Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.     

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.     

On power quality indices and real time measurement

Power quality (PQ) indices are used to quantify the quality of the power supply and serve as the basis for comparing the negative impacts of different disturbances on power networks. To overcome the limitations and deficiencies of the practical applications of some power quality indices in common use, a set of three new indices, namely the fundamental frequency deviation ratio (FDR), waveform distortion ratio (WDR), and symmetrical components deviation ratio (SDR) are proposed in this paper to summarize different types of power disturbances in a comprehensive manner. As instantaneous quantities, these novel indices can reveal the time varying characteristics of power disturbances in real time. Hence, the new PQ indices can well accommodate practical waveform distortions in power networks, which may be caused by multiple types of time varying power disturbances. They can therefore be further used to evaluate both the effectiveness and dynamic responses of PQ mitigation equipment in practical applications. To fully realize the advantages of the new PQ indices, a novel Atom (transform kernel) based time frequency transform and its recursive algorithm are also proposed as the supporting measurement technique. The new Atom approach can continuously measure the instantaneous frequencies and amplitudes of signal components in a nonstationary disturbance waveform with high accuracy, and then update the new PQ indices at each sample. The effectiveness of the new PQ indices and the supporting measurement technique were ascertained using various PQ events, both simulated events and those recorded at an industrial site.     

基于自适应不完全S变换与LOO-KELM算法的复合电能质量扰动识别

针对电能质量复合扰动识别中特征提取效率低、分类器识别能力与学习速度无法同步提高的问题,提出一种基于自适应窗不完全S变换与留一交叉验证优化的核极限学习机(LOO-KELM)算法的复合电能质量扰动识别方法。首先根据选定的主频率点自适应调节S变换窗宽系数,提取具有高时频分辨率的59种电能质量(PQ)特征,再通过留一交叉验证寻找最小预测残差平方和,实现核极限学习机的输出权重优化,最后根据提取PQ特征集与优化后的核极限学习机实现复合扰动的识别与分类。仿真和实测结果表明,所提方法对不同噪声下的16类混合电能质量扰动均具有较高的分类精度,相较于现有复合电能质量识别方法,分类精度更高且训练时间更短。     

基于S变换和多级SVM的电能质量扰动检测识别

提出了一种基于S变换和多级支持向量机(SVMs)的电能质量扰动检测和识别方法.首先通过S变换对电能质量扰动信号进行时频分析,有效实现对各种扰动的检测输出.然后对检测输出进行时频特征提取,并通过一个N?1级支持向量机器分类器,最后实现N种电能质量扰动信号的分类识别.测试结果表明,该方法能有效识别参数大范围内随机变化的各种电能质量扰动,识别正确率高,且训练时间很短,实时性能好.     

Detection and classification of power quality disturbances using S-transform and modular neural network

This paper presents an S-transform based modular neural network (NN) classifier for recognition of power quality disturbances. The excellent time—frequency resolution characteristics of the S-transform makes it an attractive candidate for the analysis of power quality (PQ) disturbances under noisy condition and has the ability to detect the disturbance correctly. On the other hand, the performance of wavelet transform (WT) degrades while detecting and localizing the disturbances in the presence of noise. Features extracted by using the S-transform are applied to a modular NN for automatic classification of the PQ disturbances that solves a relatively complex problem by decomposing it into simpler subtasks. Modularity of neural network provides better classification, model complexity reduction and better learning capability, etc. Eleven types of PQ disturbances are considered for the classification. The simulation results show that the combination of the S-transform and a modular NN can effectively detect and classify different power quality disturbances.     

电能质量扰动信号的自适应去噪方法

有效地降低电能质量信号中的噪声,是做好电能质量信号检测、识别等工作的基础。为了克服一维电能质量信号降噪的难点问题,即有效地去除噪声并完整地保留奇异点的特征,对目前图像处理领域中针对高斯等噪声降噪性能最好的基于块匹配的三维变换域联合滤波(BM3D)算法进行了改进,提出一种电能质量扰动信号的自适应去噪新方法。该方法参数较少,无需估计噪声方差,也无需人为设定滤波阈值,而是通过自适应估算较为准确的阈值实现离散余弦变换(DCT)域的滤波。通过对电压中断、电压暂降、电压暂升、脉冲暂态、振荡暂态和谐波这6种常见的电能质量信号进行降噪仿真实验,并与应用较为广泛的小波阈值去噪法进行对比分析,最后应用于实际电能质量扰动数据的降噪,验证了所述算法的有效性。     

不相干约束项的判别字典表示电能质量扰动分类研究

针对稀疏表示电能质量扰动识别中判别字典学习的冗余性,提出一种具备精简性和不相干约束项的判别字典学习电能质量扰动分类方法。首先,将不同电能质量扰动样本训练获得子字典,公共字典和判别字典。接着,利用判别字典优化方法求解出降维测试信号的稀疏表示。最后,利用稀疏表示重构方法求解测试样本,由冗余残差最小值确定电能质量扰动信号的类型。不相干约束项的判别字典学习方法是在训练字典的过程中直接驱使字典具有判别性,获得更加精简且具有判别性的稀疏字典来提升最终的识别性能。实验结果表明8类电能质量扰动信号在40、30、20 d B信噪比递减时,平均扰动识别率有所降低但平均识别精度仍高达96%以上。仿真实验结果表明该方法能有效的对不同电能质量扰动进行识别并提高识别结果的精确度,并且不相干约束项的判别字典算法更优化于判别字典学习算法的分类识别性能。     

Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm

A novel approach for power quality disturbance classification using Hidden Markov Model (HMM) and Wavelet Transform (WT) is proposed in this paper. The energy distributions of the signals are obtained by wavelet transform at each decomposition level which are then used for training HMM. The statistical parameters of the extracted disturbance features are used to initialize the HMM training matrices which maximize the classification accuracy. Fifteen different types of power quality disturbances are considered for training and evaluating the proposed method. The Dempster–Shafer algorithm is also used for improving the accuracy of classification. In addition, the effect of the noise is studied and the performance of a denoising method is also investigated. Simulation results in a 34-bus distribution system verify the performance and reliability of the proposed approach. Also the results obtained for practical data prove the capability of the proposed method for implementing in experimental systems.     

基于稀疏自动编码器深度神经网络的电能质量扰动分类方法

针对智能电网日益突出的电能质量扰动问题,提出了一种基于稀疏自动编码器(SAE)深度神经网络的电能质量扰动分类方法。利用SAE对电能质量扰动原始数据进行无监督特征学习,自动提取数据特征的稀疏特征表达;通过堆栈式稀疏自动编码器(SSAE)进行逐层学习,获得电能质量扰动数据的深层次特征;将其连接到softmax分类器进行微调训练,并输出电能质量扰动事件分类结果。利用已添加高斯白噪声的数据对SSAE进行训练,以提高其特征表达的抗噪声能力。仿真结果表明,所提方法能够准确地识别包含2种复合扰动在内的9种电能质量扰动信号,并且具有很好的鲁棒性。     

基于不完全S变换与梯度提升树的电能质量复合扰动识别

针对电能质量复合扰动中特征选择困难和分类准确率不高的问题,提出基于不完全S变换和梯度提升树的特征选择和分类器构建方法。首先通过选取特定频率的不完全S变换得到扰动的时频矩阵。再从时频矩阵中提取53种原始特征量,并基于梯度提升树对各个特征的重要性进行度量,选取重要特征。最后根据选取的特征集训练和构建梯度提升树,得到扰动分类器。仿真实验表明,对于包括8种复合扰动在内的共17种扰动类型,该方法的分类准确率高于CART决策树、随机森林(RF)、多层感知机(MLP)等现有方法。不同噪声条件下的分类结果表明,该方法具有良好的抗噪性能和算法鲁棒性,展现出良好的应用前景。     

基于小波变换和分形理论的电能质量扰动监控系统

介绍了实时电能质量扰动监控系统的结构,详细说明了该系统硬件和软件各个构成模块的工作原理。为实现实时在线监控电能质量扰动,首先需检测出扰动信号,然后进行分析处理。在扰动检测模块中,采用自适应线性神经元实现了对各种扰动的检测,将检测出的扰动信号送入分类模块,采用离散小波多分辨率分析提取不同尺度下的能量分布特征,同时采用分形几何学提取局部方差维数,将二者结合共同构成扰动信号的特征矢量。将提取的特征矢量送入概率神经网络实现网络训练和扰动分类。通过模拟数据测试,该系统的分类率可达到90%。另外,该系统是在CAN总线变电站自动化系统上实现的,通过调整数据的传输格式也可将其应用到其它传输平台的变电站,实现对电能质量扰动的监控。     

基于改进多层前馈神经网络的电能质量扰动分类

电能质量扰动分类是电能质量控制的重要工作之一,主要工作包括信号特征提取和分类器构造两个阶段。采用S变换与改进的多层前馈神经网络相结合,提出一种新的电能质量扰动分类方法。首先利用S变换对原始数据进行处理,提取具有代表性的4类典型特征以表征不同种类的扰动类型的特性,之后使用拟牛顿法和自适应因子改进传统的多层前馈神经网络,将特征作为改进的多层前馈神经网络的输入向量,实现自动的分类识别。实验表明,新方法减少了噪声对分类准确率的影响,学习能力强,能够有效的识别电压暂降、电压瞬升、电压中断、暂态震荡、谐波等5种电能扰动。     

基于一维卷积神经网络多任务学习的电能质量扰动识别方法

传统电能质量识别需要先用信号处理技术提取信号特征,且已有的多分类和多标签分类建模方式没有很好地反映多重扰动和单扰动之间的标签关联性,使得复合扰动分类的鲁棒性和抗噪性能不理想。针对这些问题,提出了一种基于多任务学习的一维卷积神经网络模型来识别各种电能质量扰动。此结构去除了传统方法的信号特征提取阶段,将扰动分类任务分成四个子任务,设计了相应的标签编码方案,最后输出一个10维标签向量完成多任务分类。仿真结果表明,该方法在不同信噪比时均具有较好的识别准确率,表明此模型具有较强的鲁棒性和抗噪声能力。同时,多任务分类相比One-hot多分类和多标签分类准确率更高,表明了该建模方式的有效性。     

An effective wavelet-based feature extraction method for classification of power quality disturbance signals

This paper presents a wavelet norm entropy-based effective feature extraction method for power quality (PQ) disturbance classification problem. The disturbance classification schema is performed with wavelet-neural network (WNN). It performs a feature extraction and a classification algorithm composed of a wavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The PQ signals used in this study are seven types. The performance of this classifier is evaluated by using total 2800 PQ disturbance signals which are generated the based model. The classification performance of different wavelet family for the proposed algorithm is tested. Sensitivity of WNN under different noise conditions which are different levels of noises with the signal to noise ratio is investigated. The rate of average correct classification is about 92.5% for the different PQ disturbance signals under noise conditions.     

基于S变换和最小二乘支持向量机的电能质量扰动识别

采用S变换和最小二乘支持向量机相结合,构建了一种电能质量扰动识别的新方法.首先利用S变换对电能质量扰动信号进行时频分解;然后,从扰动信号S变换的结果中,提取扰动信号的特征向量,组成训练样本和测试样本;最后,使用最小输出编码的最小二乘支持向量机对扰动信号进行训练,实现电能质量扰动信号自动分类和识别.仿真结果表明,该方法识别准确率高,抗噪能力强,且训练时间很短,适用于电能质量扰动辨识系统.     

A de-noising scheme for enhancing wavelet-based power qualitymonitoring system

By means of the wavelet transform (WT), a power quality (PQ) monitoring system could easily and correctly detect and localize the disturbances in the power systems. However, the signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The performance of the WT in detecting the disturbance would be greatly degraded, due to the difficulty of distinguishing the noises and the disturbances. To enhance the capability of the WT-based PQ monitoring system, this paper proposes a de-noising approach to detection of transient disturbances in a noisy environment. In the proposed de-noising approach, a threshold of eliminating the influences of noises is determined adaptively according to the background noises. The abilities of the WT in detecting and localizing the disturbances can hence be restored. To test the effectiveness of the developed de-noising scheme, employed were diverse data obtained from the EMTP/ATP programs for the main transient disturbances in the power systems as well as from actual field tests. Using the approach proposed in this paper, remarkable efficiency of monitoring the PQ problems and high tolerance to the noises are approved     

一种实时电能质量扰动分类方法

针对电能质量扰动实时分类的需求,提出了一种基于强跟踪滤波器和极限学习机的电能质量扰动分类方法。强跟踪滤波器通过引入渐消因子矩阵克服了扩展卡尔曼滤波器的易发散的问题。强跟踪滤波器不仅可以检测扰动幅值而且还可以提供渐消因子作为特征量,以此识别暂态扰动和谐波。该方法提出使用基波幅值最大值、最小值、波动次数和渐消因子频度均值四个特征量组成特征向量作为极限学习机分类模型的训练样本;最后将分类器用于电能质量扰动识别。为了提高极限学习机分类精度,提出了对少量边界错分样本的类别进行校正的规则校正法。仿真表明改进后的方法能够识别包括两种复合扰动在内的10种电能质量扰动信号,并具有良好抗噪性。与随机梯度下降反向传播方法、最小二乘支持向量机和序贯极限学习机相比,该方法训练和分类速度快,分类准确率高,适合于在线应用。     

Fuzzy classifiers for power quality events analysis

The present paper proposes the design of a tool to quantify power quality (PQ) parameters using wavelets and fuzzy sets theory. The tool merges the best characteristics of these two theories in establishing a method to analyze PQ events. The proposed method addresses two issues, such as selection of discriminative features and classifies event classes with minimum error. Wavelet features (WF) of PQ events are extracted using wavelet transform (WT) and fuzzy classifiers classify events using these features. Often the captured signals are corrupted by noise. Also the non-linear and non-stationary behavior of PQ events make the detection and classification tasks more cumbersome. WT has been proven an effective tool for detecting and classifying these. We exploited WT for noise removal to make the task of detection and/or localization of events simpler. In the proposed approach of event classification, fuzzy product aggregation reasoning rule based method has been used. Varieties of PQ events including voltage sag, swell, momentary interruption, notch, oscillatory transient and spikes are considered for performance analysis. Comparative simulation studies revealed the superiority of proposed method compared to WF based fuzzy explicit, fuzzy k-nearest neighbor and fuzzy maximum likelihood classifiers under noisy environment.     

一种电能质量扰动信号的分层识别新方法

为了快速准确地对电能质量扰动进行分类和识别,结合时域和频域分析方法,提出了一种新的电能质量分层分类识别新方法.该方法由基波和扰动分离模块、扰动时间特征提取分类模块等多个功能模块构成,通过将dq变换、广义形态滤波、傅里叶变换等计算简单的信号分析方法相结 合,逐层提取出幅值、扰动时间、扰动频域奇异熵等特征并对其进行分类,最后依据各层分类结果对信号的扰动类型进行综合识别.对7种常见的单一电能质量扰动及部分混合电能质量扰动仿真分析表明,所提出的方法有较好的分类识别效果.