Estimation of time-varying power quality indices using a computationally efficient algorithm

In this paper a new recursive adaptive filter based on a fast Gauss–Newton method has been proposed for the estimation of power quality (PQ) indices for time-varying voltage and current signals in an electric power system. The presented algorithm is based on the minimization of a weighted forgetting factor based error cost function by the use of Recursive Gauss–Newton method. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, which is immune to random noise, waveform distortion and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time PQ indices estimation. Further, the forgetting factor is tuned in accordance with signal error covariance to provide improved performance. Also power system frequency variations are estimated and correction factors are derived. The effects of sub harmonics, and interharmonics in the signal have been considered while estimating the various PQ indices.     

弱电网条件下的单相频率自适应锁相环

为消除直流偏置和谐波畸变对基于二阶广义积分器( SOGI )的单相频率自适应滤波器锁相环跟踪精度的影响,提出了一种级联结构的弱电网条件下的单相频率自适应锁相方法.分析了输入直流偏置对SOGI２ 路正交输出信号的影响,以及 SOGI 锁相环的频率跟踪特性.设计级联结构的双二阶广义积分器( DSOGI )锁相环,前级 DSOGI 消除单相电网电压采样信号中的直流偏置,生成 ２ 路正交电压信号,后级DSOGI 分离出 ２ 路正交信号中的基波正序分量,并抑制电网谐波畸变对锁相环相角与频率检测精度的影响.功率硬件在环实验装置上的实物实验验证了所提锁相方法的有效性.     

频率大范围变换下的电能计量

针对在频率不断变换的情况下对信号功率进行测量的问题,提出了一种可同时测量信号瞬时频率、基波有功功率和视在功率的算法。输入信号为已经滤除了直流和高次谐波的基波分量,通过自适应有限长单位冲击响应(FIR)数字滤波器测量了基波有功功率,通过Prony算法测量了信号的瞬时频率和视在功率,利用改进的最小加权二乘法(WLS)算法提高了功率的精确度。研究结果表明,在频率大量变化的情况下,该算法对信号频率、有功功率以及视在功率的测量的精确度可满足工程应用的需要。     

基于双端同步电压相量的故障测距新算法

针对电子系统中的故障定位问题,提出了一种仅利用线路两端电压相量实现传输线路故障测距的新方法。该方法首先运用对称分量法和线性叠加原理建立故障后的附加正序网络,并且定义了故障点的电压相量平衡指标,进而基于该指标运用遍历搜索方法寻找故障点的位置。该算法仅利用电压相量进行计算,避免了由电流互感器饱和所带来的误差影响。PSCAD仿真实验表明,该算法能够有效地定位故障,并且不受故障类型、过渡电阻等因素的影响。     

基于 AVMD 与改进能量算子的非稳态谐波分析

针对非稳态谐波分析中时频参数检测精度较低的问题,提出一种基于自适应变分模态分解(AVMD)与改进能量算子的 非稳态电力谐波分析方法。 首先,采用 AVMD 对非稳态谐波信号进行分解,其中采用波形特征匹配法对非稳态谐波信号进行 延拓以减轻边界效应影响,并提出能量差和相关系数作为 AVMD 中模态分解个数的判据;结合模态分量,提出改进间隔采样能 量算子快速提取谐波的瞬时幅值和频率,根据差分和信号完成其起止时刻的定位,实现非稳态谐波时频参数的快速准确测量。 仿真与实测结果表明,本文方法能够在电网工频波动、间谐波以及噪声干扰等情况下有效完成非稳态谐波的准确检测,实现暂 态谐波的精确定位,且对非稳态谐波频率、幅值的最大检测误差分别为 0. 094 9% 和 0. 931 4% 。     

Design and implementation of a high accuracy sampling wattmeter under non-sinusoidal and off-nominal frequency conditions

Errors may creep in when measuring active power under non-sinusoidal and off-nominal frequency conditions by a conventional regularly spaced sampling digital wattmeter (RSSDW) due to the existence of higher order voltage and current harmonics, and asynchrony between the signal fundamental frequency and the sampling rate. This paper presents design and implementation of a modified regularly spaced sampling digital wattmeter, called M-RSSDW, immune to above mentioned drawbacks of the conventional RSSDW. The proposed method utilizes a modified/ first degree digital integrator design technique, suitable for the distorted power system signals. The harmful truncation errors of asynchronous sampling have been reduced using numerical compensation method. Compared with the well-established technique such as Liangs’ method, the proposed algorithm provides higher degree of immunity and insensitivity to harmonics and frequency deviation. Structural simplicity, wide range of application and reduced computational load are other salient features of the proposed method. Based on simulation studies, the performance and validity of the proposed algorithm at different operating conditions have been presented and its accuracy and response time have been compared with the Liangs’ approach.     

