A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform

This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents.     

Decision Tree based discrimination between inrush currents and internal faults in power transformer

This paper presents a new approach for discriminating inrush currents from internal faults in case of power transformer protection using Decision Tree (DT). Differential current of one cycle inrush and internal faults are taken as inputs to the Decision Tree with corresponding target outputs as ‘0’ for inrush and ‘1’ for internal fault conditions. The DT is trained with inrush currents and internal faults with wide variations in operating parameters of the power network and provides accurate results in distinguishing inrush currents from internal faults. The proposed DT based algorithm is found to be accurate and robust, and thus highly suitable for power transformer protection.     

基于小波算法的变压器涌流和内部故障电流识别

小波变换可将信号同时从时域和频域两个方面加以分解,对分析变压器励磁涌流和内部故障电流非常有效.在PSCAD/EMTDC软件中通过对变压器励磁涌流和内部接地故障进行仿真,并对变压器两侧的电流差分信号进行了小波变换.结果表明,两类信号在细节部分有明显的不同特征,可作为判别的依据.     

基于瞬时励磁电感频率特性判别变压器励磁涌流

基于变压器瞬时励磁电感基频分量的有无，提出了判别励磁涌流与内部故障的新方法。涌流时变压器铁心必然经历饱和与非饱和过程，瞬时励磁电感是时变、交替变化的，具有较大的基频分量；内部故障时，变压器铁心工作于线性区，瞬时励磁电感恒为常数，无基频分量。据此，可实现励磁涌流与内部故障的有效判别。但瞬时励磁电感很难精确求取，为方便计算，提出了等效瞬时电感的概念，论证了其基频特性的等效性。动模实验有力地证实了所提方法的可靠性与安全性。     

A novel digital directional transformer protection technique based on wavelet packet

This paper presents a novel digital technique for transformer protection. The technique is based on deriving a directional quantity proportional to the fault current signal and the prefault voltage signal. Standard fast wavelet transform (FWT) schemes may not be as effective for data that has chiefly oscillatory features. An effective solution to discrimination involves examining the signal in both the time and frequency domains simultaneously. The wavelet packet transform is an extension of the FWT that allows for finer characterization of signal content for both time and frequency together. A 11/132-kV transformer connected to a 132-kV power system was simulated using Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP). Results indicate that the proposed technique is stable, reliable, and fast during the discrimination between internal and external faults, magnetizing inrush currents, and internal faults, ratio-mismatch, and saturation of current transformers (CTs).     

A wavelet-based technique for discrimination between faults and magnetizing inrush currents in transformers

This paper presents the development of a wavelet-based scheme, for distinguishing between transformer inrush currents and power system fault currents, which proved to provide a reliable, fast, and computationally efficient tool. The operating time of the scheme is less than half the power frequency cycle (based on a 5-kHz sampling rate). In this work, a wavelet transform concept is presented. Feature extraction and method of discrimination between transformer inrush and fault currents is derived. A 132/11-kV transformer connected to a 132-kV power system were simulated using the EMTP. The generated data were used by the MATLAB to test the performance of the technique as to its speed of response, computational burden and reliability. The proposed scheme proved to be reliable, accurate, and fast.     

Discrimination between faults and magnetising inrush currents in transformers based on wavelet transforms

This paper presents the development of a wavelet-based scheme, for distinguishing between transformer inrush currents and power system fault currents, which proved to provide a reliable, fast and computationally efficient tool. The operating time of the scheme is less than half power frequency cycle (based on 5 kHz sampling rate). In this work wavelet transform concept is presented. Feature extraction and method of discrimination between transformer inrush and fault currents is derived. A 132/11 kV, transformer connected to a 132 kV power system were simulated using the EMTP. The generated data were used by the MATALB to test the performance of the technique as to its speed of response, computational burden and reliability. The proposed scheme proved to be reliable, accurate and fast.     

