电动车蓄电池荷电状态估计的支持向量机方法

结合电动车蓄电池容量判断问题,将支持向量机方法用于蓄电池荷电状态估计。针对蓄电池本身的非线性特性,使用核函数为非线性核的最小二乘支持向量机算法完成估计器的设计,得到了基于多项式核函数和径向基核函数的估计器。通过实验分析了两种核函数对估计器性能的影响。从实际应用出发,分析了如何合理简化估计器模型的复杂性。结果表明基于多项式核函数的估计器精度较低,但应用过程简单;基于径向基核函数的估计器精度较高,但其应用时需要存储部分训练数据,增加了应用的复杂性。具体应用可以根据实际环境,合理选择核函数,提高估计器的综合性能。     

最小二乘小波支持向量机在非线性控制中的应用

结合小波技术和支持向量机,提出了一种基于多维允许小波核的最小二乘小波支持向量机,其小波核函数具有近似正交和适用于信号局部分析的特点。同时,给出了一种有效求解最小二乘小波支持向量机的Cholesky分解算法。将最小二乘小波支持向量机应用在非线性系统的自适应控制上,仿真结果表明,与最小二乘支持向量机、多层前向神经网络或模糊逻辑系统相比,最小二乘小波支持向量机均能给出较好的性能,显示出快速而稳定的学习速度,而且在相同条件下,最小二乘小波支持向量机比最小二乘支持向量机的逼近精确度提高了一个数量级。所提出的用于非线性动态系统自适应控制的最小二乘小波支持向量机方法具有效性和实用性。     

配电网谐波源定位的支持向量机估计算法

使用一种简化的方法确定谐波监测点的位置。该方法基于电路网络拓扑分析,通过计算母线谐波电压相对注入谐波电流变化的敏感因子,确定谐波监测点的位置;利用谐波监测点的实时数据,建立基于支持向量机算法的谐波源定位估计器,实现谐波源的位置估计。谐波源定位估计器通过基于支持向量机算法进行设计。对线型估计器、基于多项式核函数以及径向基核函数的估计器进行了比较,实验结果表明,基于径向基核函数的非线性支持向量机估计器具有更高的估计精度,且能够较准确地判定谐波源的位置。     

LS-SVM在通信电源蓄电池SOC估计中的应用

结合统计学习理论的方法,将最小二乘支撑向量机(LS-SVM)用于通信电源中蓄电池的荷电状态(SOC)检测。选定径向基核函数为支撑向量机算法的核函数,并选取矩阵分块求逆的方法改进最小二乘支撑向量机的算法,在此基础上建立了蓄电池荷电状态估计的模型。通过仿真实验验证了该算法具有较好的SOC估计效果,对于实际应用,可以选择合理的剪辑标准,从而得到更好的估计效果。     

基于网格搜索优化LS_SVM蓄电池SOC估测

依据最小二乘支持向量机(LS_SVM)的基本理论,针对蓄电池荷电状态(state of charge,SOC)随温度、电压、电流而变化的特点,建立基于LS-SVM支持向量机的蓄电池SOC估测模型。通过数据验证,比较不同核函数下的效果,利用网格搜索寻找最优参数。观察在最优参数和最优核函数下LS_SVM支持向量机的预测效果。结果表明,与其他算法相比,采用RBF核函数,并用网格搜索优化的LS_SVM模型精度较高,适合用在蓄电池的SOC估测上。     

基于最小二乘支持向量机的锅炉氮氧化物排放量预测模型

为了降低氮氧化物排放量,建立了基于最小二成支持向量机的氮氧化物排放量预测模型,并用差分算法优化最小二成支持向量机的参数。为了表明所提算法的有效性,利用BP神经网络、多层感知器(MLP)和偏最小二乘法(PLS)进行对比,试验结果表明该算法具有很好的预测能力和稳定性。     

基于支持向量机的船舶电力负荷预测

船舶电力系统是一个独立的电力系统，需要根据准确的负荷预测来控制多台发电机组的运行。本文提出了一种基于支持向量机的船舶电力负荷短期预测方法。对某大型集装箱船舶在不同工况下的电力负荷数据，分别用基于径向基核函数的支持向量机方法、多层BP网络和RBF网络方法进行训练和预测计算，仿真结果表明支持向量机具有更高的预测精度，是船舶电力负荷预测的一种有效方法。     

电动车电池SOC估计的径向基函数神经网络方法

结合电池容量估计问题,将广义生长剪枝径向基函数神经网络方法用于判断电动车蓄电池的荷电状态.通过神经网络输入参数的选择,文章设计标准模型、递推模型和安时模型三种不同的估计模型.实验结果表明:估计模型经过训练后,可以通过,蓄电池的工作电压、工作电流和表面温度参数估计蓄电池的荷电状态实时值,其中安时模型的训练时间、估计精度、网络的规模较其他两种模型更为出色.同时,本文引入解耦卡尔曼滤波器算法,有效提高了广义生长剪枝径向基神经网络的训练速度,在保证精度的前提下,将模型的训练时间缩短了1/2.     

