励磁涌流波形密度系数鉴别算法

针对变压器差动保护在识别内部故障电流与励磁涌流时的可靠性不高这一问题,提出了基于变压器差流波形密度系数的励磁涌流鉴别算法。利用励磁涌流与故障电流具有不同的正弦相关系数的特点,提出波形密度系数的概念,通过比较变压器各工况下的波形密度系数,理论上可准确识别励磁涌流和故障电流。进一步通过建立仿真模型和实际变压器合闸录波数据进行验证所提算法的可行性和快速性。研究表明,该算法具有判据简单、自带滤波效果、不受非周期分量影响的特点,且能无延时地准确判别励磁涌流与内部故障电流。     

自适应变压器励磁涌流判据研究

基于差流谐波正序分量的变压器励磁涌流判据能够有效识别励磁涌流和内部故障。为了进一步提高差动保护的励磁涌流闭锁可靠性和内部故障动作快速性,分析了变压器端电压及其基波正序分量突变量在励磁涌流和内部故障时的不同特征,由此确定了自适应控制因子Q,并将Q加入到差流谐波正序分量的励磁涌流判据中,进而提出了一种自适应变压器励磁涌流新判据。大量EMTP仿真及动模实验表明：新判据能够在励磁涌流时自动降低涌流制动定值,提高涌流的闭锁可靠性;在内部故障时自动升高涌流制动定值,提高区内故障时保护的动作速度。     

基于综合形态算法的变压器励磁涌流识别方法

针对励磁涌流对变压器差动保护的影响,提出了一种基于综合形态算法的励磁涌流识别方法.首先,利用改进形态梯度算子对采集的信号进行处理,然后利用加权形态滤波算子对梯度信号进行滤波.然后,基于内部故障电流和励磁涌流基波相似性的显著差异构造判据对二者进行区分.判据1用来快速识别大多数内部故障,判据2应对电流互感器饱和及空合于内部轻微匝间故障等特殊情况.两识别判据通过协同逻辑,实现涌流情况下的保护闭锁与内部故障情况下的快速动作.最后,通过PSCAD/EMTDC仿真对所提算法进行了验证分析.     

基于模糊多判据的变压器励磁涌流识别新算法

在分析现有各种变压器励磁涌流识别算法的基础上,采用模糊集合理论,综合各判据的优点,得到一种较为理想的新算法,通过EMTP仿真计算,并考虑CT传变特性后,该模糊多判据算法能快速识别各种变压器内部故障和励磁涌流情况,克服了谐波制动原理在某些工况下的误判和延时动作问题。     

变压器和应涌流和励磁涌流识别新判据

针对识别变压器和应涌流与励磁涌流这一差动保护的难题,提出一种利用瞬时频率变化率识别这两种电流的新判据。该判据对原电流信号进行经验模态分解（EMD）,得到一组固有模态分量（IMF）后,对固有模态分量进行希尔伯特黄变换（HHT）得到瞬时频率。根据和应涌流和励磁涌流中含有大量高次谐波和非周期分量,而内部故障电流基本保持基频这一频率特性,将得到的瞬时频率进行微分,通过微分值的大小可判别该电流的性质。理论分析与仿真试验表明,该判据不受涌流中非周期分量的影响,不受间断角影响,并且能准确可靠地识别变压器和应涌流和励磁涌流。     

基于模糊多判据的变压器励磁涌流识别新算法

在分析现有各种变压器励磁涌流识别算法的基础上 ,采用模糊集合理论 ,综合各判据的优点 ,得到一种较为理想的新算法 ,通过EMTP仿真计算 ,并考虑CT传变特性后 ,该模糊多判据算法能快速识别各种变压器内部故障和励磁涌流情况 ,克服了谐波制动原理在某些工况下的误判和延时动作问题     

变压器励磁涌流和内部故障识别新判据

研究变压器差动保护中的关键问题:励磁涌流和内部故障的识别。根据Prony算法能够精确、快速地分析暂态信号的原理,利用Prony算法求取的原信号中基波和二次谐波的幅值和衰减因数,建立了变压器励磁涌流和内部故障识别的新判据,并基于PSCAD/EMTDC建立了变压器发生励磁涌流和内部故障时的仿真图。分析仿真数据,励磁涌流时的Id2/Id1=06170.15,a2/a1=1.672;变压器内部故障时的Id2/Id1=00.15,a2/a1=∞2。由此可见,在变压器励磁涌流时,通过二次谐波与基波幅值之比构成的判据,灵敏度更高;而在变压器发生内部故障时,通过衰减系数构成的判据来判别,灵敏度更高。     

小波变换在励磁涌流和短路电流识别中的应用

为了识别励磁涌流电流和内部故障电流,根据小波变换后励磁涌流在高频率段处呈现出明显的奇异性的特点,利用小波变换对变压器的励磁涌流模型和内部故障模型进行仿真分析,确定了基于一尺度下奇异值的小波判据,比较其高频段上能量的变化情况来识别励磁涌流和内部故障电流。结果表明,此方法可以对励磁涌流和内部故障电流进行良好识别。     

