基于BP神经网络的李家峡拱坝材料参数反演

利用神经网络模型对大坝材料参数进行反演 ,可避免建模因子的选择不当而造成的误差 ,精度较高。本文利用有限元模型建立神经网络的训练样本 ,采用BP神经网络模型对李家峡拱坝进行了坝体和坝基材料参数反演 ,最后以反演后的材料进行正向计算得到大坝的变形 ,并与实测变形对比 ,结果令人满意。     

基于神经网络的风力发电系统风速软测量

对风力发电并网系统的有效风速测量问题进行研究。鉴于神经网络可应用于非线性系统的模型与辨识,不受非线性模型类的限制,且可给出工程上易于实现的学习算法,提出基于神经网络的有效风速软测量。对实时采集的风力发电机组的风速样本参数集进行分析、训练及拟合,获得相应的有效风速计算网络。仿真结果表明,有效风速软测量可代替风速仪的作用,是一种非常有效的风速估计方法。     

多角透射测量中的电磁参数反演

本文基于自由空间法,采用多入射角透射测量替代传统的位移或相位的直接测量,根据测得的透射率,用梯度迭代法反演出所测材料的电磁参数。此方法测量准确、计算简单,并且具有宽频带的优点。     

电网参数数字测量系统

介绍了利用80C196单片机构成的电网参数数字测量系统。     

基于神经网络与布里渊散射监测技术的线路覆冰计算方法研究

针对目前单纯采用点式覆冰监测终端对输电线路覆冰情况实时监测时,因终端续航时间短、摄像球机本身凝冰或结雾、拉力传感器难校准、终端安装维护困难、覆盖范围有限等造成覆冰效能低等问题,提出了一种基于神经网络与布里渊散射监测技术的线路覆冰计算方法。通过在OPGW线路两端安装布里渊传感器,测量布里渊散射信号的强度及频移,计算任意点光纤温度及应变,利用RBF神经网络对应变及气象参数建模,计算线路覆冰厚度。经系统运行数据证明,该方法能够有效结合气象参数修正OPGW应力法计算的覆冰厚度误差,提高覆冰监测效率及准确性。     

神经网络软测量在家用房间空调器制冷量测量中的研究与应用

软测量是应用计算机技术对难以测量或无法直接测量的变量,选择与之相关的一些容易测量的变量,通过建模变量关系来推断或者估算。本文针对普通家用房间空调器系统的制冷量测量提出了一种基于BP神经网络的软测量方法。目前普通家用房间空调器多数为一拖一空调系统,通过测量采集空调系统中与制冷量变化相关的状态参数,采用BP神经网络预测系统制冷量,并在MATLAB中进行了仿真。同时采用平均影响值MIV对BP神经网络输入层节点参数的权重进行分析,从而达到变量的筛选,发现各变量在系统中作用的重要程度。结果表明,采用BP神经网络软测量的预测效果与实际测量结果偏差较小,可以用于家用房间空调系统制冷量的测量,且输入变量的权重分析结果与影响系统制冷量的组件结构特性较为一致。     

分布式频域电磁散射特性测量系统

为满足雷达目标的超宽带散射特性和天线电参数的超宽带测量,设计了一套功能强大的自动测量系统;在硬件方面采用了分布式的搭建形式,而在软件方面则使用了Visual Basic和Measurement Studio语言并采用模块化、层次化的软件集成方式,提高了软件的可维护性,缩短了开发周期;该系统实现了外场0.1~18GHz（内场0.1~38GHz）频带下,对雷达目标的超宽带散射特性测量和天线电参数的超宽带测量,并同时进行数据的接收、处理、分析和显示;该系统丰富的功能为雷达目标的RCS测量提供了强大的测试平台。     

低频噪声参数自动测量系统研制

本文介绍了利用CF-920信号分析仪组成的低频噪声自动测量系统.该系统可以实现:(1)En、In的噪声功率谱;(2)噪声系统NF随频率变化曲线;(3)NF图;(4)最佳工作参数等的丰富测量功能.具有精度高、测量速度快、结果可靠等优点.文末给出了测量与理论结果的对比.     

电力系统负荷频率控制LFC的小波神经网络辨识

建立了非线性的电力系统负荷频率控制LFC模型,利用递归NARMA模型的小波网络的实现方法对LFC模型进行了辨识,利用Akaike's的最终预测误差准则FPE和信息准则AIC,进行了隐层节点数目和反馈阶次的计算,理论和仿真表明辨识模型可取得较好效果。     

Erroneous measurement detection in substation automation system using OLS based RBF neural network

With the development of communication and information technology over the past decades, Electronic Instrumental Transducer (EIT) and broadband communication network have been prevalent within Substation Automation System (SAS) and power utilities. Since mal-function of EIT and broadband communication network within SAS can produce dangerous erroneous measurements, the risk for the protection system to receive these erroneous measurements and thereafter to mis-operate increase. Pattern identification can be utilized to detect erroneous measurements. In order to achieve satisfying pattern identification precision within time limit imposed by protection systems, Radial Basis Function Neural Network (RBFNN) are investigated in the paper. Orthogonal Least Square (OLS) learning algorithm is used to prune network scale in order to mitigate contradictory requirements of high precision and low time delay. Simulation results show OLS based RBFNN can achieve satisfying performance within limited time.     

