CN104597344A - Fault arc online detecting method based on wavelet first-layer high-frequency component correlation - Google Patents
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Abstract
本发明涉及一种基于小波一层高频分量相关性的故障电弧在线检测方法,包括以下步骤:1)提取负载正常运行时串联电流的第一层小波分解高频分量,获得采样电流的参考序列;2)以设定频率采集负载所在低压交流串联电路的采样电流,对其进行小波分解,提取第一层高频细节分量;3)对步骤2)提取的第一层高频细节分量进行分割,获得待测序列;4)根据待测序列与参考序列的相关系数判断是否存在故障电弧,若是,则执行步骤5),若否,则返回步骤2);5)判断故障电弧发生个数是否满足UL1699标准,若是,则输出跳闸信号。与现有技术相比,本发明克服了现有的低压交流串联故障电弧判别方法对负载特殊性的依赖,缩短了故障判断时间。
The invention relates to an online fault arc detection method based on the correlation of one layer of wavelet high-frequency components, comprising the following steps: 1) extracting the first-layer wavelet decomposition high-frequency components of the series current when the load is in normal operation, and obtaining the reference sequence of the sampling current ; 2) Collect the sampling current of the low-voltage AC series circuit where the load is located at a set frequency, perform wavelet decomposition on it, and extract the first layer of high-frequency detail components; 3) segment the first layer of high-frequency detail components extracted in step 2) , to obtain the sequence to be tested; 4) judge whether there is a fault arc according to the correlation coefficient between the sequence to be measured and the reference sequence, if so, then perform step 5), if not, return to step 2); 5) judge whether the number of fault arcs occurs Meet the UL1699 standard, if so, output a trip signal. Compared with the prior art, the present invention overcomes the dependence of the existing low-voltage AC series fault arc discrimination method on the particularity of the load, and shortens the fault judgment time.
Description
技术领域technical field
本发明涉及一种低压交流串联故障电弧判别方法,尤其是涉及一种基于小波一层高频分量的相关性分析的故障电弧在线检测方法。The invention relates to a low-voltage AC series fault arc discrimination method, in particular to an on-line fault arc detection method based on the correlation analysis of wavelet one-layer high-frequency components.
背景技术Background technique
电路长期带载或过载运行会因线路绝缘破损老化、电气接触不良而产生局部放电现象,进而引起电弧。电弧局部电阻增大,释放的能量产热使得线路恶化加剧,引燃周围物体。Long-term load or overload operation of the circuit will cause partial discharge due to damaged and aging line insulation and poor electrical contact, which will cause arcing. The local resistance of the arc increases, and the released energy generates heat, which aggravates the deterioration of the circuit and ignites the surrounding objects.
国内外现有的低压故障电弧断路器(AFCI)的内置算法多是基于对电路中故障电弧电流、电压信号进行时域、频域特征的分析。Most of the built-in algorithms of low-voltage arc fault circuit interrupters (AFCI) at home and abroad are based on the analysis of time domain and frequency domain characteristics of fault arc current and voltage signals in the circuit.
频域方法中,有学者提出用快速傅里叶分析法,取电流五次谐波的幅值大小变化作为电弧发生的判据,但通过实验测量和数据分析,对于电吹风高档、电容型启动风扇该判断方法适用,对于电水壶等阻性负载,五次谐波的幅值大小在正常运行和故障时区别不大,对于电脑等开关电源型负载,甚至正常时的五次谐波的幅值高于故障时的,可见该判断方法对不同负载的适用有局限性。有研究人员用离散小波分解的方法,提取经若干层分解后的电流信号细节,结合神经网络学习方法,用其幅值变化或与横轴围成的面积大小判断电弧发生,具有一定的负载通用性,但自学习和样本训练过程消耗相对较长的时间,难以达到实时检测的要求。有学者把电弧视为一种局部放电现象,用高频电流互感器、射频线圈或Rogowski线圈提取电弧发生时的高频信号,并分析其所在频段,制作专用频带的天线来检测电弧,但该方法易受外界空间信号干扰,有一定的误判率。In the frequency domain method, some scholars proposed to use the fast Fourier analysis method to take the amplitude change of the fifth harmonic of the current as the criterion for arc occurrence. This judgment method is applicable to fans. For resistive loads such as electric kettles, the amplitude of the fifth harmonic is not much different between normal operation and failure. For switching power supply loads such as computers, the amplitude of the fifth harmonic even in normal conditions If the value is higher than the fault, it can be seen that the judgment method has limitations in the application of different loads. Some researchers use the discrete wavelet decomposition method to extract the details of the current signal after several layers of decomposition, combined with the neural network learning method, use its amplitude change or the size of the area surrounded by the horizontal axis to judge the occurrence of the arc, which has a certain load universality However, the process of self-learning and sample training consumes a relatively long time, and it is difficult to meet the requirements of real-time detection. Some scholars regard electric arc as a kind of partial discharge phenomenon, and use high frequency current transformer, radio frequency coil or Rogowski coil to extract the high frequency signal when arc occurs, and analyze the frequency band where it is located, and make an antenna with special frequency band to detect arc, but this The method is susceptible to external space signal interference, and has a certain rate of misjudgment.
