基于Elman神经网络的负荷预测研究

电力系统负荷预测是电力生产部门的重要工作之一,其负荷变化具有明显的周期性,文章采用Elman神经网络与BP神经网络建立模型,提出了一种基于神经网络的负荷预测方法.对某电网实际历史数据进行仿真预测,经研究发现,Elman模型具有收敛速度快、预测精度高的特点,同时表明利用Elman回归神经网络建模对莱电网负荷进行预测是完全可行的,在负荷预测领域有着较好的应用前景.     

基于Elman神经网络的高压电缆导体温度动态计算方法

导体温度作为运行电缆的关键状态参数,是影响电缆载流能力、绝缘性能的重要因素。针对传统方法难以克服电缆本身物性参数和外部环境变化影响的局限,构建了以电缆运行电流和电缆实时外表面温度为输入,以导体温度为输出的Elman神经网络模型,并引入粒子群算法优化网络的初始权值和阈值。最后,提出了具体的电缆导体温度动态计算方法,通过设计不同工况下电缆温升实验,进而验证了所提出方法的有效性。实验结果表明,所提方法的计算准确度可以不受电缆物性参数、负荷变化方式及外部环境变化的影响,有助于实现电缆导体温度的实时监测。     

基于改进Elman神经网络的风速预测

指出风速预测对并网风力发电系统的运行有重要意义。为提高风速预测精度,提出了一种基于改进的Elman神经网络风速预测方法,利用误差反向传播的方法来确定反馈增益γ值。分别采用改进Elman神经网络与BP神经网络建立模型,对实际历史风速数据进行仿真预测。利用风电厂实际数据验证,并阐述了仿真结果。     

基于Archimedean Copula函数的流域降雨-径流关系变异分析

降雨径流关系是工程水文学领域中一个重要的应用问题,利用Copula函数在捕捉多变量间相依结构的灵活性这一优点,引入Archimedean Copula函数构建流域降雨-径流联合分布,诊断流域降雨-径流关系变异。以泾河流域为例,确定了降雨、径流分别服从Gumbel分布与对数正态分布,Copula函数检验结果表明,泾河流域降雨径流关系变异发生在1996年,并以降雨-径流关系变异点为分割点,将降雨、径流序列分割为1960~1996与1997~2010年两个阶段,分析表明两个时段降雨-径流关系发生较大变化,相同降雨情况下1997~2010年产流量相对1960~1996年减小30%以上,年降水量越小,产流量减小幅度越大,当年降水量为500mm时,减小幅度达到37.59%,并结合人类活动对照等分析,验证了Copula函数诊断流域降雨径流关系变异结果的合理性与准确性。     

基于Elman神经网络的传感器故障诊断研究

针对传统的传感器故障诊断技术的不足,提出一种基于Elman神经网络的故障诊断方法,建立了Elman网络故障诊断模型,利用小波包分解方法获取用于训练神经网络的特征能量谱,对所建立的模型进行训练。为了检验模型的实际诊断能力,以某动力系统管路流量传感器的4种典型故障诊断为例进行仿真实验,并和标准BP神经网络的诊断结果进行对比。仿真结果表明:基于Elman神经网络的故障诊断速度更快、准确率更高、泛化能力更强,验证了所提出方法的实用性和有效性。     

基于地理信息的SCS模型及其在黄土高原典型流域降雨-径流关系中的应用

基于土地利用、土壤类型等信息数据和流域水文、气象资料,应用美国农业部水土保持局研制的小流域设计洪水模型-SCS模型对王东沟流域径流过程进行了模拟.按照集水区自然分水线划分流域子单元,并提出了适合该流域产流计算的CN值表.结果表明,模型所模拟的径流过程与实测径流过程具有较好的一致性,模拟精度在75%以上,说明模型在参数的确定上是较为合理的,可以应用于黄土高原典型流域.为建立分布式水文模型对流域进行生态水文综合评价提供了科学方法.     

基于Elman神经网络的风力发电机组齿轮箱故障诊断研究

风力发电机组齿轮箱是用于改变转速和传递动力的部件,易于发生故障的一个装置,且其运行状态对风力发电机的工作性能有很大的影响,故对风力发电机组齿轮箱故障进行诊断有实际意义。Elman神经网络可以降低网络对参数调整的敏感性,有效抑制局部极小值的出现,仿真结果显示,Elman神经网络能识别出风力发电机组齿轮箱故障类型,该诊断方法效果较好。     

神经网络径流预报模型中基于互信息的预报因子选择方法

神经网络在径流预报中得到了广泛应用并取得了良好效果,其关键问题之一是输入变量(预报因子)选择,但这一问题通常没有受到重视.本研究基于互信息的概念探讨了如何选择径流预报输入变量,并结合三峡工程建成前长江干流宜昌水文站的日径流预报进行了研究.结果表明,基于互信息能够有效地判断待选预报因子(输入变量)与预报变量之间的相互关系...     

