技术领域technical field
本发明涉及电力设备状态检测方法，尤其是涉及一种基于分合闸电流波形的断路器状态检测方法。The invention relates to a method for detecting the state of electric equipment, in particular to a method for detecting the state of a circuit breaker based on an opening and closing current waveform.
背景技术Background technique
随着城市规模的扩大和用电量的增长，电网越来越庞大，单位人员所管辖的配电网设备数量急剧增加，因此提高电网运行效率和现场劳动生产率，开展电网设备状态检修，转变电网设备运行管理模式迫在眉睫。With the expansion of city scale and the growth of electricity consumption, the power grid is getting bigger and bigger, and the number of distribution network equipment under the jurisdiction of the unit personnel has increased sharply. Equipment operation management mode is imminent.
电网开关设备是数量最庞大的电网设备，断路器是最复杂、最重要的开关设备，被广泛用于发电厂、变电站、开关站及用电线路上。断路器的安全稳定运行，不仅可以有效限制短路电流从而降低故障电流产生的电热效应，而且对提高整个电力系统的安全稳定性有重要意义。Power grid switchgear is the largest number of power grid equipment, and circuit breakers are the most complex and important switchgear, which are widely used in power plants, substations, switch stations and power lines. The safe and stable operation of the circuit breaker can not only effectively limit the short-circuit current to reduce the electrothermal effect caused by the fault current, but also has great significance for improving the safety and stability of the entire power system.
断路器随着动作次数的增加，其故障概率也显著增加。60％-70％的断路器缺陷或故障是由断路器机构缺陷造成的，包括：机构卡涩、油脂凝固、弹簧老化、慢分慢合、连杆传动轴断裂和机构拒动等。As the number of operations of the circuit breaker increases, its failure probability also increases significantly. 60%-70% of circuit breaker defects or failures are caused by circuit breaker mechanism defects, including: mechanism jamming, grease solidification, spring aging, slow opening and closing, broken connecting rod drive shaft and mechanism refusal to move, etc.
目前常用的断路器故障检测包括红外检测，以及局部放电检测，这类检测方式可以较好地实现断路器在线监测。但其仅仅针对电接触缺陷和绝缘介质缺陷进行诊断，对断路器机构故障尚不能进行有效检测。Currently commonly used circuit breaker fault detection includes infrared detection and partial discharge detection. This type of detection method can better realize the online monitoring of circuit breakers. However, it only diagnoses electrical contact defects and insulation medium defects, and cannot effectively detect circuit breaker mechanism faults.
发明内容Contents of the invention
本发明是为避免上述现有技术所存在的不足，提供一种基于波形特征识别的断路器状态检测方法，利用采集获得的断路器分合闸电流信号，通过识别并对比正常与故障时的电流波形，从而对断路器状态进行高效、准确检测。In order to avoid the shortcomings of the above-mentioned prior art, the present invention provides a circuit breaker state detection method based on waveform feature recognition, which utilizes the circuit breaker opening and closing current signals obtained by collecting, and compares the normal and fault currents by identifying and comparing Waveform, so as to efficiently and accurately detect the state of the circuit breaker.
本发明为解决技术问题采用如下技术方案：The present invention adopts following technical scheme for solving technical problems:
本发明基于波形特征识别的断路器状态检测方法的特点是包括如下步骤：The characteristics of the circuit breaker state detection method based on waveform feature recognition of the present invention include the following steps:
步骤1、采集一段正常工况下断路器的正常分合闸电流信号x(t)；Step 1. Collect the normal opening and closing current signal x(t) of a circuit breaker under normal working conditions;
步骤2、将电流信号x(t)分解为m个固有模式函数分量IMF，即：Step 2. Decompose the current signal x(t) into m intrinsic mode function components IMF, namely:
x(t)＝∑ci(t)+ri(t)；x(t)=∑ci (t)+ri (t);