基于小波能量的变压器励磁涌流识别方法

提出了一种基于小波能量变化实现励磁涌流判别的新方法。由于励磁涌流和内部故障电流产生的机理不同,因此其能量分布是不同的。小波变换能够准确捕捉暂态信号的特征,且小波能量能够反映信号能量在时域的分布情况。采用db4作为母小波对电力系统仿真所产生的大量实验数据进行了小波变换,通过提取小波变换后的d4（第四层细节部分）并计算其能量变化情况,制定了合理的判据。实验结果表明,该方法能够准确、迅速地识别出变压器励磁涌流状态。     

A correlation based method for discrimination between inrush and short circuit currents in differential protection of power transformer using Discrete Wavelet Transform: Theory,simulation and experimental validation

This paper presents a new method for discrimination between magnetic inrush current and internal fault current in the differential protection of power transformer. The proposed method is based on the Wavelet Transform and correlation coefficient. In this study Discrete Wavelet Transform is used to describe the current signal in terms of different time and frequency components. In the proposed method, the energy signals of these components are employed. In the proposed method, a statistical parameter known as correlation coefficient is used to establish a criterion for the proposed discriminative algorithm. The correlation coefficient is used for express the relationship between wavelet coefficients energy at different scales of the signal resolution. Then pattern of these relations is utilized as a measure to discriminate the inrush current from fault current. For investigation the accuracy of the proposed algorithm different cases of inrush and internal fault currents is simulated by PSCAD/EMTDC software. Also, the proposed method is tested by the gathered data from experimental test at the laboratory. Current signals obtained from the simulation as well as the results obtained from the experimental test are employed by the proposed discriminative algorithm. Analysis of the simulation and experimental results show that the proposed method accurately identifies inrush and fault currents in the distance of the power transformer protection in a time period less than quarter of power frequency cycle. In addition to the sensitivity and high reliability, the proposed method has low computation work and does not required determining the threshold for each new power system.     

电力变压器励磁涌流判别的自适应小波神经网络方法

励磁涌流识别一直是电力变压器差动保护中比较关注的问题。文中提出了一种基于自适应小波神经网络实现变压器励磁涌流判别的新方法。结合励磁涌流和内部故障电流的特点，构建了一个四层的自适应小波神经网络模型，并对其具体的实现方法进行了详细的分析；利用ATP—EMTP程序进行仿真计算生成训练样本和测试样本，对所构建的网络进行了训练和测试，结果表明自适应小波神经网络能准确、可靠地识别出变压器的励磁涌流状态。     

同步关合空载变压器抑制励磁涌流和过电压的研究

高压断路器在随机关合空载变压器时会产生幅值、频率很高的涌流和过电压,从而可能导致继保装置误动、变压器绕组机械应力增加和用户电能质量下降。采用同步开关技术选相关合空载变压器能有效减少励磁涌流和操作过电压。分析了空载变压器同步关合的原理和最佳目标关合相位确定,提出空载变压器铁芯剩磁计算方法,并采用最小二乘算法实现剩磁计算。最后,采用ATP-EMTP建立空载变压器同步关合仿真模型,验证了提出的空载变压器同步关合和铁芯剩磁计算算法有效性,为空载变压器的同步关合减少励磁涌流和过电压提供了理论基础。     

基于小波变换的电力变压器继电保护研究

以小波变换中多分辨分析为理论依据,对电流信号进行多尺度小波分解,提取各尺度高频段能量,提出小波能量法,该方法以不同尺度能量变化之比为判据,对变压器的励磁涌流和内部短路电流进行区分。使用 SIMULINK搭建模型对励磁涌流与短路电流的仿真,得到两者波形。通过小波工具箱提取两者的高频系数,得到各尺度能量。仿真实验结果表明,该方法计算简单便利,能够对励磁涌流和短路电流进行有效地区分。     

基于波形系数的变压器励磁涌流快速识别算法

通过分析励磁涌流波形与磁化曲线的关系,提出了一种基于波形系数的变压器励磁涌流快速识别算法。利用空载合闸和内部故障后符合正弦特征的采样数据预测出参考波形,并将1／2周波内的采样数据与预测数据进行比较,以此定义波形系数。通过波形系数的大小区分变压器励磁涌流状态和内部故障状态。仿真和动模实验表明,该算法能在一个周波内准确识别出空载合闸和各种内部故障状态。     