基于最小二乘SVM的混沌跳频通信码预测方法

作为一种用于解决诸如非线性分类,函数估计,密度估计一类问题有力的解决方法,支持向量机近来在其他许多基于核函数学习领域得到发展.基于混沌跳频码的动力系统特性,本文构建了一种支持向量机的预测模型,并采用最小二乘法对模型进行训练,最后通过L-K映射生成的跳频码序列对模型进行了验证,仿真结果表明这种高精度的支持向量机用于针对基于混沌映射的跳频码的预测,具有很低的均方误差和高相关系数良好性能.     

基于Morlet小波核多类支持向量机的故障诊断

故障诊断问题实质上是一个模式识别问题,即多分类问题.采用Morlet小波来构造支持向量机(Support Vector Machine, SVM)的核函数,Morlet小波核SVM比普通SVM具有更好的鲁棒性和更强的泛化能力.在一对一算法的基础上实现Morlet小波核多类支持向量机的故障诊断,并将此方法成功应用于电厂汽轮发电机组的故障诊断.实验仿真结果表明Morlet小波核多类SVM故障分类器比BP神经网络训练和测试速度快,且其分类精度在高斯噪声干扰下还保持100%,比BP神经网络高出11.8%.因此该方法能够快速而准确地对电厂汽轮发电机组的故障进行诊断,满足电力系统实时操作的要求.     

回溯搜索算法改进RBF算法的锂离子电池SOC估算研究

随着电动汽车的发展和应用,动力电池SOC估算的意义越来越重要,为了提高SOC估算的精度,本文在标准RBF网络模型的基础上提出了利用回溯搜索算法改进RBF神经网络模型。通过对锂电池模型中的目标函数进行优化求解,利用寻找最佳的目标权值和阈值提高RBF网络模型的SOC估算精度。最后搭建了实验仿真平台,对改进前后的算法SOC估算进行了仿真对比分析,实验结果证明了改进后RBF网络比标准RBF网络算法SOC估算精度更高,并把估算误差降低到2%以内,对锂离子电池有较好的估算精度,具有一定的理论研究意义。     

三种主流风电场功率预测算法适应性对比研究

风电的间歇性和波动性给风电功率预测带来了较大难度,而地形和气象上的复杂性使单一功率预测算法很难适应不同的风电场。以3种主流风电功率预测算法为研究对象进行比较研究,分别是遗传算法优化的BP神经网络(GA-BP)、径向基函数神经网络(RBF)和支持向量机(SVM),帮助研究人员针对不同风电场的地形和气候特征选择最适合的预测模型,从而提高短期功率预测精度。以中国地形和气候特征不同的3个风电场为例,从预测精度、计算效率、模型适应性3个角度对比分析3种模型在不同气候、不同地形条件下的适应性。结果表明,RBF和SVM预测效果整体优于GA-BP模型,但在不同季节、不同地形条件下3种模型各具优势,以月份为单位建立功率预测模型,可以提高短期功率预测精度。     

Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network,an OLS algorithm and AGA

A method to accurately estimate the State-of-Charge (SOC) for LiFePO₄ (LFB) batteries is urgently required, to address the issues associated with the increased use of LFP batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network, an orthogonal least-squares (OLS) algorithm and an adaptive genetic algorithm (AGA) to estimate the SOC in discharging condition. The OLS algorithm determines the optimal number of nodes in the hidden layer of the RBF neural network. With an optimal RBF neural network structure, the AGA is then used to tune the parameters of the RBF neural network, including the centers and widths of RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using LFP batteries under several different discharging conditions. The effectiveness of the proposed method is compared with the Coulomb integration method and a back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.     