其中，ci(t)为电流信号x(t)的一个IMF分量，i＝1,…,m；ri(t)为差值信号；Among them, ci (t) is an IMF component of the current signal x(t), i=1,...,m; ri (t) is the difference signal;
步骤3、将前p阶IMF分量相加，p≤m，构成去噪后的电流信号z(t)为：Step 3. Add the first p-order IMF components, p≤m, to form the current signal z(t) after denoising:
步骤4、对于所述电流去噪信号z(t)进行分析，得到正常工况下断路器的分合闸电流信号特征向量集X为：是与正常分合闸电流信号的特征向量所对应的特征点的坐标；Step 4, analyzing the current denoising signal z(t), and obtaining the feature vector set X of the opening and closing current signal of the circuit breaker under normal working conditions is: is the coordinate of the feature point corresponding to the feature vector of the normal opening and closing current signal;
步骤5、针对现场实际工况进行信号采集，获得被测断路器的分合闸电流信号，按照与步骤2～步骤4的相同方式获得实际工况下被测断路器的电流信号特征向量集Y为：是与被测断路器的分合闸电流信号的特征向量所对应的特征点的坐标；Step 5. Collect signals according to the actual working conditions on site to obtain the opening and closing current signals of the circuit breaker under test, and obtain the current signal feature vector set Y of the circuit breaker under test in the same way as steps 2 to 4 for: is the coordinate of the feature point corresponding to the feature vector of the opening and closing current signal of the circuit breaker under test;
步骤6、计算获得向量集合相似度d为：Step 6. Calculate and obtain the vector set similarity d as:
其中，k为特征向量的个数，j＝1，2…k；Wherein, k is the number of feature vectors, j=1, 2...k;
若d值大于或等于0.9，判断被测断路器与正常工况下断路器的分合闸电流信号波形相似，现场实际工况为正常工况，被测断路器为正常；If the d value is greater than or equal to 0.9, it is judged that the waveform of the opening and closing current signal of the circuit breaker under test is similar to that of the circuit breaker under normal working conditions, the actual working condition on site is normal working condition, and the circuit breaker under test is normal;
若d值小于0.9，因被测断路器与正常工况下断路器的分合闸电流信号波形存在较大偏差，判断为被测断路器出现故障。If the d value is less than 0.9, it is judged that the circuit breaker under test is faulty because there is a large deviation between the opening and closing current signal waveform of the circuit breaker under test and the circuit breaker under normal working conditions.
本发明基于波形特征识别的断路器状态检测方法的特点也在于：所述步骤2中将电流信号x(t)分解为若干个固有模式函数分量IMF是按如下步骤进行：The feature of the circuit breaker state detection method based on waveform feature recognition of the present invention is also that: in the step 2, decomposing the current signal x(t) into several intrinsic mode function components IMF is carried out according to the following steps:
步骤2.1、对所述电流信号x(t)进行求导，获得时间序列y(t)，将所述时间序列y(t)中相邻两点乘积记为：pyr(t)，则有：pyr(t)＝yr(t)×yr-1(t)，r＝2,3,…,n-1；Step 2.1, deriving the current signal x(t) to obtain a time series y(t), and recording the product of two adjacent points in the time series y(t) as: pyr (t), then we have : pyr (t) = yr (t) × yr-1 (t), r = 2,3,...,n-1;
步骤2.2、根据pyr(t)和y(t)的值，按如下方式依次找寻电流信号x(t)的所有局部极大值点eb(t)和所有局部极小值点es(t)：Step 2.2. According to the values of pyr (t) and y(t), find all local maximum points eb(t) and all local minimum points es(t) of the current signal x(t) sequentially as follows :
若pyr(t)<0，且yr-1(t)<0，则xr-1(t)为局部极小值点es(t)；If pyr (t)<0, and yr-1 (t)<0, then xr-1 (t) is the local minimum point es(t);
若pyr(t)<0，且yr-1(t)>0，则xr-1(t)为局部极大值点eb(t)；If pyr (t)<0, and yr-1 (t)>0, then xr-1 (t) is the local maximum point eb(t);
若pyr(t)>0，则xr-1(t)为非极值点；If pyr (t)>0, then xr-1 (t) is a non-extreme point;
当pyr(t)＝0，且yr-1(t)＝0，令pyr(t)'为：pyr(t)'＝yr(t)×yr-2(t)，则有：When pyr (t)=0, and yr-1 (t)=0, let pyr (t)' be: pyr (t)'=yr (t)×yr-2 (t), Then there are:
若pyr(t)'<0，且yr-2(t)<0，则xr-1(t)为局部极小值点es(t)；If pyr (t)'<0, and yr-2 (t)<0, then xr-1 (t) is the local minimum point es(t);
若pyr(t)'<0，且yr-2(t)>0，则xr-1(t)为局部极大值点eb(t)；If pyr (t)'<0, and yr-2 (t)>0, then xr-1 (t) is the local maximum point eb(t);
若yr-2(t)＝0，则xr-1(t)为非极值点；If yr-2 (t)=0, then xr-1 (t) is a non-extreme point;
步骤2.3、将步骤2.2中所有局部极大值点eb(t)和所有局部极小值点es(t)用三次样条插值函数s(t)连接起来，分别求出上包络线emax(t)和下包络线emin(t)，所述三次样条插值函数s(t)是在电流信号x(t)的每一个小区间上不超过三次的多项式，并有：Step 2.3. Connect all local maximum points eb(t) and all local minimum points es(t) in step 2.2 with the cubic spline interpolation function s(t) to obtain the upper envelope emax (t) and the lower envelope emin (t), the cubic spline interpolation function s (t) is a polynomial no more than three times in each small interval of the current signal x (t), and has:
其中，mq和mq+1为三次样条插值函数s(t)在对应的小区间两端点处的二阶导数值；所述小区间定义为[tq,tq+1]；Among them, mq and mq+1 are the second-order derivative values of the cubic spline interpolation function s(t) at the two ends of the corresponding small interval; the small interval is defined as [tq ,tq+1 ];
步骤2.4、计算获得上包络线和下包络线的均值m(t)为：m(t)＝(emax(t)+emin(t))/2，将电流信号x(t)减去所述均值m(t)，得到更新时间序列y1(t)；Step 2.4, calculate and obtain the mean value m(t) of the upper envelope and the lower envelope as: m(t)=(emax (t)+emin (t))/2, the current signal x(t) Subtract the mean value m(t) to obtain an updated time series y1 (t);
步骤2.5、判断所述更新时间序列y1(t)是否同时满足条件A和条件B：Step 2.5, judging whether the update time series y1 (t) satisfies condition A and condition B at the same time:
条件A、在整个信号长度上，极值点和过零点的数目相等或者相差一个；Condition A. Over the entire signal length, the number of extreme points and zero-crossing points is equal or differs by one;
条件B、在任意时刻，由极大值点定义的上包络线和由极小值点定义的下包络线的平均值为零；Condition B. At any moment, the average value of the upper envelope defined by the maximum point and the lower envelope defined by the minimum point is zero;
若同时满足条件A和条件B，则y1(t)为固有模式函数分量；If condition A and condition B are satisfied at the same time, then y1 (t) is the intrinsic mode function component;
若不同时满足条件A和条件B，则将y1(t)作为一个原始分量，重复步骤2.1～2.4，直至更新时间序列y1(t)同时满足条件A和条件B时，将更新时间序列y1(t)记为ci(t)，ci(t)即为电流信号x(t)的一个固有模式函数分量；If condition A and condition B are not met at the same time, then take y1 (t) as an original component and repeat steps 2.1 to 2.4 until the updated time series y1 (t) meets condition A and condition B at the same time, the time series will be updated y1 (t) is denoted as ci (t), and ci (t) is an intrinsic mode function component of the current signal x(t);
步骤2.6、将ci(t)从电流信号x(t)中分离出来，得到差值信号ri(t)为：ri(t)＝x(t)-ci(t)；Step 2.6, separating ci (t) from the current signal x(t), and obtaining the difference signal ri (t) is: ri (t)=x(t)-ci (t);
以所述差值信号ri(t)作为更新的待处理电流信号；Using the difference signal ri (t) as an updated current signal to be processed;
步骤2.7、重复步骤2.1～2.6，直至满足迭代终止准则，得到全部m个固有模式函数分量，所述迭代终止准则为：所得到的更新时间序列yi(t)为窄带信号；Step 2.7, repeating steps 2.1 to 2.6 until the iteration termination criterion is satisfied, and all m intrinsic mode function components are obtained. The iteration termination criterion is: the obtained update time series yi (t) is a narrowband signal;
所述电流信号x(t)即被分解为由式(1)表征的若干个IMF分量和剩余的差值信号之和：The current signal x(t) is decomposed into the sum of several IMF components represented by formula (1) and the remaining difference signal:
x(t)＝∑ci(t)+ri(t)。x(t)=Σci (t)+ri (t).
