A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station

This study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary conditions in threshold value methods are avoided and at the same time, cost and computational burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20–40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%.     

Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity

In this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity.     

改进的电缆中间接头局放信号去噪方法

针对电力电缆中间接头局部放电信息检测系统实际采集到的局部放电信号含有噪声的问题,提出了一种将快速傅里叶变换与改进小波包变换相结合的处理方法,对于周期性窄带干扰,选取快速傅里叶变换来处理;对于白噪声,通过一种改进的阈值函数的小波包算法进行处理。实际应用结果表明,该方法去噪效果明显,不仅有效去除了局部放电脉冲信号中的噪声,可较好地保留了原始信号的有用信息。     

A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis

Condition monitoring of a wind turbine is important to extend the wind turbine system's reliability and useful life. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear systems, can detect weak signals overwhelmed in the noise. Therefore, a new noise-controlled second-order enhanced SR method based on the Morlet wavelet transform is proposed to extract fault feature for wind turbine vibration signals in the present study. The second-order SR method can obtain better denoising effect and higher signal-to-noise ratio (SNR) of resonance output by means of twice integral transform compared with the traditional SR method. Morlet wavelet transform can obtain finer frequency partitions and overcome the frequency aliasing compared with the classical wavelet transform. Therefore, through Morlet wavelet transform, the noise intensity of different scales can be adjusted to realize the resonance detection of weak periodic signal whatever it is a low-frequency signal or high-frequency signal. Thus the method is well-suited for enhancement of weak fault identification, whose effectiveness has been verified by the practical vibration signals carrying fault information. Finally, the proposed method has been applied to extract feature of the looseness fault of shaft coupling of wind turbine successfully.     

主动频移孤岛检测的抗干扰测频算法

针对并网光伏发电系统主动频移孤岛检测中,并网侧待测信号在过零点附近存在的噪声干扰,对过零测频法和锁相环测频法精度带来的不良影响,提出了一种具有噪声抑制功能的三角变换测频法。通过对最优检测点间隔进行估计和少数频率奇异点的剔除,即使并网光伏发电系统主动频移孤岛检测中并网侧待测信号受噪声干扰,带噪声抑制的三角变换测频法也能避免孤岛效应误判现象,提高了系统运行的可靠性。在MATLAB/Simulink下进行了建模和仿真,验证了该三角变换测频算法的正确性。     

Real-Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three-Phase Induction Motors

This paper presents a real-time implementation of an online protection technique for induction motor fault detection and diagnosis. The protection system utilizes a wavelet packet transform (WPT)-based algorithm for detecting and diagnosing various disturbances occurring in three-phase induction motors. The criterion for the detection is the comparison of the coefficients of the WPT of line currents using a mother wavelet at the second level of resolution with a threshold determined experimentally during the healthy condition of the motor. The algorithm is implemented in real-time using the Texas Instrument TMS320C31 32-b floating-point digital signal processor with the help of object-oriented programming. The proposed technique is tested on two three-phase induction motors. The online test results give a response signal at the instant or within one cycle of disturbance in all cases of investigated faults. In addition, the algorithm is also tested during no load and full load operating conditions of the motor.     

基于小波分析及ITD法识别气缸内气体压力

基于小波分析及Ibrahim时域法ITD（Ibrahim Time Domain)识别内燃机缸内气体压力，利用时域ITD法建立气缸盖振动的数学模型，并用小波分析方法对气缸盖振动响应信号进行有效的信噪分离，最后通过时域模态坐标转换识别内燃机缸内气体压力，由于振动信号信噪分离效果好，从而提高了缸内气体压力的识别精度，为内燃机工作状态的实时监测和故障早期预报提供了行之有效的方法。     

基于小波包变换与遗传膜优化的BP神经网络的孤岛检测方法

光伏并网分布式发电运行中,为保障系统运行的稳定和安全,需具备较高的孤岛检测能力。为此,提出了基于小波包变换与遗传膜优化BP神经网络的孤岛检测方法,在不添加任何扰动的情况下,能实现无盲区检测,弥补传统被动式和主动式孤岛检测存在的不足,通过对PCC点电压信号进行小波包变换提取特征向量,并作为神经网络的输入样本,再利用WPT-GAPS-BP神经网络算法进行孤岛判断。通过仿真,验证了该方法的检测速度和精度更快更准确,具有一定的可行性和可靠性。     

基于小波分析的柴油机故障信号特征的提取

本文提出了一种新的柴油机表面振动信号的故障特征的提取方法，利用柴油机表面振动信号经过小波降噪处理，有效地剔除柴油机表面振动信号的噪声干扰，提高信号的信噪比。用小波包提取降噪后振动信号的能量特征参数。以表征柴油机故障特征，建立起能量到柴油机故障的映射关系。实际研究表明这一特征提取方法是有效的。     

Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG

In this paper, a passive neuro-wavelet based islanding detection technique for grid-connected inverter-based distributed generation was developed. The weight parameters of the neural network were optimized by intelligent water drop (IWD) to improve the capability of the proposed technique in the proposed problem. The proposed method utilizes and combines wavelet analysis and artificial neural network (ANN) to detect islanding. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Discrete wavelet transform (DWT) is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. In passive schemes with a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulation results from Matlab/Simulink shows that the proposed method has a small non-detection zone, and is capable of detecting islanding accurately within the minimum standard time.     

A novel anti islanding detection method for grid connected fuel cell power generation systems

Renewable energy sources have been developed rapidly all around the world, and one of these green energy sources is hydrogen energy. The fuel cell systems have become prominent in renewable energy sources because of its minimal dimensions and energy conversion method. There have been developed, some applications, especially in domestic and automotive areas, and fuel cell systems are also have been started to use in grid connected systems. Fuel cell systems must have some electrical connection standards while they connected to an electrical grid. One of these electrical conditions and may be the most important one is unplanned islanding condition. Islanding is a very dangerous situation because it can damage to the fuel cell and related electrical systems and also working people have been at risk in islanding situation on the grid. In this study, a novel islanding detection method was introduced for grid connected fuel cell systems. 0.5 kW solid oxide fuel cell (SOFC) system used in developed experimental system and a novel anti islanding detection method was researched by using an effective method. The proposed method was also developed by using Matlab Simulink and its useful tools. The developed islanding detection method is robust, reliable and has a fast response time, according to present methods. The results confirm the suggested conditions, and it can be seen in this method, it can also be adapted easily to the grid connected fuel cell systems.     

微电网孤岛检测方法的比较与分析

孤岛检测技术是微电网在特定情况下由并网运行模式向孤岛运行模式转换的必不可少的前提条件。总结了目前孤岛检测的各种方法,详细分析了各种常用的主动检测法和被动检测法在电能质量和检测盲区等方面的优缺点。     

小波变换和信号的奇异性检测技术

小波变换由于具有良好时频局部及多分辨率分析特性，被广泛地应用在信号处理领域中信号奇异性检测等方面。它作为一门新的学科，有着广泛的应用前景。简要地介绍了小波应用在信号奇异性检测方面变换的基本原理。     

基于二进小波变换的高速柴油机故障特征辨识

将采用类Ｍｅｘｉｃａｎｈａｔ小波基并进行二进制离散的小波变换,应用于高速柴油机缸内部件中活塞－缸是隙异常和活塞环胶结两类故障的辨识。结果显示,由于小波变换具有的抑制噪声特性香－频局部化特性,适用于具有非平稳局部突变性质的柴油机机身振动信号分析,从而可以辨识故障产生与否和故障的类型。     

探地雷达在堤防检测中的应用

针对堤防工程质量检测现状,研究了探地雷达检测方法在堤防检测中的应用,采用小波分析法分析了雷达实测数据,根据信号与噪声的小波系数性质不同,采用相应规则对含噪信号进行小波阈值去噪。实例应用结果表明,去噪效果较好,有利于实测资料的分析解释。     

内燃机传动噪声识别的小波分析方法

为了降低内燃机噪声必须确定其噪声源。基于内燃机噪声信号非平稳和周期性的特点，用模拟数据说明了将小波分析应用于内燃机噪声信号分析的合理性，并利用连续小渡变换对一台车用发动机前、后端的噪声源进行了识别诊断，确定了发动机的噪声源，证明了小渡分析是内燃机噪声源识别的一种有效方法。     

基于小波变换的汽轮机振动信号软阀值消噪技术研究

信号消噪是小波变换的重要应用,介绍了小波消噪的基本原理及其主要步骤,以及软阀值消噪方法及软阀值规则的选取,最后利用实验仿真信号和现场实测汽轮机振动信号,并考虑噪声方差估计的对消噪的影响,分析比较了各种软阀值选取方式的消噪效果,从而得到软阀值选取方式的有价值的规律和原则,利用最佳软阀值处理后的小波系数重建信号,分析结果表明该方法能够最有效地消除噪声.     

相关技术在并网发电孤岛检测中的应用研究

提出一种基于相关技术的主动式孤岛检测方法,该方法引入一个二进制伪随机信号,使PWM调制正弦波紧随着该信号做相应变化,从而使输出并网公用连接点(PCC)电压具有优良的自相关特性.当并网时,由于PCC点电压受控于电网电压,其自相关函数值很小;当孤岛出现时,其自相关函数值明显变大,根据PCC点电压自相关函数值的大小确定孤岛.实验和仿真结果表明,该方法能有效检测出孤岛,且算法简单,易于实现.     

基于时频分析法的开关柜避雷器泄漏电流检测

  目的  在电力系统中,开关柜避雷器承担着抑制瞬态过电压和泄放脉冲大电流的重要作用,对于维持其正常稳定运行具有重大意义。  方法  为了有效抑制噪声对泄漏电流信号检测的干扰,提出了一种基于自相关系数与卡方检验优化的时频分析方法。首先通过电流传感器和分流器两种测量结果进行分析,然后利用基于自相关系数与卡方检验优化的小波变换消除信号中的噪声干扰,实现最优分解尺寸的确定,从而更好地适应小信噪比场合。  结果  通过软件平台分析得出在分流器的测量基础上利用优化后的小波算法去噪的抗干扰能力更强,波形质量更好。最后研制了一款泄漏电流在线检测装置,对提出的泄漏电流检测模型加以验证。  结论  实验结果表明该装置能够较好地实现避雷器泄漏电流的实时检测。     

基于小波包和Markov链的低压电力线背景噪声重构

针对传统方法难以重构出时域特性和频域特性与真实低压电力线背景噪声一致的背景噪声问题,搭建了噪声测量平台实测了背景噪声,提出了一种基于小波包变换与Markov链相结合的背景噪声重构方法,通过小波包变换得到真实背景噪声在不同频带中的小波包分解系数,并利用Markov链对分解系数进行统计,模拟生成与实测噪声分解系数统计规律相同的仿真分解系数,将仿真分解系数重构后即可得到背景噪声。实例仿真结果表明,该方法重构的背景噪声在时域波形上与实测噪声极为相似,且功率密度谱变化趋势也与实测噪声基本一致,可作为电力线载波通信设备性能测试的可靠噪声源。