时域的方法通常与其他智能算法相结合,有学者利用卡尔曼滤波器的学习能力,通过计算习得信号与时域实际信号的差值判断电弧是否发生,有一定的准确性,但其学习过程耗时相对较长。还有通过比较正常和故障时每半周期电流峰值或平均值变化量来做判断,但局限于某些具体负载,缺乏通用性。The time-domain method is usually combined with other intelligent algorithms. Some scholars use the learning ability of the Kalman filter to judge whether the arc occurs by calculating the difference between the acquired signal and the actual signal in the time domain. It has certain accuracy, but its learning The process takes a relatively long time. There is also a judgment by comparing the current peak value or average value of each half cycle during normal and fault conditions, but it is limited to certain specific loads and lacks versatility.
现有的交流串联故障电弧判别算法对于不同的负载,通常仅能做到在电流平均值或峰值、频域分布频带、频域某次谐波、小波高频细节幅值等某一情况下有良好适用效果,但在实际低压串联故障电弧断路器产品的应用中,由于负载的多样性和复杂性,难以做到某单一方法实现的准确、可靠、通用的判断效果和较短的故障判断时间以满足在线检测的实时性要求。For different loads, the existing AC series fault arc discrimination algorithm can usually only be effective in certain situations such as current average value or peak value, frequency domain distribution frequency band, frequency domain certain harmonic, wavelet high frequency detail amplitude, etc. Good application effect, but in the actual application of low-voltage series fault arc circuit breaker products, due to the variety and complexity of loads, it is difficult to achieve accurate, reliable, and universal judgment results and short fault judgment time achieved by a single method To meet the real-time requirements of online detection.
发明内容Contents of the invention
本发明的目的就是为了克服现有的低压交流串联故障电弧判别方法对负载特殊性的依赖,缩短故障判断时间以满足实时在线检测的要求,而提供一种基于小波一层高频分量相关性的故障电弧在线检测方法。The purpose of the present invention is to overcome the dependence of the existing low-voltage AC series fault arc discrimination method on the specificity of the load, shorten the fault judgment time to meet the requirements of real-time online detection, and provide a wavelet-based one-layer high-frequency component correlation On-line detection method of arc fault.
本发明的目的可以通过以下技术方案来实现:The purpose of the present invention can be achieved through the following technical solutions:
一种基于小波一层高频分量相关性的故障电弧在线检测方法,包括以下步骤:An online fault arc detection method based on wavelet one-layer high-frequency component correlation, comprising the following steps:
1)提取负载正常运行时串联电流的第一层小波分解高频分量,获得采样电流的参考序列;1) Extract the first layer of wavelet decomposition of the high-frequency component of the series current when the load is in normal operation, and obtain the reference sequence of the sampling current;
2)以设定频率采集负载所在低压交流串联电路的采样电流,对其进行小波分解,提取第一层高频细节分量;2) Collect the sampling current of the low-voltage AC series circuit where the load is located at a set frequency, perform wavelet decomposition on it, and extract the first layer of high-frequency detail components;
3)对步骤2)提取的第一层高频细节分量进行分割,获得待测序列;3) Segment the first layer of high-frequency detail components extracted in step 2) to obtain the sequence to be tested;
4)根据待测序列与参考序列的相关系数判断是否存在故障电弧,若是,则执行步骤5),若否,则返回步骤2);4) Judging whether there is an arc fault according to the correlation coefficient between the sequence to be measured and the reference sequence, if so, then perform step 5), if not, then return to step 2);
5)判断故障电弧发生个数是否满足UL1699标准,若是,则输出跳闸信号,若否,则返回步骤2)。5) Judging whether the number of fault arc occurrences meets the UL1699 standard, if yes, then output a trip signal, if not, then return to step 2).
