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.     

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.     

基于小波和神经网络的电能质量辨识方法

本文提出一种新的基于小波和神经网络技术的电能质量辨识方法。对各种电能质量信号进行时域、频域和幅值分析,并从小波变换结果中提取与信号时域、频域和幅值量相关的几个特征量来表征不同电能质量信号。将这些特征量作为神经网络的输入可以实现电能质量的辨识。计算结果表明该方法的有效性和准确性。     

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.     

Novel power quality indices based on wavelet packet transform for non-stationary sinusoidal and non-sinusoidal disturbances

High power quality (PQ) level represents one of the main objectives towards smart grid. The currently used PQIs that are a measure of the PQ level are defined under the umbrella of the Fourier foundation that produces unrealistic results in case of non-stationary PQ disturbances. In order to accurately measure those indices, wavelet packet transform (WPT) is used in this paper to reformulate the recommended PQIs and hence benefiting from the WPT capabilities in accurately analyzing non-stationary waveforms and providing a uniform time–frequency sub-bands leading to reduced size of the data to be processed which is a necessity to facilitate the implementation of smart grid. Numerical examples’ results considering non-stationary waveforms prove the suitability of the WPT for PQIs measurement; also the results indicate that Daubechies 10 could be the best candidate wavelet basis function that could provide acceptable accuracy while requiring less number of wavelet coefficients and hence reducing the data size. Moreover, a new time–frequency overall and node crest factors are introduced in this paper. The new node crest factor is able to determine the node or the sub-band that is responsible for the largest impact which could not be achieved using traditional approaches.     

Study of a new method for power system transients classification based on wavelet entropy and neural network

The detection and classification of transient signals are widely applied in many fields of power system. The study of transient signal detection and classification is a sustaining focus of researchers as well as a difficult issue. There are still many problems needed to be solved in this area. Based on the wavelet transform (WT), the idea of entropy and weight coefficient is introduced, and the wavelet energy entropy (WEE) and wavelet entropy weight (WEW) are defined in this paper. The distribution picture of WEE and WEW along with scales are presented for the first time. PSCAD/EMTDC models for six types of transients, namely breaker switching, capacitor switching, short circuit fault, primary arc, lightning disturbance and lightning strike fault, are constructed. With WEE and WEW, the eigenvectors for the six transients are established and a model which uses the eigenvectors as the input of the BP (back-propagation) neural network is set up to realize the classification of these transients. The simulation has been executed based on a 500 kV transmission line model in China and the results show that feature extraction based on WEE and WEW can effectively discover the useful local features. With the help of neural network classifier, it has effective classifying result. This method is applicable in the power system.     

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。     

Detection and Classification of Voltage Disturbances in Electrical Power Systems using a Modified Euclidean ARTMAP Neural Network with Continuous Training

This article presents a method to detect and classify voltage disturbances in electric power distribution systems using a modified Euclidean ARTMAP neural network with continuous training. This decision-making tool accelerates the procedures to restore the normal operation conditions providing security, reliability, and profits to utilities. Furthermore, it allows the diagnosis system to adapt to changes from the constant evolution of the electric system. The voltage signals features or signatures are extracted using discrete wavelet transform, multiresolution analysis, and the energy concept. Results show that the proposed methodology is robust and efficient, providing a fast diagnosis process. The data set used to validate the proposal is obtained by simulations in a real distribution system using ATP software.     

电力系统谐波的检测分析方法的发展

介绍了电力系统目前已有的谐波检测方法,叙述了傅立叶变换、小波变换、瞬时无功功率理论和神经网络等谐波检测新方法的发展现状,讲述了目前已有的谐波检测实现技术,分析了谐波检测方法与实现技术的发展趋势。     

基于小波包分析的配电网故障选线新方法

分析了小电流接地系统中基于小波包分析的故障选线方法的优势和不足,提出了基于小波包分析的故障选线新方法。首先对采样的零序暂态电流信号做增强处理,然后利用小波包分析方法进行故障选线。基于上述原理研制了配电网接地选线装置,给出了装置的硬件结构及软件流程,试验结果表明,该方法有效地提高了小电流接地选线的准确性。     

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.     

小波包能量熵神经网络在电力系统故障诊断中的应用

提出了一种基于小波包能量熵神经网络的电力系统故障诊断方法。对采集到的故障后电压信号进行3层小波包分解,提取小波包能量熵,然后构造信号的小波包特征向量, 并以此向量作为故障样本对3层BP神经网络进行训练,实现智能化故障诊断。ATP和Matlab仿真结果表明该方法有效可行。     

小波熵理论及其在电力系统故障检测中的应用研究

电力系统暂态信号经小波变换后数据众多,且对故障的判别缺乏定量的手段,所以,挖掘和融合出一个或系列普适量来有效地检测电力系统故障或判别其稳定性至关重要。该文利用小波分析具有时频局部化特性和熵能对系统状态进行表征的特点,将小波分析和熵结合起来,定义了3种小波熵(小波能谱熵、小波时间熵、小波奇异熵),并给出其算法,揭示了这3种小波熵对系统故障表征的机理,对两种理论信号和基于PSCAD／EMTDC仿真的输电线路故障信号的分析表明,这3种小波熵能反映系统变化,且不受噪声干扰,能够有效地检测出电力系统故障。     

利用小波变换分析配电网电能质量扰动

小波变换具有良好的时频局部化特性,可以同时提取信号的时频特性,是分析电能质量问题的有力工具。文中介绍了利用小波变换分析配电网中常见的因开关动作、变压器磁饱和及电力电子装置引起的电能质量扰动问题。经仿真分析,验证了小波变换能够更好地提取扰动的特征信息,从而为电能质量问题的检测、评估、分类以及治理提供了依据。     

Power quality evaluation in smart grids considering modern distortion in electric power systems

High power quality level is required in smart grids especially for non-stationary situations due to increased use of nonlinear loads and PQ disturbances such as dips, swells, transients and interruptions. Many power quality indices (PQIs) are available. In this paper a new fuzzy-wavelet packet transform-based power quality Index (FWPTPQI) is developed to amalgamate existing power quality indices as the output of a fuzzy based module based on fuzzy inference systems, knowledge base and existing PQI as input. Fuzzy systems allow handling the uncertainties associated with the electric power quality evaluation. The proposed approach has been applied to two case studies; stationary balanced and non-stationary unbalanced three-phase systems. The results are compatible with prevalent situations. The new index gives significant sense of the quality of transmitted electrical power. A comparative study of using different wavelet basis functions is considered and results indicate that Daubechies 10 and Daubechies 15 could be considered as the overall best wavelet basis functions. Since the new index represents an amalgamation of the PQ indices with less number of wavelet coefficients, it helps reduce the size of data processed which is required in smart grid applications.     

小波变换在电力系统监控网络通信中的应用

电力系统故障数据的压缩与解压缩对于电力系统数字监控网络通信的实时性，分布性，同步性具有重要意义，基于提升策略的小波变换是实时实现电力系统故障数据的压缩与解压缩的有力工具。     

基于小波变换的直流主动配电网电压波动源辨识

由于直流主动配电网接入了大量分布式电源,直流母线的电压问题主要包括直流母线的电压波动,直流母线的电压暂降和直流母线的电压中断等。本文介绍了小波变换的基本思想,建立不同的小波函数,利用基于小波变换的方法对直流供电系统电压波动源进行辨识,MATLAB仿真分析表明,不同小波函数可以辨识出直流电压的波动时刻与对应的波动源,具有良好的辨识效果。     

基于改进型小波神经网络的灭火系统故障诊断

在装甲车辆灭火系统故障诊断中,小波神经网络算法能将故障诊断定位到元件级,但各元件存在容差,导致参数变化的连续性和随机性使得诊断率不高。为了提高小波神经网络算法在灭火系统中的诊断率,针对网络在运行过程中存在着收敛效果差、训练误差大及容易陷入局部极小值的缺点,网络无法继续训练和测试,提出一种以增加动量的小波自适应神经网络的改进型算法,可以使网络运行更稳定,学习速率更快。经MATLAB仿真实验表明改进后的算法诊断率远高于普通算法。