无功补偿控制器中数据采集及FFT算法处理

控制器集无功补偿、电度量计量、电能质量监测以及通信于一体，将三相电压、电流信号通过传感器进行转换和信号调理，送入单片机80K196KC中的A／D转换器，通过对信号一个周期内的等间隔采样和将电压、电流作为复序列的实部和虚部对采样数据进行快速傅里叶变换处理(FFT)，对电压、电流信号进行频域分析，从而得到各有关电量参数，实现无功补偿、电度量计量及电能质量监测功能。对谐波分析的基本原理、采样方法和采样数据处理的方法进行了详尽阐述，给出了硬件电路和软件设计方法。而且，对数据进行处理的FFT算法进行了分析。本算法经实际测量表明：精度满足无功补偿、电度量计量及电能质量监测的要求。     

A fast,simple and accurate time-varying frequency estimation method for single-phase electric power systems

This paper presents a simple and accurate method to estimate time-varying frequency for single-phase electric power systems, based on three equally spaced samples. A sinusoidal voltage signal model, without dc offset, with time-varying frequency was assumed. Analytical formulas are derived. The method shows good estimation accuracy over a real world wide range of frequency changes. Simulations have been performed.     

Online tracking of instantaneous frequency and amplitude of dynamical system response

This paper presents a sliding-window tracking (SWT) method for accurate tracking of the instantaneous frequency and amplitude of arbitrary dynamic response by processing only three (or more) most recent data points. Teager–Kaiser algorithm (TKA) is a well-known four-point method for online tracking of frequency and amplitude. Because finite difference is used in TKA, its accuracy is easily destroyed by measurement and/or signal-processing noise. Moreover, because TKA assumes the processed signal to be a pure harmonic, any moving average in the signal can destroy the accuracy of TKA. On the other hand, because SWT uses a constant and a pair of windowed regular harmonics to fit the data and estimate the instantaneous frequency and amplitude, the influence of any moving average is eliminated. Moreover, noise filtering is an implicit capability of SWT when more than three data points are used, and this capability increases with the number of processed data points. To compare the accuracy of SWT and TKA, Hilbert–Huang transform is used to extract accurate time-varying frequencies and amplitudes by processing the whole data set without assuming the signal to be harmonic. Frequency and amplitude trackings of different amplitude- and frequency-modulated signals, vibrato in music, and nonlinear stationary and non-stationary dynamic signals are studied. Results show that SWT is more accurate, robust, and versatile than TKA for online tracking of frequency and amplitude.     

An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis

In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment.     

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.     

QIM digital watermarking based on LDPC code and message passing under scaling attacks

基于量化索引调制(QIM)的数字水印系统,被越来越广泛地应用于数字水印信号的嵌入和提取,但是它对于幅度缩放攻击有明显的脆弱性。本文结合LDPC编码辅助和消息传递算法进行水印信号的提取和参数估计,改善了QIM数字水印系统的性能。对于固定幅度缩放攻击,本文提出的算法比利用重复码编码辅助的消息传递算法,更接近于脏纸码代码香农限。     

基于准同步DFT的非整数谐波分析算法

在准同步DFT谐波分析算法的基础上,提出了一种基于非整数波概念的谐波分析算法。该算法的核心思想是:信号频率漂移导致频谱峰值出现的位置与理想位置不一致是短范围泄漏产生的根本原因,而信号频率的漂移可以通过测量相邻点基波相角差来获得,那么应用该漂移值对基波和高次谐波的波次值进行非整数修正就可以准确测量各次谐波的幅值和相角。仿真实验和工程实例证明,该算法能够有效抑制频谱的长范围泄漏和短范围泄漏,提高基波与高次谐波的幅值与相位测量准确度。     

Method, algorithm and device for estimation of components above the harmonic frequency range up to 9 kHz