所述步骤1)具体为:Described step 1) is specifically:
获取负载正常运行时p个周期的串联电流[s1,…,sp],其平均值的小波分解第一层高频分量sj作为采样电流的参考序列,所述参考序列中包含m个点。Obtain the series current [s 1 ,…,s p ] of p cycles when the load is running normally, and its average value The wavelet decomposition of the first layer of high-frequency components s j is used as a reference sequence of sampling current, and the reference sequence contains m points.
所述p的取值为5。The value of p is 5.
所述步骤2)中,对采集的采样电流进行经归一化和降噪处理后,再进行小波分解。In the step 2), the collected sampling current is subjected to normalization and noise reduction processing, and then wavelet decomposition is performed.
所述步骤2)中,设定频率的范围为(0.35f,0.5f],f=104Hz。In the step 2), the range of the set frequency is (0.35f, 0.5f], f=10 4 Hz.
所述小波分解是采用Daubechies小波族中的db1小波基对采样电流进行小波分解。The wavelet decomposition is to use the db1 wavelet base in the Daubechies wavelet family to perform wavelet decomposition on the sampling current.
所述步骤3)具体为:The step 3) is specifically:
对第一层高频细节分量D1按顺次每m个点分为一组,得到n个待测序列yi(i=1,2…,n)。The high-frequency detail components D1 of the first layer are divided into one group by m points sequentially, and n sequences to be measured y i (i=1, 2...,n) are obtained.
所述步骤4)中,待测序列yi与参考序列sj间的相关系数ξij通过以下公式计算:In the step 4), the correlation coefficient ξij between the test sequence y i and the reference sequence s j is calculated by the following formula:
式中,m为每个序列中包含的点数。In the formula, m is the number of points contained in each sequence.
所述步骤4)中,当ξij<ξ0时,判断存在故障电弧,当ξij≥ξ0时,判断不存在故障电弧,进行下一个待测序列与参考序列的相关系数计算,ξ0为相关系数阈值。In the step 4), when ξ ij <ξ 0 , it is judged that there is an arc fault, and when ξ ij ≥ ξ 0 , it is judged that there is no fault arc, and the correlation coefficient calculation between the next sequence to be measured and the reference sequence is performed, ξ 0 is the correlation coefficient threshold.
所述相关系数阈值ξ0的取值为0.4。The value of the correlation coefficient threshold ξ 0 is 0.4.
所述步骤5)中,判断故障电弧发生个数是否满足UL1699标准具体为:In the step 5), it is judged whether the number of arc fault occurrences satisfies the UL1699 standard specifically as follows:
判断第1个故障电弧半周波与第8个故障电弧半周波之间的时间差Δt是否小于或等于0.5s,若是,则满足UL1699标准,若否,则不满足UL1699标准。Determine whether the time difference Δt between the first fault arc half-cycle and the eighth fault arc half-cycle is less than or equal to 0.5s, if yes, meet the UL1699 standard, if not, then do not meet the UL1699 standard.
与现有技术相比,本发明具有以下优点:Compared with the prior art, the present invention has the following advantages:
(1)本发明在进行小波分解前对采样电流进行归一化、降噪等处理,减小电弧以外的因素对电流波形的影响,方便不同负载时的电流数据特征比较,提高了不同负载下电流细节的分辨率,从而提高了相关性算法正确识别故障电弧的效率。(1) The present invention performs normalization and noise reduction processing on the sampling current before wavelet decomposition, reduces the influence of factors other than the arc on the current waveform, facilitates the comparison of current data characteristics at different loads, and improves the efficiency of the current data under different loads. The resolution of the current details improves the efficiency of the correlation algorithm to correctly identify the fault arc.
