基于支持向量机的暂态稳定分类中的特征选择

特征选择是支持向量机(SVM)分类实现中非常重要的环节。针对传统方法进行特征选择的缺陷,提出了基于遗传算法的特征选择方法。综述和提出了支持向量机暂态稳定分类的初始特征;建立了IEEE16机86节点系统的暂态稳定分类初始特征样本集;利用主成分分析和遗传算法对维数较大的初始特征进行了有效降维;并通过因子负荷,完成了暂态稳定输入特征的选择;经过支持向量机分类器测试,显示选出的特征有很好的分类效果。     

基于支持向量机的高速铁路电能质量数据分类方法研究

针对电能质量监测系统的海量多特征数据信息,提出采用基于支持向量机的回归特征消去法进行特征选择,综合支持向量机对不同的电能质量特征集的分类正确率选取了最优特征集。以高速铁路电能质量数据为例,利用该方法对有无高铁负荷运行进行了分类研究。实验结果表明,所选出的特征集反映了高铁电能质量特点并具有很好的分类效果,证明了所提方法的可行性,为电能质量数据挖掘分类提供了一种思路和方法。     

基于遗传算法和支持向量机的特征子集选择方法

在模式分类系统中,往往需要从大量的特征中选择最优的特征子集,人工选择特征的方法往往费时费力,本文采用遗传算法(GA)对支持向量机进行封装的方法选择特征子集。首先使用遗传算法随机产生若干特征子集,通过选择、交叉和变异操作产生新的特征子集,经过若干代之后,得到最优的特征子集。在遗传算法中最重要的是适应度的确定,本文用支持向量机(SVM)作为分类器,为了避免出现“过拟和”,把特征子集的5阶交叉验证分类准确率和特征数量的联合函数作为适应度函数。对UCI机器学习库中SONAR和LED数据集进行实验,结果表明本方法可以有效滤除无关特征并提高分类准确率。     

基于蚁群优化算法的电力系统暂态稳定评估特征选择

暂态稳定评估的特征选择是一个典型的组合优化问题。针对该问题解的离散性特点,提出基于蚁群优化算法的特征选择方法。该方法以最小二乘支持向量机作为暂态稳定评估分类器,以分类错误率最低和特征选择比率最小为优化目标,通过二进制编码形式的蚁群优化算法实现特征的选择。这样能选择出计及分类器特性的最优特征子集,减少了特征维数,提高了分类正确率。通过对综合程序EPRI-36节点系统的仿真计算,验证了该方法的有效性。     

基于改进GSA-SVM算法的电能质量扰动分类方法

针对不同类型电能质量扰动信号分类准确率不高的问题,通过MATLAB/simulink搭建常见的9种不同的电能质量扰动信号的模型进行仿真分析,提出一种改进的万有引力搜索算法(improved gravitational search algorithm, IGSA)对支持向量机(support vector machine, SVM)的惩罚因子和核函数参数进行寻优的方法,通过优化SVM的惩罚因子和核函数参数,构建IGSA-SVM分类器,再把提取到的特征向量进行归一化之后输入到所构造好IGSA-SVM分类器中进行训练与分类。仿真结果表明,IGSA-SVM分类器的分类准确率比SVM和GSA-SVM这2种分类器都要好,可以实现对9种不同的电能质量扰动信号的快速准确分类,有利于解决实际的工程问题。     

电力系统暂态稳定概率评估方法

提出了一种基于蒙特卡罗-支持向量机的电力系统暂态稳定概率评估方法。首先构建了一组包含电力系统稳定和故障信息的原始特征,经特征选择降维后作为支持向量机的输入,在训练集上进行10折交叉验证,研究了4种支持向量机,其中径向基核支持向量机具有优良的评估性能;然后采用非序贯蒙特卡罗模拟方法选择随机因素,径向基核支持向量机加速暂态稳定评估过程,利用累计分类结果计算电力系统暂态不稳定概率。新英格兰39节点测试系统算例表明,该方法能大幅减少模拟时间,满足暂态稳定概率评估的精度要求。     

Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy.     