本发明基于波形特征识别的断路器状态检测方法的特点也在于：The characteristics of the circuit breaker state detection method based on waveform feature recognition of the present invention are also:
所述步骤4是按如下过程获得特征向量集X：The step 4 is to obtain the feature vector set X according to the following process:
步骤4.1、记信号起始点O为坐标原点O(0,0)，从起始点O开始，每间隔n0个点选取一个特征点T，所述特征点T的坐标记为n0＝round(f/1000)，round表示取整数运算，特征点个数w为：w＝round(n/n0)，j＝1,2,...,w，f为采样频率，n采样点个数；Step 4.1, mark the starting point O of the signal as the coordinate origin O(0,0), start from the starting point O, select a feature point T at intervals of n0 points, and the coordinates of the feature point T are marked as n0 = round(f/1000), round means integer calculation, the number of feature points w is: w=round(n/n0 ), j=1,2,...,w, f is the sampling frequency, n number of sampling points;
步骤4.2、分别连接坐标原点O与各特征点T，得到各特征向量，以所述各特征向量构成电流信号的特征向量集X，Step 4.2, respectively connect the coordinate origin O and each feature point T to obtain each feature vector, and form the feature vector set X of the current signal with each feature vector,
所述步骤5是按如下过程获得特征向量集Y：The step 5 is to obtain the feature vector set Y according to the following process:
针对现场实际工况进行信号采集，获得被测断路器的分合闸电流信号，按照与步骤4.1～步骤4.2相同的方式获得特征向量集Y，Carry out signal collection according to the actual working conditions on site, obtain the opening and closing current signals of the circuit breaker under test, and obtain the feature vector set Y in the same way as steps 4.1 to 4.2,
断路器分合闸控制回路的电流信号中包含了断路器分合闸过程的各种时间参量、机构动作参量、以及线圈电气参量等信息，利用该电流信号可以对断路器进行全方位的检测。同时可以降低检修工具的投资，提高现场检测效率，优化配电网设备状态管理模式和状态检修策略。本发明通过求取断路器分合闸电流波形的特征向量集合，并将实际特征向量集合与正常工况下进行对比，从而得到断路器的运行状态，与现有技术相比，本发明有益效果体现在：The current signal of the circuit breaker opening and closing control circuit contains information such as various time parameters, mechanism action parameters, and coil electrical parameters during the opening and closing process of the circuit breaker. The current signal can be used for all-round detection of the circuit breaker. At the same time, it can reduce the investment of maintenance tools, improve the efficiency of on-site inspection, and optimize the status management mode and status maintenance strategy of distribution network equipment. The present invention obtains the eigenvector set of the opening and closing current waveform of the circuit breaker, and compares the actual eigenvector set with the normal working condition, thereby obtaining the operating state of the circuit breaker. Compared with the prior art, the present invention has beneficial effects Reflected in:
1、本发明方法中对电流信号进行分解，并将分解后保留电流信号主要特征的IMF进行组合，形成新的去噪电流信号，从而能够最大限度避免噪声及其它干扰对分析结果的影响。1. In the method of the present invention, the current signal is decomposed, and the IMF that retains the main characteristics of the current signal is combined after the decomposition to form a new denoised current signal, so that the influence of noise and other interference on the analysis result can be avoided to the greatest extent.
2、本发明方法对于电流信号的全波形进行较为密集的特征点提取，从而使得特征向量集合能够更好地描述电流波形的变化情况。2. The method of the present invention performs dense feature point extraction on the full waveform of the current signal, so that the set of feature vectors can better describe the variation of the current waveform.
3、本发明方法中定义向量集合相似度，能够充分考虑各个特征向量的差异情况，从而得到更为准确的对比结果。3. The similarity of the vector set defined in the method of the present invention can fully consider the difference of each feature vector, thereby obtaining a more accurate comparison result.
4、以本发明方法所得到的向量集合相似度，可以为断路器状态检测提供定量判断依据，从而得到更为准确的得到断路器运行状态。4. The vector set similarity obtained by the method of the present invention can provide a quantitative judgment basis for circuit breaker state detection, thereby obtaining a more accurate operating state of the circuit breaker.
5、本发明方法可以实现断路器故障的高效、准确诊断，通过引入特征向量集合消除波形特征提取不准确对结果判定的影响，提高计算准确度及效率。5. The method of the present invention can realize efficient and accurate diagnosis of circuit breaker faults, eliminate the influence of inaccurate waveform feature extraction on result judgment by introducing feature vector sets, and improve calculation accuracy and efficiency.
附图说明Description of drawings
图1是本发明断路器故障检测的流程图。Fig. 1 is a flow chart of circuit breaker fault detection in the present invention.
具体实施方式detailed description
参见图1，本实施例中基于波形特征识别的断路器状态检测方法包括如下步骤：Referring to Figure 1, the circuit breaker state detection method based on waveform feature recognition in this embodiment includes the following steps:
步骤1、采集一段正常工况下断路器的正常分合闸电流信号x(t)。Step 1. Collect a normal opening and closing current signal x(t) of a circuit breaker under normal working conditions.
步骤2、将电流信号x(t)分解为m个固有模式函数分量Intrinsic Mode Function，简称为IMF分量，即：Step 2. Decompose the current signal x(t) into m intrinsic mode function components Intrinsic Mode Function, referred to as IMF components, namely:
x(t)＝∑ci(t)+ri(t)；x(t)=∑ci (t)+ri (t);
其中，ci(t)为电流信号x(t)的一个IMF分量，i＝1,…,m；ri(t)为差值信号。Wherein, ci (t) is an IMF component of the current signal x(t),i =1,...,m;ri (t) is a difference signal.