(2)本发明所采用的采样频率能准确计算相关系数,保证判断结果的有效准确和尽可能小的运算量。(2) The sampling frequency adopted in the present invention can accurately calculate the correlation coefficient, ensuring effective and accurate judgment results and a small amount of computation as possible.
(3)本发明所选取的相关系数阈值适用实验条件下各类负载的故障电弧判断,用同一个相关系数阈值可以实现断路器按实际情况的正确动作,提高了通用性和有效性。(3) The correlation coefficient threshold selected by the present invention is suitable for fault arc judgment of various loads under experimental conditions, and the same correlation coefficient threshold can realize the correct action of the circuit breaker according to the actual situation, which improves the versatility and effectiveness.
(4)本发明只用到乘法和加法基本运算,便于处理器的数据计算和高效传输,不需离线自学习或样本训练过程,缩短故障判断时间,较小的计算量和适用于实时在线检测也是其显著优点。(4) The present invention only uses the basic operations of multiplication and addition, which is convenient for data calculation and efficient transmission of the processor, does not require offline self-learning or sample training process, shortens the fault judgment time, has a small amount of calculation and is suitable for real-time online detection It is also its significant advantage.
附图说明Description of drawings
图1是本发明检测方法的结构框图;Fig. 1 is the block diagram of detection method of the present invention;
图2是本发明检测方法的流程示意图;Fig. 2 is a schematic flow sheet of the detection method of the present invention;
图3是本发明方法在复印机负载电流时不同采样率下所求相关系数的对比;Fig. 3 is the comparison of the correlation coefficient sought under the different sampling rates of the inventive method when copier load current;
图4是本发明检测方法在阻性负载1000W电水壶时故障电弧的判断过程示意图;Fig. 4 is the schematic diagram of the judging process of the fault arc when the detection method of the present invention is in a resistive load 1000W electric kettle;
图5是本发明检测方法在感性负载500W电钻时故障电弧的判断过程示意图;Fig. 5 is a schematic diagram of the judgment process of the fault arc when the detection method of the present invention is inductively loaded with a 500W electric drill;
图6是本发明检测方法在容性负载50W电容启动型风扇时故障电弧的判断过程示意图;Fig. 6 is a schematic diagram of the judgment process of the fault arc when the detection method of the present invention has a capacitive load of a 50W capacitor start-up fan;
图7是本发明检测方法在开关电源负载1台300W电脑时故障电弧的判断过程示意图;Fig. 7 is a schematic diagram of the judgment process of the fault arc when the switching power supply loads a 300W computer in the detection method of the present invention;
图8是本发明检测方法在开关电源负载1200W复印机时故障电弧的判断过程示意图;Fig. 8 is a schematic diagram of the judgment process of the fault arc when the switching power supply load is 1200W copier by the detection method of the present invention;
图9是本发明检测方法在开关电源负载2台300W电脑并联时故障电弧的判断过程示意图。Fig. 9 is a schematic diagram of the judgment process of the fault arc when the switching power supply load is connected in parallel with two 300W computers by the detection method of the present invention.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明进行详细说明。本实施例以本发明技术方案为前提进行实施,给出了详细的实施方式和具体的操作过程,但本发明的保护范围不限于下述的实施例。The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
图1是本发明提出的基于小波一层高频分量的相关性分析的低压交流故障电弧在线检测方法的结构框图,其判断过程的基本步骤有:按一定频率采样电路电流,采样电流归一化并降噪得到g(t),用小波分析提取g(t)的高频细节分量D1,计算正常运行时采样电流的参考序列sj,计算各待测序列yi(i=1,2…,n)与sj的相关系数ξij,比较ξij和相关系数阈值ξ0的大小,当ξij<ξ0,表明检测出一个半周波的电弧故障,若0.5s内检测出8个故障电弧半周波则判断电弧故障发生,发出跳闸信号。Fig. 1 is the structural block diagram of the low-voltage AC fault arc online detection method based on the correlation analysis of wavelet one-layer high-frequency component that the present invention proposes, and the basic steps of its judging process include: sampling circuit current by certain frequency, sampling current normalization and denoising to obtain g(t), use wavelet analysis to extract the high-frequency detail component D1 of g(t), calculate the reference sequence s j of the sampling current during normal operation, and calculate each sequence to be measured y i (i=1,2... ,n) and the correlation coefficient ξ ij of s j , compare the size of ξ ij and the correlation coefficient threshold ξ 0 , when ξ ij <ξ 0 , it indicates that a half-cycle arc fault is detected, if 8 faults are detected within 0.5s The half-cycle of the arc is used to judge the occurrence of an arc fault and send out a trip signal.