控制Logistic系统的自适应Chebyshev多项式神经网络算法

提出了一种基于自适应Chebyshev多项式神经网络(ACNN)的Logistic混沌系统控制算法。该算法采用Chebyshev正交多项式作为神经网络的激励函数,构建Logistic混沌系统的预测与控制模型。为了保证算法的稳定性,提出和证明了收敛定理,并利用自适应学习率算法提高神经网络的学习效率和收敛速度。通过采用自适应Chebyshev神经网络直接学习Logistic混沌系统的动态特性,并对系统实施目标函数控制。实验仿真结果表明,该算法在Logistic混沌系统有外部干扰的情况下仍能对其进行有效控制,网络学习时间为0.178 s,训练步长为10,均方误差达到1.15×10-4,与其他常见算法相比具有计算量小、速度快、精度高和网络结构简单等优点。     

忆阻神经网络在智能电网监控系统中的应用

随着能源危机的到来,智能电网技术成为世界各国所关注的重点。而与此同时,智能发电、输电、变配电、用电以及智能调度等各个环节所取得的技术性突破也为智能电网的大规模实现提供了可能。然而,智能电网结构复杂,电气设备分布广泛,应用需求多,这些特点都给电网运行的安全性和可靠性提出了挑战。针对智能电网的安全运行问题,提出了将忆阻器与人工神经网络相结合,构建出基于忆阻神经网络的智能电网运行状态监控系统,从而可以利用忆阻器所具有的记忆功能,节省人工神经网络的权值传输时间,提高神经网络的数据训练效率,保证了监控系统的及时性和有效性。     

Development of peak load forecasting system using neural networks and fuzzy theory

This paper presents a peak load forecasting system using multilayer neural networks and fuzzy theory. Electric load forecasting in power systems is a very important task from the perspective of reliability and economic operation. Daily peak load forecasting is one of the basic operations of generation scheduling for the following day. Therefore, many statistical methods have been developed and used for such forecasting even though it has been difficult to construct a proper functional model. The developed system is applied by neural network and fuzzy theory to forecast for daily, weekly and monthly peak load. The system consists of an engineering workstation (EWS) and a personal computer (PC). The EWS is for learning and data-bases, and the PC is for man-machine interface such as forecasting operation. The system has been used since June 1993. The result evaluated with an absolute mean error is 1.63 percent for 10 months. From the results shown here, the system applied by neural network and fuzzy theory has high validity.     

基于Elman型回馈神经网络的流域降雨-径流动态过程建模的研究

本文将Elman回馈型神经网络与水文系统的特点相结合 ,建立了流域降雨 -径流动态模型。文中用较简单的方法解决了神经网络模型难以直接描述水文系统前期状态的难题 ,并以福建沙县流域的降雨 -径流过程为研究对象 ,通过完整的分析 ,表明Elman型神经网络与水文系统概念相结合的动态过程模型是一种能够保证较高的预报准确性的模型 ,同时又具有良好的适应性、健壮型和外延性 ,显示出良好的应用前景。     

Power system transient stability margin estimation using neural networks

This paper proposes a methodology for estimating a normalized power system transient stability margin (ΔV_n) using multi-layered perceptron (MLP) neural network with a fast training approach. The nonlinear mapping relation between the ΔV_n and operating conditions of the power system is established using the MLP neural network. The potential energy boundary surface (PEBS) method along with a time-domain simulation technique is used to obtain the training set of the neural network. Results on the New England 10-machine 39-bus system demonstrate that the proposed method provides a fast and accurate tool to evaluate online power system transient stability with acceptable accuracy. In addition, based on the examination of generators rotor angles after faults, a method is presented to select the power system operating conditions that most effect the ΔV_n for each fault.     

基于神经网络非线性系统的故障诊断研究

提出了一种关于变工作点非线性系统故障诊断研究的新方法。将此类非线性系统用变参数线性系统表示,其中模型参数为可测量工作点及故障的函数。基于Hopfield神经网络,估计系统模型参数,引入“参考工作点”这一新概念,根据模糊聚类分析方法,确定故障种类。最后,在某位置伺服系统故障诊断研究中证实了这种方法的有效性。     

Convergence of threshold networks using their dissipative system model

A new dynamical energy system model representation is given for threshold networks. Inspired by the relation between stability and dissipativeness of dynamical systems, the convergence property of threshold networks is investigated. Using the energy function inherent within the given model a condition, namely the dissipativeness of the dynamical system, necessary and sufficient condition for the convergence of the threshold network to a fixed point, is given. Also, an easy to check inequality is stated to test the convergence of the threshold network. Copyright © 2000 John Wiley & Sons, Ltd.     

Nuclear reactor diagnostic system using genetic algorithm (GA)-trained neural networks

Several nuclear reactor diagnostic systems using neural networks have been proposed in recent years. Neural networks trained by backpropagation, the standard training algorithm, have certain problems such as local minima and long training times. In this paper, neural networks trained by genetic algorithms are used in a nuclear reactor diagnostic system to solve these problems. The system is tested by simulated data modeled on the experimental fast reactor JOYO, and two categories of abnormality (abnormal control rod vibration and abnormal coolant flow) are identified. The comparisons to networks trained by back-propagation also are discussed.