基于Elman神经网络的甘肃电网负荷预测模型

为提高甘肃电网负荷预测精度,提出了一种基于神经网络的负荷预测方法。针对甘肃电力系统负荷数据的非线性和动态特性,在多层前向BP网络中引入特殊关联层,形成有“记忆”能力的Elman神经网络,从而可以映射系统的非线性和动态特性。在网络训练算法中,采用自适应学习速率动量梯度下降反向传播算法,显著提高了网络的训练速率,有效地抑制了网络陷入局部极小点。文中分别采用El-man神经网络与BP神经网络建立模型,对甘肃电网实际历史数据进行仿真预测,经分析比较,证明前者具有收敛速度快、预测精度高的特点。这表明利用Elman回归神经网络建模对甘肃电网负荷进行预测是可行的,能有效提高负荷预测精度,在负荷预测领域有着较好的应用前景。     

线性响应模型与神经网络联合模拟降雨～径流过程

本文将简单线性（黑箱）模型与神经网络联合起来对降雨～径流过程进行了模拟,在早期大多数有关降雨～径流的神经网络研究中,往往将前期径流作为模型输入的一部分,而本文利用线性模型对径流的估算值代替前期径流作为神经网络输入的一部分,从而为资料稀缺流域降雨～径流关系的模拟提供了新的途径,研究结果表明该组合模型是可行的。     

基于多种递归神经网络的海岛水库径流预报

海岛地区径流量偏小甚至出现断流,会极大影响短期径流预报精度。对海岛地区供水水库多组入库径流时间序列,基于三种递归神经网络（RNN）建立了不同预报因子组合和预见期的径流预报模型,探讨了RNN模型在海岛地区短期水文预报中的适用性。以舟山岛水库群为例,说明研究方法的有效性。结果表明仅考虑径流时间序列信息的预报精度最差,而耦合气象预报信息可提高径流预报准确性;随着预见期的增加,简单RNN模型的信息融合能力有限,而具有复杂神经元结构的基本长短时记忆神经网络和门控循环单元预报效果稳定;RNN模型对于平稳时间序列数据模拟效果优于非平稳序列,而气象信息的引入和参数优选能够改善其在处理非平稳时间序列中的缺陷。     

Learning in dynamic neural networks using signal flow graphs

The paper presents the universal approach to the determination of the sensitivity functions for dynamic neural networks and its application in learning algorithms of adaptive networks. The method is based on the application of signal flow graph and specially defined graph adjoint to it. The method is equally applied to either feed‐forward or recurrent network structures. This paper is mainly concerned with neural network applications of the approach. Different kinds of dynamic neural networks are considered and discussed in the paper: the FIR dynamic multilayer perceptron (MLP), the cascade connection of dynamic MLPs as well as two non‐linear recurrent systems: the dynamic recurrent MLP network and ARMA recurrent network. The rule of sensitivity determination has been applied in practical learning of neural networks. Chosen results of numerical experiments concerning the application of this approach to the learning processes of recurrent neural networks are also given and discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

径流预报的极点对称模态分解-Elman网络模型

针对径流序列非线性、非平稳的特点,将极点对称模态分解(ESMD)方法与Elman神经网络模型相结合,建立了ESMD-Elman神经网络组合模型,并应用于长江上游干支流8站的年、月径流预报.首先利用ESMD方法将径流序列分解为各模态分量和趋势余项;然后利用Elman神经网络模型分别预测各平稳序列;最后加和重构得到最终预测...     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

Elman神经网络在非线性模拟电路故障诊断中的应用

Elman神经网络把隐层输出反馈回输入层,是一种动态递归神经网络,改进的Elman网络能逼近任意动态非线性系统。本文研究了Elman网络的结构和学习算法,并把改进的Elman网络应用于非线性模拟电路故障诊断激励信号参数优化中,以提高故障正确诊断率。首先用改进的Elman神经网络建立故障电路和无故障电路系统模型,然后用遗传算法搜索电路故障诊断的最佳激励信号参数。     

Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays.     

Voltage and reactive power control using recurrent neural networks

Many controllers using neural networks have been recently developed. The hybrid and direct types are two major categories of such controllers. While the first type tunes the parameters of the conventional controller by means of neural networks, the latter constructs the controller by learning the inverse dynamics of the control target. Electric power systems require voltage and reactive power (VQ) control to avoid voltage collapse. The conventional VQ control, however, meets this requirement unsatisfactorily because the control is only approximate. In this paper, we will propose a new algorithm for VQ control using recurrent neural networks (RNNs), which have the ability to deal with the system's controlled target by means of RNNs. Second, we will apply this algorithm to VQ control. We will call this controller “neuroVQC.” Finally, the usefulness of the neuroVQC will be shown by comparison with the conventional VQ controller.     

进化Elman神经网络在实时数据预测中的应用

为了提高电站实时数据的准确性,提出了一种利用改进粒子群算法进化Elman神经网络的动态系统实时数据预测方法.改进粒子群算法中,根据群体早熟收敛程度和当前最优解的大小对部分不活跃粒子进行变异,增强了算法跳出局部最优解的能力.利用改进的粒子群算法训练Elman神经网络权值和自反馈增益因子,有效地解决了梯度下降法训练网络权值...     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.