图2是本发明提出的基于小波一层高频分量的相关性分析的低压交流串联故障电弧在线检测方法的软件程序逻辑流程图,工作流程是:Fig. 2 is the software program logic flow chart of the low-voltage AC series fault arc on-line detection method based on the correlation analysis of wavelet one-layer high-frequency component that the present invention proposes, and the workflow is:
开始→参数初始化,包括:电弧故障发生标志位flag、电弧故障个数count、跳闸标志位Trip均置0,设置电流采样率f,待测序列与参考序列的相关系数阈值ξ0=0.4→按既定采样率采样得到电流原始信号f(t)→采样电流f(t)的归一化和软件降噪,f(t)的幅值为Am,将f(t)归一化得到再对进行软件降噪,得到g(t)=[x1,x2,…,xN]→对g(t)进行小波分解得到高频分量D1,选取Daubechies小波族中的db1小波基ψ(t)对g(t)进行小波分解, 是宽为1,高为1的脉冲函数。提取高频细节系数
图3是本发明检测方法在复印机负载时的电流在不同采样率下所求相关系数的对比,实验中分别在采样率为f=104Hz,0.7f,0.5f,0.35f,0.25f时求序列yi与sj的相关系数。由图3可见,采样率为f,0.7f,0.5f时对各序列计算的相关系数数值相近,且判断结果符合电路运行状态,采样率为0.35f,0.25f时,计算的相关系数出现与电气状态不符的坏点,说明此时的采样率过低,没有准确提取到信号特征,所计算的相关系数不能用于电弧发生与否的判断。定义能准确计算相关系数的最小采样率为最佳采样率,保证判断结果的有效准确和尽可能小的运算量。实验论证f~0.5f时的采样率均满足实验要求,且最佳采样率应在(0.35f,0.5f]。Fig. 3 is a comparison of the correlation coefficients obtained by the detection method of the present invention when the copier is loaded by the current at different sampling rates. Find the correlation coefficient between sequence y i and s j . It can be seen from Figure 3 that when the sampling rate is f, 0.7f, and 0.5f, the correlation coefficient values calculated for each sequence are similar, and the judgment result is in line with the circuit operation state. When the sampling rate is 0.35f and 0.25f, the calculated correlation coefficient appears to be similar The bad points where the electrical state does not match indicate that the sampling rate at this time is too low, and the signal features are not accurately extracted, and the calculated correlation coefficient cannot be used to judge whether the arc occurs or not. Define the minimum sampling rate that can accurately calculate the correlation coefficient and the best sampling rate to ensure the effectiveness and accuracy of the judgment result and the minimum amount of calculation as possible. Experimental demonstration f ~ 0.5f sampling rate meets the experimental requirements, and the best sampling rate should be (0.35f, 0.5f].
实施例1Example 1
本实施例所用的方法结构框图和程序流程图如图1、图2所示,负载是1000W电水壶阻性负载,故障电弧识别的具体步骤如下:The method structural block diagram and program flow chart used in this embodiment are shown in Figure 1 and Figure 2, the load is a 1000W electric kettle resistive load, and the specific steps of fault arc identification are as follows:
1)按采样率f=104Hz采集负载所在电路的串联电流f(t)。1) Collect the series current f(t) of the circuit where the load is located at a sampling rate of f=10 4 Hz.
2)采样电流f(t)的幅值为Am,将f(t)归一化得到去除信号中的外界噪声,将归一化后的电流序列进行软件降噪,得到g(t)=[x1,x2,…,xN]。2) The amplitude of sampling current f(t) is A m , normalize f(t) to get Remove the external noise in the signal, and the normalized current sequence Perform software noise reduction to obtain g(t)=[x 1 , x 2 , . . . , x N ].