步骤3、将前p阶IMF分量相加，p≤m，构成去噪后的电流信号z(t)为：Step 3. Add the first p-order IMF components, p≤m, to form the current signal z(t) after denoising:
步骤4、对于电流去噪信号z(t)进行分析，得到正常工况下断路器的分合闸电流信号特征向量集X为：是与正常分合闸电流信号的特征向量所对应的特征点的坐标。Step 4. Analyze the current denoising signal z(t), and obtain the feature vector set X of the opening and closing current signal of the circuit breaker under normal working conditions: is the coordinate of the feature point corresponding to the feature vector of the normal opening and closing current signal.
步骤5、针对现场实际工况进行信号采集，获得被测断路器的分合闸电流信号，按照与步骤2～步骤4的相同方式获得实际工况下被测断路器的电流信号特征向量集Y为：是与被测断路器的分合闸电流信号的特征向量所对应的特征点的坐标。Step 5. Collect signals according to the actual working conditions on site to obtain the opening and closing current signals of the circuit breaker under test, and obtain the current signal feature vector set Y of the circuit breaker under test in the same way as steps 2 to 4 for: is the coordinate of the feature point corresponding to the feature vector of the opening and closing current signal of the circuit breaker under test.
步骤6、计算获得向量集合相似度d为：Step 6. Calculate and obtain the vector set similarity d as:
其中，k为特征向量的个数，j＝1，2…k；Wherein, k is the number of feature vectors, j=1, 2...k;
若d值大于或等于0.9，判断被测断路器与正常工况下断路器的分合闸电流信号波形相似，现场实际工况为正常工况，被测断路器为正常。If the d value is greater than or equal to 0.9, it is judged that the waveform of the opening and closing current signal of the circuit breaker under test is similar to that of the circuit breaker under normal working conditions, the actual working condition on site is normal working condition, and the circuit breaker under test is normal.
若d值小于0.9，因被测断路器与正常工况下断路器的分合闸电流信号波形存在较大偏差，判断为被测断路器出现故障。If the d value is less than 0.9, it is judged that the circuit breaker under test is faulty because there is a large deviation between the opening and closing current signal waveform of the circuit breaker under test and the circuit breaker under normal working conditions.
具体实施中，步骤2中将电流信号x(t)分解为若干个固有模式函数分量IMF按如下步骤进行：In the specific implementation, in step 2, the current signal x(t) is decomposed into several intrinsic mode function components IMF according to the following steps:
步骤2.1、对电流信号x(t)进行求导，获得时间序列y(t)，将时间序列y(t)中相邻两点乘积记为：pyr(t)，则有：pyr(t)＝yr(t)×yr-1(t)，r＝2,3,…,n-1。Step 2.1. Deriving the current signal x(t) to obtain the time series y(t), and recording the product of two adjacent points in the time series y(t) as: pyr (t), then: pyr ( t) = yr (t) × yr-1 (t), r = 2, 3, . . . , n-1.
步骤2.2、根据pyr(t)和y(t)的值，按如下方式依次找寻电流信号x(t)的所有局部极大值点eb(t)和所有局部极小值点es(t)：Step 2.2. According to the values of pyr (t) and y(t), find all local maximum points eb(t) and all local minimum points es(t) of the current signal x(t) sequentially as follows :
若pyr(t)<0，且yr-1(t)<0，则xr-1(t)为局部极小值点es(t)；If pyr (t)<0, and yr-1 (t)<0, then xr-1 (t) is the local minimum point es(t);
若pyr(t)<0，且yr-1(t)>0，则xr-1(t)为局部极大值点eb(t)；If pyr (t)<0, and yr-1 (t)>0, then xr-1 (t) is the local maximum point eb(t);
若pyr(t)>0，则xr-1(t)为非极值点；If pyr (t)>0, then xr-1 (t) is a non-extreme point;
当pyr(t)＝0，且yr-1(t)＝0，令pyr(t)'为：pyr(t)'＝yr(t)×yr-2(t)，则有：When pyr (t)=0, and yr-1 (t)=0, let pyr (t)' be: pyr (t)'=yr (t)×yr-2 (t), Then there are:
若pyr(t)'<0，且yr-2(t)<0，则xr-1(t)为局部极小值点es(t)；If pyr (t)'<0, and yr-2 (t)<0, then xr-1 (t) is the local minimum point es(t);
若pyr(t)'<0，且yr-2(t)>0，则xr-1(t)为局部极大值点eb(t)；If pyr (t)'<0, and yr-2 (t)>0, then xr-1 (t) is the local maximum point eb(t);
若yr-2(t)＝0，则xr-1(t)为非极值点。If yr-2 (t)=0, then xr-1 (t) is a non-extreme point.