基于新型半周期波形正弦特征的励磁涌流判别方法

提出一种新型基于波形正弦特征判别变压器励磁涌流的方法,利用变压器励磁涌流和内部故障电流波形特征的不同,即变压器正常运行、内部故障时波形具有正弦函数的性质;而空投时涌流波形包含非周期分量、间断角并具有尖顶波特性。通过对各种情况下波形极值点的快速识别,构造出与之对应的半周期正弦波形,进而定义正弦贴近函数K,通过 正弦贴近函数K来区分变压器励磁涌流和内部故障。变压器各种运行情况的动模试验验证了所提方法的可行性和判据的正确性。     

识别变压器励磁涌流的1/4周波面积比较法

为识别变压器励磁涌流和内部故障电流,提出了利用波形特征识别变压器励磁涌流的1/4周波面积比较法。该算法以工频一周波为数据窗长,将数据窗内的差流波形按时间均分为4份,根据各份面积之间的比值来识别励磁涌流和内部故障电流。该算法既能识别出单向涌流,又能识别出对称涌流,算法稳定性好,高次谐波分量对其影响也较小。该识别法与二次谐波制动原理相比,不会出现某些内部故障情况下由于二次谐波大可能造成差动保护延时动作的问题。大量的软件仿真和动模实验证明了该方法的可靠性和可实施性。     

Studies for identification of the inrush based on improved correlation algorithm

A new scheme based on an improved correlation algorithm to distinguish between inrush and internal fault in the transformer is proposed. In this scheme, the current over one cycle of each phase is reorganized as a new measurand. Fault currents can be distinguished from the inrush current if the improved waveform correlation coefficient between the first half-cycle and the latter-half-cycle of the above measurand is over a threshold. Test results show that all types of internal faults, even those accompanied by the magnetizing inrush, can be correctly identified from the inrush with the proposed algorithm. Besides high security, this scheme has satisfactory sensitivity. In addition, this algorithm can be easily implemented in the real-time process.     

基于误差估计的变压器励磁涌流识别原理

提出了一种利用波形特征来区分励磁涌流和内部故障新方法。该方法利用励磁涌流为具有间断角的尖项波这一特征，通过比较半个周期时间窗内的实际波形与两个不同频率的参考波型的相似程度来区分励磁涌流和区内故障。动模实验表明：该方法能够快速切除各种情况下的变压器内部故障，保护动作速度达10～13ms。     

A new method to identify inrush current based on error estimation

This paper presents a novel scheme based on error estimation for identification of magnetizing inrush and internal fault conditions in transformers. Because the inrush current is a peaked wave with a dead angle, we can discriminate internal fault current from inrush current by comparing the similarity between the actual wave and two reference waves under two different frequency conditions per half cycle. Experimental results indicate that this technique is effective under different fault conditions, and an operating time of 10-13 ms can be achieved for internal faults in transformers.     

基于不同傅里叶算法之间相似度的励磁涌流鉴别方法

利用半波傅里叶算法计算的差流基波幅值与绝对门槛值相比较来鉴别励磁涌流和内部故障电流,存在定值整定困难的缺点.提出了一种改进算法,利用全波和半波傅里叶算法计算的差流基波幅值之间的波形相似系数进行励磁涌流鉴别,克服了原方法的上述不足.理论分析和大量动模试验表明:该方法能够可靠区分励磁涌流和变压器内部故障电流,并对对称性励磁涌流具有较好的识别效果,在空载合闸于内部轻微匝间故障时,差动保护仍能快速动作出口.     

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.     

一种基于波形相关分析识别变压器励磁涌流的方法

提出了一种基于波形相关分析识别变压器励磁涌流的方法。该方法首先用线性插值法找出保护启动后最近的差流过零点,然后利用数字信号处理中相关函数的基本原理对采样数据进行分析,计算过零点后一周波中前后半波的自相关系数,根据自相关系数的大小构成区分励磁涌流和故障电流的主判据。动模试验结果表明该方法算法简单,原理清楚,识别正确,具有较高的可靠性和灵敏性。     

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。     

基于波形相关性分析的变压器励磁涌流识别新算法

提出一种利用数字信号处理中的相关函数的基本概念 ,对采样数据进行分析 ,计算采样数据在不同时段上的自相关系数 ,利用自相关系数的大小来区分变压器励磁涌流和内部故障差流的新方法。由于波形自相关性的定义中不仅包含波形幅值大小、形状的信息 ,而且还包含波形的相位信息 ,因此 ,文中所提出的波形相关性分析方法 ,不是单纯地利用涌流或故障电流的单一方面的特征 ,而是对波形进行有机、综合地分析。理论分析和 EMTP仿真结果均表明 :该算法原理清晰 ,识别正确 ,特征明显 ,且不受非周期分量和电流互感器饱和等的影响。     

基于波形互相关系数的变压器励磁涌流识别方法

励磁涌流是导致变压器差动保护误动的主要因素。提出一种利用波形互相关系数特征的励磁涌流识别方法。利用空载合闸或内部故障后的短数据窗构造一标准正弦参考波,对采样波形与构造的标准正弦波两信号的相关性进行分析,计算归一化互相关系数进行内部故障和涌流识别。内部故障时波形互相关系数为接近1的稳定值,而励磁涌流时互相关系数偏离1且其值波动显著。最后PSCAD仿真计算分析结果表明:该方法能在一个周波内准确识别出励磁涌流与内部故障,为提高变压器差动保护性能和优化变压器保护配置提供了有益的理论依据。     

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.