变桨故障是风电机组重要的停机故障之一,对变桨系统进行故障预测并提高预测精度,是风电开发的关键技术,不但保证电网安全运行而且减少运维成本。分析处理SCADA系统数据,提取相关联参数,即输出功率、风速、桨距角和转子转速。采用BP神经网络对系统进行模型训练,考虑到风电机组参数具有波动性、不确定性等,同时采用小波BP神经网络进行模型训练。建立变桨故障预测模型,预测未来15 d的变桨系统运行情况,用于制定合理的运维方案。通过MATLAB系统仿真研究,对比分析了预测模型性能指标、误差指标和输出数据图形,小波BP神经网络训练预测模型诊断精度比BP神经网络提高了17%,可信率提高了18%,诊断能力提高了15.4%,诊断误报率降低了17%。Variable pitch fault is one of the most important faults in wind power system. It is the key technology for the development of variable pitch control system to predict the fault and improve the prediction accuracy. It not only guarantees the safe operation of the power grid,but also reduces the cost of operation and maintenance. Use the data of SCADA can extract the associated parameters. It includes the output power,wind speed,pitch angle and rotor speed. BP neural network is used for the model system. Considering the fluctuation of wind power system parameters,uncertainty etc.,at the same time,the wavelet BP neural network is used to train the model. It can be predicted that the variable pitch control system can be operated in the future 15 days through the establishment of pitch fault prediction model. It is used to develop a reasonable operation and maintenance program. Through the MATLAB system simulation study,the paper analyzes the performance of the forecast model,the error index and the output data. Wavelet BP neural network training prediction model diagnostic accuracy than BP neural network increased by 17%,the reliability rate increased by 18%,the diagnostic ability increased by 15.4%,and the diagnostic false alarm rate was reduced by 17%.河北省教育厅青年基金项目（QN2016104）; 河北省科技厅指令性项目（16210310D