步骤2.3、将步骤2.2中所有局部极大值点eb(t)和所有局部极小值点es(t)用三次样条插值函数s(t)连接起来，分别按常规方法求出上包络线emax(t)和下包络线emin(t)，三次样条插值函数s(t)是在电流信号x(t)的每一个小区间上不超过三次的多项式，并有：Step 2.3, connect all local maximum points eb(t) and all local minimum points es(t) in step 2.2 with the cubic spline interpolation function s(t), and obtain the upper envelope according to the conventional method The line emax (t) and the lower envelope line emin (t), the cubic spline interpolation function s (t) is a polynomial of no more than three times in each small interval of the current signal x (t), and has:
其中，mq和mq+1为三次样条插值函数s(t)在对应的小区间两端点处的二阶导数值；小区间定义为[tq,tq+1]。Among them, mq and mq+1 are the second-order derivative values of the cubic spline interpolation function s(t) at the two ends of the corresponding small interval; the small interval is defined as [tq ,tq+1 ].
步骤2.4、计算获得上包络线和下包络线的均值m(t)为：m(t)＝(emax(t)+emin(t))/2，将电流信号x(t)减去均值m(t)，得到更新时间序列y1(t)。Step 2.4, calculate and obtain the mean value m(t) of the upper envelope and the lower envelope as: m(t)=(emax (t)+emin (t))/2, the current signal x(t) Subtract the mean m(t) to get the updated time series y1 (t).
步骤2.5、判断更新时间序列y1(t)是否同时满足条件A和条件B：Step 2.5. Determine whether the updated time series y1 (t) satisfies condition A and condition B at the same time:
条件A、在整个信号长度上，极值点和过零点的数目相等或者相差一个；Condition A. Over the entire signal length, the number of extreme points and zero-crossing points is equal or differs by one;
条件B、在任意时刻，由极大值点定义的上包络线和由极小值点定义的下包络线的平均值为零；Condition B. At any moment, the average value of the upper envelope defined by the maximum point and the lower envelope defined by the minimum point is zero;
若同时满足条件A和条件B，则y1(t)为固有模式函数分量；If condition A and condition B are satisfied at the same time, then y1 (t) is the intrinsic mode function component;
若不同时满足条件A和条件B，则将y1(t)作为一个原始分量，重复步骤2.1～2.4，直至更新时间序列y1(t)同时满足条件A和条件B时，将更新时间序列y1(t)记为ci(t)，ci(t)即为电流信号x(t)的一个固有模式函数分量。If condition A and condition B are not met at the same time, then take y1 (t) as an original component and repeat steps 2.1 to 2.4 until the updated time series y1 (t) meets condition A and condition B at the same time, the time series will be updated y1 (t) is denoted asci (t), andci (t) is an intrinsic mode function component of the current signal x(t).
步骤2.6、将ci(t)从电流信号x(t)中分离出来，得到差值信号ri(t)为：ri(t)＝x(t)-ci(t)；Step 2.6, separating ci (t) from the current signal x(t), and obtaining the difference signal ri (t) is: ri (t)=x(t)-ci (t);
以差值信号ri(t)作为更新的待处理电流信号。The difference signal ri (t) is used as an updated current signal to be processed.
步骤2.7、重复步骤2.1～2.6，直至满足迭代终止准则，得到全部m个固有模式函数分量，迭代终止准则为：所得到的更新时间序列yi(t)为窄带信号；Step 2.7, repeat steps 2.1 to 2.6 until the iteration termination criterion is satisfied, and all m intrinsic mode function components are obtained. The iteration termination criterion is: the obtained update time series yi (t) is a narrowband signal;
电流信号x(t)即被分解为由式(1)表征的若干个IMF分量和剩余的差值信号之和：The current signal x(t) is decomposed into the sum of several IMF components represented by formula (1) and the remaining difference signal:
x(t)＝∑ci(t)+ri(t)。x(t)=Σci (t)+ri (t).
本实施例中按如下过程获得特征向量集X：In this embodiment, the feature vector set X is obtained as follows:
步骤4.1、记信号起始点O为坐标原点O(0,0)，从起始点O开始，每间隔n0个点选取一个特征点T，特征点T的坐标记为n0＝round(f/1000)，round表示取整数运算，特征点个数w为：w＝round(n/n0)，j＝1,2,...,w，f为采样频率，n采样点个数。Step 4.1, record the starting point O of the signal as the coordinate origin O(0,0), start from the starting point O, select a feature point T every n0 points, and the coordinates of the feature point T are marked as n0 = round(f/1000), round means integer calculation, the number of feature points w is: w=round(n/n0 ), j=1,2,...,w, f is the sampling frequency, nNumber of sampling points.