基于 KPCA-SVM 的表面肌电信号疲劳分类研究

为了提高手臂疲劳模型识别的准确率,本研究在常用时域、频域特征的基础上,引入了时频域、非线性和参数模型特征,提取3通道的表面肌电信号,构成特征集合.特征降维一般分为特征提取以及特征选择,分别采用特征提取中的主成分分析(PCA),核主成分分析(KPCA)方法以及特征选择中的互信息(MI)度量方法进行特征降维,采用支持向量机(SVM)和K近邻(KNN)作为分类器,通过3种降维方法分与SVM和KNN的不同组合构成疲劳分类模型.结果 表明,KPCA与SVM的组合模型对于疲劳的正确识别率最高达到99％,高于其他组合算法.     

基于相空间重构和支持向量机的电能扰动分类方法

电能扰动的分类需要信号特性提取和分类器构造2个阶段,文中采用相空间重构和支持向量机的组合,提出了一种全新的电能扰动信号的分类方法。首先利用相空间重构方法构造扰动信号轨迹,通过编码获得二进制轨迹图像。针对该图像定义了4类具有区别性的指标,以表征不同扰动类型的特性。然后将特性指标作为支持向量机分类器的输入矢量,实现自动分类识别。算例表明该方法计算量少,正确率高,所需训练样本少,可以有效分类识别电压暂降、电压瞬升、电压中断、脉冲振荡、谐波、闪变等6种电能扰动。     

结合随机森林和SVM的风机叶片结冰预测

风电领域里工作在严寒地区的风机结冰现象严重。材料、结构性能的变化以及低温环境引起的负荷变化威胁风机的发电和安全运行。文中提出结合随机森林和SVM的风机叶片结冰监测方法。主要采取递归特征消除随机森林的特征选择方法从原始风机数据集选择出有效特征,SVM对特征选择后的数据集进行训练,最后用Stacking结合策略融合SVM模型和随机森林模型。经试验结果表明,采取RFE-随机森林特征选择和SVM相结合的方法比未经过特征选择的SVM模型在分类精度上平均提高9.64%;采取Stacking结合策略融合SVM模型和随机森林模型,融合模型具有最好的准确率99.05%和泛化性。该方法可以实现对风机结冰有效预测且可理解性好,对风场操作人员维护风机具有指导意义。     

基于Couple熵的抑郁症相干性反馈指标提取

为探究抑郁症患者脑网络连通特性及其作为在线反馈指标的可行性。首先,采用对容积导体效应不敏感的相干性虚部(IC)构建脑网络,能够有效便捷的避免虚假连接影响。然后,提取具有显著性差异的IC值作为特征集,提出结合Couple熵(CE)和Relief过滤式特征选择方法优化特征集,结合特征与类、特征之间关系信息提高特征集质量。同时,根据自我参照脑网络模块整合特征集,构造在线反馈指标。最后,采用K最近邻(KNN)、支持向量机(SVM)分类器进行对比分析。结果发现,各频段内CE-Relief特征选择方法提取的特征集最小,且分类准确率均高于90%;Alpha频段IC值识别抑郁效果最好,分类准确率可达到100%;自我参照脑网络的前额区平均IC值分类能力在各频段内具有优势且稳定,分类准确率均高于80%。     

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.     

基于GA和ELM的电能质量扰动识别特征选择方法

电力系统中海量暂态扰动的分析与治理需要以高效准确的扰动分类为基础。现有扰动识别方法缺少合理的特征选择环节,分类器过于复杂,不能满足高效分类的需要。提出一种新的电能质量扰动特征选择方法。首先,对原始信号使用S变换进行预处理,提取具有代表性的25种扰动信号特征构建原始特征集合;然后,根据极限学习机识别准确率构造用于扰动特征选择的遗传算法适应度函数;最后,用遗传算法来进行迭代运算,确定最优特征集合。实验证明,新方法能够有效去除冗余特征,在保证分类准确率前提下,有效降低分类器复杂度,提高分类效率。     

Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.     