3)选取Daubechies小波族中的db1小波基ψ(t)对g(t)进行小波分解, 是宽为1,高为1的脉冲函数,计算高频细节系数 得到g(t)的高频细节序列D1。3) Select the db1 wavelet base ψ(t) in the Daubechies wavelet family to perform wavelet decomposition on g(t), Is a pulse function with a width of 1 and a height of 1, and calculates the high-frequency detail coefficient Obtain the high-frequency detail sequence D1 of g(t).
4)取该负载下电路正常运行时5个周期的电流[s1,s2,s3,s4,s5],其平均值的小波分解第一层高频分量sj作为采样电流的参考序列。4) Take the current of 5 cycles [s 1 , s 2 , s 3 , s 4 , s 5 ] when the circuit is operating normally under the load, and its average value The wavelet decomposition of the first layer of high-frequency components s j as the reference sequence of the sampling current.
5)把D1按顺次每m=200个点分为一组,得到n个待测序列yi(i=1,2…,n),m=f/50是每序列周期内的点数。5) Divide D1 into a group with every m=200 points sequentially to obtain n sequences to be tested y i (i=1,2...,n), m=f/50 is the number of points in each sequence period.
6)计算各待测序列yi与参考序列sj的相关系数ξij:6) Calculate the correlation coefficient ξ ij of each sequence y i to be measured and the reference sequence s j :
0≤|ξij|≤1,ξij越接近于1,说明yi与sj相似度越高,被测序列yi正常,ξij越接近于0,说明yi与sj相似度越低,被测序列yi有电弧故障发生。0≤|ξ ij |≤1, the closer ξ ij is to 1, the higher the similarity between y i and s j is, the measured sequence y i is normal, the closer ξ ij is to 0, the closer the similarity between y i and s j is Low, the measured sequence y i has an arc fault.
7)比较计算出的yi与sj的相关系数ξij和相关系数阈值ξ0=0.4大小关系,若ξij≥ξ0,待测序列号i加1,转8),若ξij<ξ0,故障电弧发生标志位flag=1,转8)。7) Compare the relationship between the calculated correlation coefficient ξ ij of y i and s j and the correlation coefficient threshold ξ 0 = 0.4, if ξ ij ≥ ξ 0 , add 1 to the sequence number i to be tested, go to 8), if ξ ij < ξ 0 , arc fault occurrence flag flag=1, go to 8).
8)若flag值为1,故障电弧个数count加1,并记录该序列的序列号index,再把flag置0。8) If the flag value is 1, add 1 to the count of fault arcs, record the serial number index of the sequence, and then set the flag to 0.
9)判断第1个故障半周波与第8个故障半周波之间的时间差Δt是否不大于0.5s。若是,转10),若否,转1),继续下一序列的上述采样计算过程。9) Determine whether the time difference Δt between the first fault half cycle and the eighth fault half cycle is not greater than 0.5s. If yes, go to 10), if not, go to 1), and continue the above sampling calculation process of the next sequence.
10)跳闸信号Trip=1,断开负载所在电路。10) Trip signal Trip=1, disconnect the circuit where the load is located.
图4是本发明检测方法在阻性负载1000W电水壶时故障电弧的判断过程。图4中从上到下依次是电水壶负载归一化降噪后的采样电流g(t)(Unitized current)、g(t)的高频分量序列D1、待测序列yi与参考序列sj的相关系数ξij(Corr-Coef)、负载所在电路的跳闸信号标志位Trip。由图4可以看到,本发明可以实现阻性负载电水壶时故障电弧的准确识别判断。Fig. 4 is the judging process of the fault arc when the detection method of the present invention is in a resistive load 1000W electric kettle. In Fig. 4, from top to bottom are the sampling current g(t) (Unitized current) after the normalized noise reduction of the electric kettle load, the high-frequency component sequence D1 of g(t), the sequence to be measured y i and the reference sequence s The correlation coefficient ξ ij (Corr-Coef) of j , the trip signal flag bit Trip of the circuit where the load is located. It can be seen from FIG. 4 that the present invention can realize the accurate identification and judgment of the fault arc when the electric kettle is resistively loaded.