步骤4.2、分别连接坐标原点O与各特征点T，得到各特征向量，以各特征向量构成电流信号的特征向量集X，Step 4.2, respectively connect the coordinate origin O and each feature point T to obtain each feature vector, and use each feature vector to form the feature vector set X of the current signal,
步骤5是按如下过程获得特征向量集Y：Step 5 is to obtain the feature vector set Y according to the following process:
针对现场实际工况进行信号采集，获得被测断路器的分合闸电流信号，按照与步骤4.1～步骤4.2相同的方式获得特征向量集Y，Carry out signal collection according to the actual working conditions on site, obtain the opening and closing current signals of the circuit breaker under test, and obtain the feature vector set Y in the same way as steps 4.1 to 4.2,
本实施例以某型号断路器样机为研究对象，并有：f＝10000Hz，n＝10000，电流信号x(t)电流补分解为六个IMF分量，p＝3，n0＝10，m＝1000，最终计算获得d＝0.35，判断断路器存在故障，通过实际检修验证，该断路器启动部件存在卡涩故障，从而验证了本发明方法的有效性和准确性。This embodiment takes a certain type of circuit breaker prototype as the research object, and has: f=10000Hz, n=10000, the current signal x(t) is decomposed into six IMF components, p=3, n0 =10, m= 1000, d = 0.35 obtained through the final calculation, it is judged that there is a fault in the circuit breaker, and it is verified through actual maintenance that the starting part of the circuit breaker has a stuck fault, thereby verifying the validity and accuracy of the method of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610908008.7ACN106526468B (en) | 2016-10-18 | 2016-10-18 | Circuit-breaker status detection method based on wave character identification |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610908008.7ACN106526468B (en) | 2016-10-18 | 2016-10-18 | Circuit-breaker status detection method based on wave character identification |
| Publication Number | Publication Date |
|---|---|
| CN106526468Atrue CN106526468A (en) | 2017-03-22 |
| CN106526468B CN106526468B (en) | 2019-03-15 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610908008.7AActiveCN106526468B (en) | 2016-10-18 | 2016-10-18 | Circuit-breaker status detection method based on wave character identification |
| Country | Link |
|---|---|
| CN (1) | CN106526468B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108021676A (en)* | 2017-12-07 | 2018-05-11 | 国家电网公司 | Data reconstruction method for defect library evaluation of integrated isolation circuit breaker |
| CN109708855A (en)* | 2017-10-25 | 2019-05-03 | 平高集团有限公司 | A method and device for monitoring the switching state of a high-voltage isolation switch |
| CN110174612A (en)* | 2019-06-10 | 2019-08-27 | 珠海东帆科技有限公司 | Low-voltage circuit breaker fault early-warning detection method |
| CN110647838A (en)* | 2019-09-19 | 2020-01-03 | 电子科技大学 | Geometric-shape-based one-dimensional signal feature extraction method for equipment working state recognition |
| CN112557004A (en)* | 2020-11-23 | 2021-03-26 | 广西电网有限责任公司电力科学研究院 | Method for detecting mechanical fault of high-voltage circuit breaker |
| CN113009374A (en)* | 2019-12-20 | 2021-06-22 | 常州星宇车灯股份有限公司 | Detection device and detection method for dynamic water flowing effect of OLED tail lamp |
| CN113552448A (en)* | 2021-08-05 | 2021-10-26 | 华能沾化热电有限公司 | Breaker state detection method |
| CN118330379A (en)* | 2024-06-17 | 2024-07-12 | 北京金冠智能电气科技有限公司 | Lightning arrester running state detection method, device, system and medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050110501A1 (en)* | 2003-11-25 | 2005-05-26 | Walker Michael L. | Electrical circuit test apparatus |
| CN102998544A (en)* | 2011-09-16 | 2013-03-27 | 河南电力试验研究院 | Method for diagnosing operating state of windings in short-circuiting of transformer |
| CN103176104A (en)* | 2013-02-04 | 2013-06-26 | 上海交通大学 | Transformer winding operating state diagnosis method |
| CN104360263A (en)* | 2014-10-29 | 2015-02-18 | 国家电网公司 | Circuit breaker operating mechanism fault diagnosis expert system based on opening and closing coil current |
| CN104808137A (en)* | 2015-05-19 | 2015-07-29 | 新疆特变电工自控设备有限公司 | Method for on-line judgment of breaker failure |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050110501A1 (en)* | 2003-11-25 | 2005-05-26 | Walker Michael L. | Electrical circuit test apparatus |
| CN102998544A (en)* | 2011-09-16 | 2013-03-27 | 河南电力试验研究院 | Method for diagnosing operating state of windings in short-circuiting of transformer |
| CN103176104A (en)* | 2013-02-04 | 2013-06-26 | 上海交通大学 | Transformer winding operating state diagnosis method |
| CN104360263A (en)* | 2014-10-29 | 2015-02-18 | 国家电网公司 | Circuit breaker operating mechanism fault diagnosis expert system based on opening and closing coil current |