A discrete wavelet transform approach to discriminating among inrush current,external fault,and internal fault in power transformer using low‐frequency components differential current only

This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low‐frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer‐rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Transformer Differential Protection Using Principal Component Analysis

This paper describe a new algorithm for transformer differential protection, based on pattern recognition of the differential current obtained as the phasor sum of the current-transformer secondary currents. The algorithm uses principal component analysis to preprocess data from the power system in order to eliminate redundant information and enhance hidden pattern in differential current to discriminate between internal faults (transformer differential protection zone) from inrush and overexcitation conditions. The algorithm was proven using PSCAD/EMTDC simulations in a three-phase power system considering critical fault cases. The results show the feasibility to implement this algorithm for transformer differential protection.     

基于最小二乘矩阵束的励磁涌流识别方案

准确、快速地切除变压器故障直接影响电力系统能否持续安全稳定运行,励磁涌流的鉴别正是变压器保护中的重要一环。为了有效辨识励磁涌流与故障电流,避免变压器差动保护的不正确动作,利用变压器励磁涌流与短路故障电流频域信息的差异,提出了一种基于最小矩阵束算法的励磁涌流识别新方案,以变压器两侧的差动电流作为信号量,通过矩阵束算法分析采样信号中不同频率的分量,计算含衰减因数的电流能量信息熵识别励磁涌流。仿真试验证明了新判据具有原理清晰、抗干扰能力强等优点,能够正确区分励磁涌流和故障电流,为后续研究及工程应用提供借鉴和参考。     

电力变压器的励磁涌流判据及其发展方向

励磁涌流与内部故障电流的判别一直是伴随电力变压器差动保护的关键问题，围绕这一主题，世界各国的科技工作者先后提出了许多方法，但仍不能很好地满足当前电力变压器保护的需求——可靠（不拒动）、安全（不误动）及快的动作速度。文中对各种判别方法的原理、优缺点、技术关键及研究和应用现状进行了较详细的分析与客观评价，对比研究后给出了今后励磁涌流判别方法的发展方向。     

基于非饱和区等效瞬时电感波形特征的变压器励磁涌流鉴别方法

分析变压器各种情况下等效瞬时电感的波形特点,提出一种利用波形波动函数检验变压器是否发生励磁涌流的新方法。该方法根据等效瞬时电感在非饱和区域波形变化剧烈的特点,通过计算波形的波动系数来识别涌流与故障电流。动模实验结果显示,即使对于匝间轻微故障,该方法也具有比较高的灵敏度。此外,该方法无需利用变压器的任何参数,仅需测量三相差流和原边各相相电压,算法计算量小,实现简单,整定容易。     

基于MODWT的变压器绕组轻微故障检测及分类研究

本文基于变压器在不同运行工况下的等效瞬时励磁电感的差异,利用最大重叠离散小波变换 (MODWT) 提取有效故障特征参数,实现对变压器绕组轻微匝间故障以及匝间电弧放电故障的检测。首先提取变压器在各种工况下的电气量,求取等效瞬时励磁电感,选取基于db4小波函数的最大重叠离散小波变换进行分析,提取特征量。将故障特征量作为决策树的训练集和测试集,从而实现变压器绕组轻微故障的识别以及分类。最后通过仿真证明,所提出的算法能够准确检测以及区分励磁涌流、轻微匝间短路故障以及匝间电弧放电故障。     

改进小波结合BP网络的风力发电机故障诊断

针对风力发电机早期故障时定子电流特征量难以提取的问题,提出了单子带重构改进小波变换结合BP神经网络的风力发电机故障诊断新方法。通过对风力发电机的定子电流进行单子带重构改进小波变换,消除了传统小波变换中的频率混叠现象;从小波变换后的子带信号中选取特征域、提取特征量作为BP神经网络的输入;在此基础上,结合BP神经网络的输入输出非线性映射能力,完成对故障的诊断和定位。经过仿真实验证实,该方法准确地实现了对风力发电机故障的诊断。