共查询到19条相似文献,搜索用时 93 毫秒
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提出一种基于小波变换和多分辨分析识别变压器励磁涌流和内部故障电流的新方法。根据故障初始阶段励磁涌流的高频分量随时间推移逐渐变大而故障电流高频分量逐渐变小的特点,首先用Daub-4小波对励磁涌流和故障电流进行分解,得到第1层小波系数的2个初始峰值点,然后通过引入可靠性系数来识别励磁涌流和故障电流。动模试验结果表明,该方法能够可靠快速地识别励磁涌流和故障电流,且不需要考虑保护的整定值,实现方便。 相似文献
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为了满足电气化设备对高性能变压器的需求,针对大功率变压器中存在的励磁涌流现象展开研究,并建立了仿真模型。利用一种新型的小波算法对变压器中的励磁涌流现象进行分析和数学建模,并研究了三相变压器的励磁涌流产生机制和信号提取方法,建立了精确的故障识别模型。最后,利用Matlab/Simulink软件设计了仿真系统,实现了对三相变压器的励磁涌流波形识别。仿真结果表明,该仿真模型能够有效提取出变压器励磁涌流的有关特性,为提高变压器的性能提供了理论支持。 相似文献
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小波变换在励磁涌流和短路电流识别中的应用 总被引:1,自引:1,他引:0
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。 相似文献
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基于小波包变换的变压器励磁涌流识别新方法 总被引:18,自引:3,他引:15
提出了一种基于小波包变换的识别变压器励磁涌流的新算法。该算法能正确地区分带长线和不带长线励磁涌流与变压器的各种典型内部故障,同时不会将外部故障误判为内部故障。试验结果表明:所设计的小波包算法能保证励磁涌流的正确识别,且具有实时实现的可能。 相似文献
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变压器主要采用纵联差动保护,如何防止因涌流造成的误动已成为关键性问题。对于该问题,提出一种基于小波-DHNN识别励磁涌流的新的研究方案。利用小波变换对采样信号进行分析,得出励磁涌流的小波系数较内部故障电流有非常明显的差异,并且畸变特点伴随整个衰减过程。分析后的信号通过离散型Hopfield网络测试与识别,从而区分励磁涌流和内部故障电流。通过PSCAD和MATLAB仿真软件进行建模仿真,结果表明,该方法能可靠的识别励磁涌流和内部故障电流,并且准确率高达100%。 相似文献
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本文通过对变压器励磁涌流产生机理的分析,针对励磁涌流对变压器差动保护的影响,提出了将小波分析的数学方法应用在励磁涌流识别中的新方法,经MATLAB仿真结果证明,该方案提高了励磁涌流的识别准确度,具有较好的应用前景和使用价值。 相似文献
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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。 相似文献
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A.A. Hossam Eldin M.A. Refaey 《Electric Power Systems Research》2011,81(1):19-24
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. 相似文献
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变压器励磁涌流和内部故障的鉴别一直是变压器差动保护中的一个热点问题。在几种传统的识别励磁涌流方法的基础上 ,结合模糊神经网络这一新型的人工智能技术 ,综合利用这几种原理对电气量的采样值分别提取形成网络的特征输入量 ,并采用了Simpson模糊极小 -极大神经网络来形成区分励磁涌流和内部故障的模糊模式分类器。运用EMTP程序通过大量的仿真计算获取网络的训练和测试样本 ,结果表明 ,训练后的网络能快速地区分变压器各种运行工况下的励磁涌流和内部故障 ,对测试样本的正确率达到 10 0 %。 相似文献
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区分励磁涌流和内部故障时的短路电流,是变压器差动保护的难点技术之一,虽然目前已有多种方法,但每种方法都或多或少地存在着一些问题,空载合闸误动或内部故障时拒动(或延时动作)的情况还时有发生。在引入瞬时无功功率理论的基础上,提出了一种依据变压器两侧三相差瞬时有功功率和瞬时无功功率直流分量比值的变化关系来识别变压器励磁涌流和内部故障电流的新方法。该方法不仅简便易行,而且从平均有功和平均无功的关系出发,进一步揭示了变压器励磁涌流与内部故障本质上的不同。RTDS仿真实验结果表明:该方法简单可靠,识别效果显著。 相似文献
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This paper proposes a new technique for transformer protection. The technique is concerned with the detection of the fault generated high frequency current transients by means of a specially designed relay unit. The relay, tuned to a band of high frequencies, is used to capture the transient currents from both sides of the transformer; the differential and average currents between the two sides are then calculated. The spectral energies of these current signals are extracted to produce the operate and restraint signals; a comparison between the levels of the two signals determines whether the fault is internal or external to the protected zone. A new technique for inrush detection has also been proposed in this paper. The technique detects inrush current by using the high frequency components contained in its current transient signal. The restraint signal is derived by computing the ratio of the spectral energy of the transient signal to the fundamental current. A comparison between the level of restraint signal and a pre-defined threshold determines whether a magnetizing inrush is in process. Simulation studies with respect to different fault and inrush conditions have been conducted, and the results prove that the proposed technique is able to offer fast responses in protection and accurately discriminate between inrush magnetizing current and internal faults 相似文献
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This paper presents a novel approach for differential protection of power transformers. This method uses wavelet transform (WT) and adaptive network-based fuzzy inference system (ANFIS) to discriminate internal faults from inrush currents. The proposed method has been designed based on the differences between amplitudes of wavelet transform coefficients in a specific frequency band generated by faults and inrush currents. The performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. Also the proposed algorithm is tested off-line using data collected from a prototype laboratory three-phase power transformer. The test results show that the new algorithm is very quick and accurate. 相似文献