| CN104808137A (en)* | 2015-05-19 | 2015-07-29 | 新疆特变电工自控设备有限公司 | Method for on-line judgment of breaker failure |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109708855A (en)* | 2017-10-25 | 2019-05-03 | 平高集团有限公司 | A method and device for monitoring the switching state of a high-voltage isolation switch |
| CN108021676A (en)* | 2017-12-07 | 2018-05-11 | 国家电网公司 | Data reconstruction method for defect library evaluation of integrated isolation circuit breaker |
| CN108021676B (en)* | 2017-12-07 | 2021-01-08 | 国家电网公司 | Data reconstruction method for defect library evaluation of integrated isolation circuit breaker |
| CN110174612A (en)* | 2019-06-10 | 2019-08-27 | 珠海东帆科技有限公司 | Low-voltage circuit breaker fault early-warning detection method |
| CN110647838A (en)* | 2019-09-19 | 2020-01-03 | 电子科技大学 | Geometric-shape-based one-dimensional signal feature extraction method for equipment working state recognition |
| CN110647838B (en)* | 2019-09-19 | 2023-03-31 | 电子科技大学 | Geometric-shape-based one-dimensional signal feature extraction method for equipment working state recognition |
| CN113009374A (en)* | 2019-12-20 | 2021-06-22 | 常州星宇车灯股份有限公司 | Detection device and detection method for dynamic water flowing effect of OLED tail lamp |
| CN112557004A (en)* | 2020-11-23 | 2021-03-26 | 广西电网有限责任公司电力科学研究院 | Method for detecting mechanical fault of high-voltage circuit breaker |
| CN113552448A (en)* | 2021-08-05 | 2021-10-26 | 华能沾化热电有限公司 | Breaker state detection method |
| CN118330379A (en)* | 2024-06-17 | 2024-07-12 | 北京金冠智能电气科技有限公司 | Lightning arrester running state detection method, device, system and medium |
| Publication number | Publication date |
|---|---|
| CN106526468B (en) | 2019-03-15 |
| Publication | Publication Date | Title |
|---|---|---|
| CN106526468A (en) | Breaker state detection method based on waveform characteristics identification | |
| CN105974265B (en) | A kind of electric network fault cause diagnosis method based on svm classifier technology | |
| CN103713237B (en) | A kind of power system transmission line short trouble diagnostic method | |
| WO2022067562A1 (en) | Method and device for diagnosing fault arc, and computer-readable storage medium | |
| CN103795144B (en) | Method for identifying disturbance occurrence time of power system based on fault recording data | |
| CN105093066A (en) | Line fault judgment method based on wavelet analysis and support vector machine | |
| CN109444667B (en) | Power distribution network early fault classification method and device based on convolutional neural network | |
| CN105425118A (en) | Multi-information fusion fault arc detection method and device | |
| CN109029959B (en) | A method for detecting mechanical state of transformer windings | |
| Bhui et al. | Application of recurrence quantification analysis to power system dynamic studies | |
| CN103323755A (en) | Method and system for recognition of GIS ultrahigh frequency partial discharge signal | |
| CN106932641B (en) | A Method of Grid Fault Diagnosis Based on Improved HHT Transformation | |
| CN107482621A (en) | A Method for Evaluating Transient Voltage Stability of Power Systems Based on Voltage Sequence Trajectories | |
| CN107632258A (en) | A kind of fan converter method for diagnosing faults based on wavelet transformation and DBN | |
| CN102567783A (en) | Expert fault analytical and diagnostic method of parallel mixed type power quality regulator | |
| CN106324406A (en) | Transformer direct-current magnetic bias fault diagnosis method and device | |
| CN115409335B (en) | Power system disturbance identification method considering unknown disturbance types based on deep learning | |
| CN110866366A (en) | XGboost algorithm-based island detection method for photovoltaic microgrid containing PHEV | |
| JP2021512377A (en) | How to detect early failure of the power grid | |
| CN111999591B (en) | Method for identifying abnormal state of primary equipment of power distribution network | |
| CN106443433A (en) | Monitoring system and monitoring method for circuit breaker state based on divide-shut brake coil current | |
| CN107147143B (en) | A method for establishing an early warning model for wind turbine cascading off-grid faults | |
| Sun et al. | Transmission line fault diagnosis method based on improved multiple SVM model | |
| CN109917245B (en) | Ultrasonic detection partial discharge signal mode identification method considering phase difference | |
| Paul et al. | Efficient and Lightweight Convolutional Neural Network-based Series DC Arc Fault Protection for PV Systems |
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |