A real-time classification method of power quality disturbances

This paper describes a real-time classification method of power quality (PQ) disturbances. With an acceptable computation burden, both the elementary parameters of the power signal and the types of the disturbances in the power signal are obtained easily. The proposed method addresses the selection of discriminative features for detection and classification of PQ disturbances. Five distinguished time-frequency statistical features of PQ disturbances are extracted using RMS (root-mean-square) method and discrete Fourier transform (DFT). Using a rule-based decision tree (RBDT), the nine types of PQ disturbances can be recognized easily and there is no need to use other complicated classifiers. Finally, the proposed method is tested using the simulated waveforms. And some preliminary experimental results of the accuracy characterization of an initial development instrument are reported. The simulation and application results validate the accuracy and efficiency of the proposed method.     

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.     

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.     

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。     

基于Teager能量算子的电能质量扰动实时检测方法

建立一个有效的电能质量监控系统的关键,首先在于实现对扰动的快速、准确检测.通过引入Teager能量算子(TEO),提出基于TEO的实时检测方法.由于Teager能量算子只需要对被测波形相邻的三个采样点进行两次乘法和一次加法运算,使得所提算法快速、简洁,具有优良的时间分辨率,能实时跟踪被测信号波形变化.仿真和实验结果表明,所提算法能够准确、迅速地检测和定位电能质量扰动的发生,具有优良的检测效果.     

A new method for classification and characterization of voltage sags

This paper presents new software developments related to sag classification and characterization. A new fuzzy rule based algorithm for classifying the types of voltage sags is proposed. The voltage sags are categorized into three types, i.e. sags due to the faults, large motor starting, or due to interaction between motor operation and faults. Three distinctive features of sag waveforms are defined and extracted first. Then a fuzzy logic based inference engine utilizing these features as inputs is implemented for decision making. Also presented are the characterization methods and suggested monitoring parameters for each of the three types of sags. Finally the application of the proposed characterization approaches for the equipment sensitivity study is illustrated. The results of case studies are reported. The presented approach has been implemented in MATLAB.     

基于数学形态学和网格分形的电能质量扰动检测及定位

提出一种基于数学形态学和网格分形的电能质量扰动检测及定位方法。首先利用数学形态学理论构造一种多结构并行复合形态滤波器对扰动波形进行预处理，以滤除信号中的随机、脉冲等多种噪声；然后对滤波后的波形，根据网格的变化规律进行分析，提出一种简单快捷的奇异性检测判据，以准确快速地检测出扰动并进行时间定位。分别用电压骤降、电压骤升、谐波及其组合扰动对所提方法进行验证。数字仿真结果证实了其正确性和有效性。     

基于广义S变换的短时电能质量扰动的模糊模式识别

针对电能质量的短时扰动的分类问题,提出了一种基于广义S变换和模糊模式识别的短时电能质量的分类方法。先对扰动信号作广义S变换得到模时频矩阵,再从该矩阵中提取4种统计量特征值,然后利用模糊模式识别方法的最大隶属度原则对扰动信号进行归类,从而实现对短时电能质量扰动信号的自动分类。仿真测试结果表明,该方法识别正确率高且对噪声不敏感,适用于实际应用。     

Hybrid demodulation concept and harmonic analysis for single/multiple power quality events detection and classification

A novel real-time analysis of power quality (PQ) events has been presented using amplitude and frequency demodulation concepts. The earlier techniques were analyzing the few cycles of the power signal based upon wavelets, having the computational complexity of the order O(n²). In the proposed method, PQ events can be considered as similar to various modulating signal forms. In this paper, the concept of demodulation has been used to separate various single/multiple event patterns and MUSIC harmonics algorithm has been used to detect the presence of the various harmonics. These techniques have been well tested on transient, sag, swell harmonics and their combinations in real-time. Fuzzy classifiers have been used for the classification of PQ events from the knowledge base, obtained from amplitude demodulation, frequency demodulation and MUSIC harmonic algorithm. It is concluded from the confusion tables that the efficiency of single/multiple PQ events recognition of fuzzy product aggregation reasoning rule (FPARR) classifier is higher.     

Classification of power quality disturbances using non-linear dimension reduction

The proposed technique is different from others in respect that it is based on the concept of local non-linear relation and uses non-linear fuzzy functions to extract the feature-specific data. To extract any change during change in the patterns of power quality (PQ) events, non-linear Gaussian functions have been used which results in the formation of fuzzy lattices. The fuzzy lattices have been expressed in the form of Schrödinger equation to find the kinetic energy (KE) used corresponding to any change occurring in the power quality disturbances. Finally, the KE value embedded in two-dimension space has been used to distinguish PQ events. The method is applied to classify the various PQ events such as transient, sag, swell and harmonics and results are simulated using MATLAB version 7.3. The simulated results validate that the proposed algorithm can efficiently distinguish the PQ events in a single cycle and work perfectly in real time.     

Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system

This paper presents a hybrid technique for characterizing power quality (PQ) disturbances. The hybrid technique is based on Kalman filter for extracting three parameters (amplitude, slope of amplitude, harmonic indication) from the captured distorted waveform. Discrete wavelet transform (DWT) is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The three parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform. This hybrid technique can classify two simultaneous PQ events such as sag and harmonic or swell and harmonic. Several simulation and experimental data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.     

An adaptive PID stabilizer for power systems using fuzzy logic

A new gain scheduling PID stabilizer is designed for excitation control of power systems using fuzzy logic. The parameters of the proposed stabilizer are tuned on-line using a fuzzy rule base and a fuzzy inferencing mechanism for manipulating the speed error and its derivative. Although the new gain scheduled stabilizer does not have an apparent structure of PID controllers, fuzzy logic based controllers may be considered as nonlinear PID controllers, whose parameters can be determined on-line based on the error signal and their time derivative or difference. The new power system stabilizer is applied to single and multimachine power systems subject to various transient disturbances including faults. The superior performance of this stabilizer in comparison to the conventional fixed gain stabilizer proves the efficacy of this new approach.     

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.     

应用原子分解的电能质量扰动信号特征提取方法

提出一种应用原子分解实现的电能质量扰动信号特征提取方法.该方法以Gabor原子库和匹配追踪算法为基础,从扰动信号中迭代求取Gabor原子成分,再将Gabor原子转化为衰减正弦量原子,获得电能质量信号中各种扰动成分参量化的原子解析表示.用初始残余能量的阈值作为原子分解迭代终止条件,以改善特征提取效果.该方法可准确定量地提取各扰动成分的起止时刻、幅值、频率和变化规律等扰动特征,适用于暂态扰动、稳态扰动和多重扰动.算例分析验证了所提出的方法的有效性.     

基于分段改进S变换的复合电能质量扰动识别

针对存在多种单一电能质量扰动的复合扰动分类识别问题,提出了一种基于分段改进S变换和RBF神经网络相结合的复合电能质量扰动识别新方法。首先对离散S变换进行了分段改进,将时域分辨率和频域分辨率进行分段处理,通过分析改进S变换得到的模时频矩阵,绘制了能够反映扰动信号不同突变参数的特性曲线。其次利用统计方法优化计算提取了10种用于模式识别的特征量,并用局部逼近的RBF神经网络设计了分类器对提取的特征样本进行训练和分类,最后在不同噪声环境下对5种单一扰动及谐波+电压暂降、电压暂降+闪变等6类复合电能质量扰动的分类识别进行了仿真验证。仿真结果表明,该方案时频处理、分类能力和学习速度等方面均优于普通改进S变换+全局逼近网络的方法,且鲁棒性强,能准确识别多种单一扰动及两种扰动同时存在的复合电能质量扰动。     

Classification of power system disturbances using linear Kalman filter and fuzzy-expert system

Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. This paper presents a new approach for power system disturbances identification and classification. The concept of linear Kalman filter together with discrete wavelet transform (DWT) is used to extract two parameters; the amplitude and the slope from the captured voltage or current waveform. DWT is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The two parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to identify the class to which the waveform belongs. To prove the ability of the new approach for classifying power system disturbances, detailed digital simulation and experimental results involving various types of power quality events are presented. The results depict that the proposed technique has the ability to accurately identify and classify PQ disturbances.     

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.     

基于模糊滤波和Prony算法的低频振荡模式在线辨识方法

考虑到Prony算法对输入信号要求较高、对分析数据的噪声非常敏感,提出一种模糊滤波和Prony算法相结合的电力系统在线低频振荡模式的辨识方法。该方法以广域测量信号作为输入,通过简单的模糊逻辑推理快速对输入信号进行滤波,利用Prony算法对滤波后的数字信号进行分析后在线获得电力系统低频振荡的模式。以华中电网支路302245上的有功功率振荡分析为例,通过对模糊滤波前后的输入信号进行比较以及对传统Prony算法和考虑模糊滤波的Prony算法分别进行低频振荡模式辨识的比较,表明了前置滤波的重要性以及所提出的方法能相对精确地进行振荡模式辨识,验证了其有效性。     

Design of PSO‐based fuzzy logic controller for single axis magnetic levitation system

In this paper, a particle swarm optimization (PSO) method is proposed to design an optimal robust fuzzy logic controller (FLC). The objective of this paper is to design a nonlinear optimal robust controller for the single axis magnetic levitation system with high accuracy. PSO algorithm is applied to search globally optimal parameters of FLCs. Three different FLCs are designed. First, proportional derivative (PD)‐like FLC. Second, the FLC is based on the PSO algorithm to find the optimal range of the eight linguistic membership functions (FLC1 with PSO algorithm). Finally, the FLC is based on the PSO algorithm to find the optimal range and shape of the four linguistic membership functions (FLC2 with PSO algorithm). The performances of three different FLCs are compared. Simulation results show that PSO‐based optimal FLCs find the optimal range and shape of the four linguistic membership functions and achieved better performance than the other proposed controllers, minimizing 48 fuzzy rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.