Classification of disturbances in hybrid DG system using modular PNN and SVM

This paper presents the classification of islanding and power quality (PQ) disturbances in grid-connected distributed generation (DG) based hybrid power system. The penetration of DG influences the PQ levels in the distribution networks. Islanding disturbances are separated out from the PQ disturbances based on the selection of suitable threshold value, at the initial stage of classification process. Further, the power quality disturbances are automatically classified into distinct classes based on feature extraction using S-transform followed by training of two classifiers, namely, modular probabilistic neural network (MPNN) and support vector machines (SVMs). Five different types of disturbances are considered for the classification problem. The study reveals that S-transform (ST) in association with MPNN and SVM can effectively detect and classify islanding and PQ disturbances. The proposed methodology uses features instead of real data set and thereby reduces the data size to classify disturbance signal without losing its original property. The accuracy and reliability of proposed classifier is also tested on signals contaminated with noise and PQ disturbances caused due to wind speed variation on an experimental prototype set-up.     

基于PMU和混合支持向量机网络的电力系统暂态稳定性分析

提出了一种基于粗糙集理论的混合网络模型,结合简单的计算将同步相量测量单元(PMU)获得的故障后短时间窗内各发电机的功角信息作为输入,首先利用粗糙集和自组织特征映射(SOFM)网络分别对原始输入进行特征提取和预分类,然后对那些不能直接利用SOFM网络进行稳定性判断的样本采用提取后的特征量并利用支持向量机(SVM)优良的统计特性进一步寻找其各自的最优分类面,以确保对所有样本进行正确分类。结合新英格兰10机系统的计算结果从预测精度和训练时间两方面对多种SVM模型进行了比较,结果表明了利用文中所提模型进行电力系统暂态稳定性分析的有效性,该模型可以提高训练效率以及分类的准确性。     

电能质量复合扰动分类识别

电能质量扰动的分类分为信号特征提取和分类器2个阶段,采用S变换和支持向量机构造电能质量复合扰动的分类识别方案.利用S变换进行扰动信号特征提取,构造支持向量机静态分类树,再通过基于Mercer核的聚类方法对静态分类树进行动态扩展,形成动态分类树,实现对复合扰动的识别.给出了电能质量复合扰动分类算法的4个步骤:构建静态分类树;用基于Mercer核的聚类方法进行聚类分析;构建动态分类树;对新发现的扰动确定其具体类型,并给其命名.算例表明该方法不仅可以有效分类识别电压突降、电压突升、电压中断、暂态振荡、电压尖峰、电压缺口和谐波等7种电能质量扰动,还可以识别由其组合而成的电能质量复合扰动.     

Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface

Although brain-computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classification. In this paper, we propose a hybrid algorithm to improve the classification success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classifier, we replace the LS-SVM classifier by a logistic regression classifier and a kernel logistic regression classifier, separately, with the same features extracted from the cross-correlation technique for the classification. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classification accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classifier provides an improvement compared to the logistic regression and kernel logistic regression classifiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.     

Support vector machines for transient stability analysis of large-scale power systems

The pattern recognition approach to transient stability analysis (TSA) has been presented as a promising tool for online application. This paper applies a recently introduced learning-based nonlinear classifier, the support vector machine (SVM), showing its suitability for TSA. It can be seen as a different approach to cope with the problem of high dimensionality. The high dimensionality of power systems has led to the development and implementation of feature selection techniques to make the application feasible in practice. SVMs' theoretical motivation is conceptually explained and they are tested with a 2684-bus Brazilian system. Aspects of model adequacy, training time, classification accuracy, and dimensionality reduction are discussed and compared to stability classifications provided by multilayer perceptrons.     

基于核熵成分分析的工业过程多类型故障诊断

核熵成分分析(KECA)特征提取过程中只保留了数据的最大瑞丽熵(Renyi)信息，没有充分利用数据的类别信息。由于监督学习算法线性判别分析(LDA)能够有效提取特征中的类别信息，因此提出KECA-LDA(KEDA)的特征提取方法。首先KECA依据最小Renyi熵损失策略对数据进行维数约简；然后在KECA特征空间使用LDA算法获取具有判别信息的低维特征并输入到支持向量机(SVM)分类器中，利用天牛须优化算法(BAS)得到最佳性能的SVM分类器，从而建立故障诊断模型。将KEDA-BAS-SVM方法应用于田纳西-伊斯曼化工过程(TE)进行仿真实验，结果表明：当采用基于距离测度的矩阵相似性优化确定KEDA中所选用的径向基函数(RBF)核参数时，相比KECA和LDA算法，KEDA特征提取后多类型故障诊断准确率达到99.7%,验证了KEDA-BAS-SVM在多类型故障诊断领域的优越性。