The paper presents a method of estimation of frequency groups with 200 Hz bandwidth in the frequency range from the 50th harmonic up to 9 kHz. The method consists of the application of a fast Fourier transform (FFT) for wavelet coefficients after input signal decomposition and partial synthesis for chosen frequency bands. It enables the computational complexity of the algorithm to be reduced and also attenuates influence of the fundamental component and low-frequency harmonics, as required by IEC Standard 61000-4-7. The particulars of this method are shown and analysis for a chosen wavelet family is provided. Further, the algorithm and its implementation in real device for power quality monitoring is presented. Finally, the results of measurements of two testing signals are shown. The required attenuation of fundamental component and required accuracy was obtained.     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

Fast algorithms for frequency, amplitude and phase evaluation of nonsinusoidal signals with noises

A fast and accurate algorithm for frequency, amplitude and phase estimation of the signals with white Gaussian noises is proposed in this paper. The proposed algorithm need two sample and computation process, one of which is used for frequency estimation in half cycle of the signal and another of which is used for amplitude and phase estimation in another half cycle. The proposed algorithm spends at most 1 cycle. Frequency estimation is based on numerical differentiation, and amplitude and phase estimation is based on fast Fourier Transform. With an initial sample frequency of 512 × 50 Hz, the signal is sampled and the frequency of the signal with white Gaussian noises is estimated at an error of 0.001% over a range of 1 Hz–1000 kHz. With another sample frequency based on the estimated frequency, the signal is once again sampled and the amplitude of the signal is estimated an error of 0.001% over a range of 1 V–320 V and the phase angle of the signal is estimated an accuracy of 0.001% over a range of 0–360. Using Matlab software, the simulation results of the test example are satisfactory.     

Characteristics of the dual-bridge giant magnetoresistance magnetometer

The responsivity and the field noise characteristics of the giant magnetoresistance (GMR) magnetometer driven by an alternating magnetic field are investigated. The harmonic spectrum of the sensor voltage shows that only the odd harmonics of excitation frequency are sensitive to the external dc field. To cancel out the even harmonics, the dual-bridge GMR magnetometer with the balanced output is proposed. With the dual-bridge configuration, the field-to-voltage transfer coefficient is doubled and the field noise is reduced by a factor of 1.4. The minimum field noise is 7 nT∕√Hz at 1 Hz with the ac excitation power of 5.5 mW. The proposed sensor is suitable for the electronic compass application.     

基于Kalman滤波和短时自相关变换的电压暂降检测

为了在含有谐波和噪声的电网中准确检测电压暂降,将卡尔曼滤波器和短时自相关变换结合起来,对相邻工频周期的采样信号做差,再进行短时自相关变换,准确检测到了电压扰动,采用卡尔曼滤波器对基波幅值和相位进行了估计;采用离线计算增益矩阵K的方式减小了卡尔曼递推算法的运算量,并在稳态时通过采用定常增益矩阵K达到了抑制滤波发散的目的,同时减小了所需的存储空间;M atlab仿真实验证实了此方法的有效性。     

基于扩展Prony算法的电力系统非整次谐波分析

传统的谐波检测方法快速傅立叶变换存在栅栏和频谱泄漏现象,因而对电力系统的非整数次谐波不能够实现精确的检测,提出采用扩展Prony算法检测电力系统的谐波,该算法能一次性精确地检测到整数和非整数次谐波的相位、振幅、频率,为电力系统非整数次谐波检测提供了一条新的途径,数值仿真验证了本算法的有效性。     

A precise scheme for detection of current transformer saturation based on time frequency analysis

Current Transformers (CTs) are prone to saturation due to large amplitude of fault current and existence of decaying Direct Current (DC) offset. Since the CT saturation leads to mal-operation of protective relays, detection and correction of saturated currents is one of the most important challenges in the power systems protection. In this paper, a new algorithm is presented for detection of CT saturation time interval by using the Improved S-Transform (IST). Simultaneously, IST can yield a complete visualization of signal in both time and frequency domains. An index based on instantaneous power of the CT secondary current is calculated using the IST output matrix. Then, the saturation region is estimated by determining the maximum and minimum values of the proposed index in the time domain. Comprehensive simulations are implemented using PSCAD/EMTDC software. Main parameters which have direct effect on the saturation levels are considered in simulation studies. As extremum points of the proposed index are determined for detection of saturation regions, the proposed method is not affected by CT parameters. The obtained results show that the proposed algorithm can precisely detect CT saturation time intervals even in noisy conditions.