实施例2Example 2
图5是本发明检测方法在感性负载500W电钻时故障电弧的判断过程。故障电弧识别的具体步骤同实施例1。图5中从上到下依次是电钻负载归一化降噪后的采样电流g(t),g(t)的高频分量序列D1,待测序列yi与参考序列sj的相关系数ξij,负载所在电路的跳闸信号标志位Trip。由图5可以看到,本发明的算法可以实现感性负载电钻时故障电弧的准确识别判断。Fig. 5 is the judging process of the fault arc when the detection method of the present invention has an inductive load of 500W electric drill. The specific steps of arc fault identification are the same as those in Embodiment 1. In Fig. 5, from top to bottom are the sampling current g(t) after the normalized noise reduction of the electric drill load, the high-frequency component sequence D1 of g(t), and the correlation coefficient ξ of the sequence to be measured y i and the reference sequence s j ij , the trip signal flag bit Trip of the circuit where the load is located. It can be seen from FIG. 5 that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the inductive load electric drill is used.
实施例3Example 3
图6是本发明检测方法在容性负载50W电容启动型风扇时故障电弧的判断过程。故障电弧识别的具体步骤同实施例1。图6中从上到下依次是电容启动型风扇负载归一化降噪后的采样电流g(t),g(t)的高频分量序列D1,待测序列yi与参考序列sj的相关系数ξij,负载所在电路的跳闸信号标志位Trip。由图6可以看到,本发明的算法可以实现容性负载电容启动型风扇时故障电弧的准确识别判断。Fig. 6 is a judgment process of an arc fault when the detection method of the present invention is a capacitive load of a 50W capacitive start-up fan. The specific steps of arc fault identification are the same as those in Embodiment 1. From top to bottom in Fig. 6 are the sampling current g(t) after normalized noise reduction of the capacitive start-up fan load, the high-frequency component sequence D1 of g(t), the sequence y i to be measured and the reference sequence s j Correlation coefficient ξ ij , the trip signal flag bit Trip of the circuit where the load is located. It can be seen from FIG. 6 that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the capacitive load capacitor starts the fan.
实施例4Example 4
图7是本发明检测方法在开关电源负载1台300W电脑时故障电弧的判断过程。故障电弧识别的具体步骤同实施例1。图7中从上到下依次是1台电脑负载归一化降噪后的采样电流g(t),g(t)的高频分量序列D1,待测序列yi与参考序列sj的相关系数ξij,负载所在电路的跳闸信号标志位Trip。由图7可以看到,本发明的算法可以实现开关电源负载1台电脑时故障电弧的准确识别判断。Fig. 7 is the judging process of the fault arc when the switching power supply loads a 300W computer by the detection method of the present invention. The specific steps of arc fault identification are the same as those in Embodiment 1. In Fig. 7, from top to bottom, the sampling current g(t) after the normalized noise reduction of a computer load, the high-frequency component sequence D1 of g(t), the correlation between the sequence to be measured y i and the reference sequence s j Coefficient ξ ij , the trip signal flag Trip of the circuit where the load is located. It can be seen from FIG. 7 that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the switching power supply loads a computer.
实施例5Example 5
图8是本发明检测方法在开关电源负载1200W复印机时故障电弧的判断过程。故障电弧识别的具体步骤同实施例1。图8中从上到下依次是复印机负载归一化降噪后的采样电流g(t),g(t)的高频分量序列D1,待测序列yi与参考序列sj的相关系数ξij,负载所在电路的跳闸信号标志位Trip。由图8可以看到,本发明的算法可以实现开关电源负载复印机时故障电弧的准确识别判断。Fig. 8 is a judgment process of an arc fault when the switching power supply is loaded with a 1200W copier by the detection method of the present invention. The specific steps of arc fault identification are the same as those in Embodiment 1. In Fig. 8, from top to bottom are the sampled current g(t) after the load normalization and noise reduction of the copier, the high-frequency component sequence D1 of g(t), and the correlation coefficient ξ of the sequence to be measured y i and the reference sequence s j ij , the trip signal flag bit Trip of the circuit where the load is located. It can be seen from FIG. 8 that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the switching power supply loads the copier.