Power transformer fault diagnosis based on dissolved gas analysis by support vector machine

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA). Support vector machine (SVM) is powerful for the problem with small sampling (small amounts of training data), nonlinear and high dimension (large amounts of input data). The standard IEC 60599 proposes two DGA methods which are the ratios and graphical representation. According the experimental data, for the same input data, these two methods give two different faults diagnosis results, what brings us to a problem. This paper investigates a novel extension method which consists in elaborating an input vector establishes by the combination of ratios and graphical representation to resolve this problem. SVM is applied to establish the power transformers faults classification and to choose the most appropriate gas signature between the DGA traditional methods and a novel extension method. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to illustrate the performance of proposed SVM models. Then, the multi-layer SVM classifier is trained with the training samples. Finally, the normal state and the six fault types of transformers are identified by the trained classifier. In comparison to the results obtained from the SVM, the proposed DGA method has been shown to possess superior performance in identifying the transformer fault type. The SVM approach is compared with other AI techniques (fuzzy logic, MLP and RBF neural network); the proposed method gives a good performance for transformers fault diagnosis. The test results indicate that the novel extension method and the SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification.     

支持向量回归在贮灰坝渗流监测中的应用

鉴于支持向量机在机器学习方面表现出的良好性能，提出了基于支持向量回归(SVR)算法的贮灰坝渗流监测模型。采用基于平行网格搜索的交叉验证法选择模型参数，避免了参数选择的盲目性、随意性，提高了预测精度。实例分析表明，该渗流监测模型与传统的神经网络（反向传播（BP）网络、径向基核函数（RBF）网络）模型相比，具有预测精度高、泛化能力强等优点，能够快速、准确地预测出指定位置的测压管水位，对贮灰坝水头预报和电厂的安全生产具有实用价值。     

RBF-SVM的核参数选择方法及其在故障诊断中的应用

基于核理论的SVM中,RBF核函数应用最广,是一个普适的核函数,但其参数的选择却没有固定方法。鉴于此,本文首先分析了现有核函数参数优选算法的不足;然后在SVM网络结构分类原理的基础上提出了基于数据最大方差-关联度准则的核参数选择算法,并结合粒子群算法建立了RBF核参数的自动优选流程。将其用于模拟电路故障诊断实验,证明了所提方法具有参数选择准确、简单快速等优点,优选得到的核参数提高了故障诊断率。     

基于主成分与粒子群算法的LS-SVM短期负荷预测

短期电力负荷预测对电力系统安全经济运行和国民经济发展具有重要意义。最小二乘支持向量机(Least square support vector machines,LS-SVM)在解决小样本、非线性问题中表现出许多特有的优势,该方法已成功应用在负荷预测领域。本文提出了一种基于主成分分析的支持向量机预测模型,运用主成分分析对历史数据进行主成分提取,消除输入的训练数据本身存在着大量的噪声和冗余,从处理后的数据提取LSSVM的训练样本,并利用改进的粒子群优化算法(Particle Swarm Optimization,PSO)以LSSVM中的参数作为粒子进行优化,进而提高训练速度和预测精度。最后,将该模型运用到短期电力负荷预测中,与经典的SVM和BP神经网络相比具有更好的泛化性能和预测精度。     

Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines

This paper presents a new approach for the classification of the power system disturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accuracy of these features are selected by a feature selection technique called as sequential forward selection. Thirdly, the best appropriate input vector for SVM classifier is rummaged. The input vector is started with the first best feature and incrementally added the chosen features. After the addition of each feature, the performance of the SVM is evaluated. The kernel and penalty parameters of the SVM are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. Finally, both the noisy and noiseless signals are applied to the classifier given above stages. Experimental results indicate that the proposed classifier is robust and has more high classification accuracy with regard to the other approaches in the literature for this problem.     

Classification and assessment of power system static security using decision tree and random forest classifiers

The power system static security classification and assessment is essential in order to identify the post‐contingency problems and take corrective measures and to protect the system from blackout. In this paper, application of two data mining classifiers have been proposed for the security classification and assessment of a multiclass security problem. To design the security problem, contingency analysis is carried out under N‐1 line outage, and static severity index (SSI) is computed, which is a function of the line overload and the voltage deviation using Newton–Raphson load flow method, considering the variable load and generating conditions. Corresponding to the computed values of SSI, the voltage, phase angle, Mega Volt Ampere line flow and so on, a 1 × 7 pattern vector is generated. The generated pattern vectors are used to design a multiclass security problem. The designed security pattern vectors are given as inputs to the decision tree (DT) and random forest (RF) model in order to classify the security status of the power system. The proposed classifiers are investigated on an IEEE 30‐bus test system. The classification accuracy of the DT and the RF are compared with state‐of‐the‐art classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM).The simulation results clearly indicate that the proposed DT and RF classifiers are more efficient, reliable, and out performs MLP, RBF, and SVM classifiers for the assessment of the security status of the power system. Hence, DT and RF classifiers are found to be suitable for online implementation. Copyright © 2015 John Wiley & Sons, Ltd.