实施例6Example 6
图9是本发明检测方法在开关电源负载2台300W电脑并联时故障电弧的判断过程。故障电弧识别的具体步骤同实施例1。图9中从上到下依次是2台并联电脑负载归一化降噪后的采样电流g(t),g(t)的高频分量序列D1,待测序列yi与参考序列sj的相关系数ξij,负载所在电路的跳闸信号标志位Trip。由图9可以看到,本发明的算法可以实现开关电源负载2台电脑并联时故障电弧的准确识别判断。Fig. 9 is the judging process of fault arc when two 300W computers are connected in parallel by the detection method of the present invention. The specific steps of arc fault identification are the same as those in Embodiment 1. In Figure 9, from top to bottom are the sampling current g(t) after normalized noise reduction of two parallel computer loads, the high-frequency component sequence D1 of g(t), the sequence y i to be measured and the reference sequence s j Correlation coefficient ξ ij , the trip signal flag bit Trip of the circuit where the load is located. It can be seen from FIG. 9 that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the switching power supply load is connected in parallel with two computers.
本领域技术人员应理解,本发明的保护范围不限于上述的实施例,任何通过考察采样电流序列与正常运行时电流序列相关程度大小而做出故障电弧发生与否判断的做法均属于本发明检测方法之内。Those skilled in the art should understand that the scope of protection of the present invention is not limited to the above-mentioned embodiments, and any method of judging whether a fault arc occurs by examining the correlation between the sampling current sequence and the current sequence during normal operation belongs to the detection method of the present invention. within the method.
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Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106771798A (en) * | 2015-11-25 | 2017-05-31 | 山东建筑大学 | A kind of fault arc detection method based on the equal difference of wavelet coefficient |
CN108107321A (en) * | 2017-12-14 | 2018-06-01 | 科大智能电气技术有限公司 | A kind of electric power system fault waveform comparison method |
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CN109782126A (en) * | 2018-12-27 | 2019-05-21 | 上海交通大学 | Early fault detection method of distribution network based on human-like concept learning |
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CN113311227A (en) * | 2021-06-10 | 2021-08-27 | 中国科学技术大学先进技术研究院 | Current signal noise reduction method for fault arc diagnosis technology |
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US11693062B2 (en) | 2017-12-29 | 2023-07-04 | Huawei Technologies Co., Ltd. | Method for processing direct current electric arc and apparatus |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090168277A1 (en) * | 2007-12-28 | 2009-07-02 | Sriram Changali | Series arc fault current interrupter apparatus |
CN101696986A (en) * | 2009-10-26 | 2010-04-21 | 吴为麟 | Fault arc detection method and protection device adopting same |
CN102331543A (en) * | 2011-06-23 | 2012-01-25 | 上海市安全生产科学研究所 | Arc Fault Detection Method Based on Support Vector Machine |
CN103543375A (en) * | 2013-08-26 | 2014-01-29 | 上海交通大学 | Method for detecting alternating-current fault arcs on basis of wavelet transformation and time-domain hybrid features |
-
2015
- 2015-01-08 CN CN201510010224.5A patent/CN104597344A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090168277A1 (en) * | 2007-12-28 | 2009-07-02 | Sriram Changali | Series arc fault current interrupter apparatus |
CN101696986A (en) * | 2009-10-26 | 2010-04-21 | 吴为麟 | Fault arc detection method and protection device adopting same |
CN102331543A (en) * | 2011-06-23 | 2012-01-25 | 上海市安全生产科学研究所 | Arc Fault Detection Method Based on Support Vector Machine |
CN103543375A (en) * | 2013-08-26 | 2014-01-29 | 上海交通大学 | Method for detecting alternating-current fault arcs on basis of wavelet transformation and time-domain hybrid features |
Non-Patent Citations (3)
Title |
---|
DONGWEI L等: "A method for residential series arc fault detection and identification", 《PROCEEDINGS OF THE 55TH IEEE HOLM CONFERENCE ON ELECTRICAL CONTACTS》 * |
于德芳等: "基于小波包分解的相关分析的高压漏电保护系统的研究", 《煤矿机械》 * |
郑昕等: "低压串联电弧故障通用诊断方法的研究", 《电子测量与仪器学报》 * |
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