CN117420398A - Low-voltage distribution line series fault arc detection method based on waveform similarity algorithm - Google Patents
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Abstract
The invention provides a low-voltage distribution line series fault arc detection method based on a waveform similarity algorithm. Firstly, arc fault tests and fault characteristic analysis are carried out on common loads, and a voltage sag threshold value for fault arc detection starting and an H threshold value for arc fault identification are given. And thirdly, based on the voltage sag characteristic of the load end when the arc fault occurs, taking the voltage sag value of the load end exceeding a voltage sag threshold as a detection starting condition, then calculating the similarity of the voltage waveforms of the load end based on a Hausdorff distance algorithm, and judging that the arc fault occurs when the Hausdorff distance H value between the voltage of the load end and the sine wave exceeds an H threshold. Finally, the embodiment verifies the identification effect of the detection method provided by the invention, and the result shows that the method can effectively improve the reliability of arc fault detection.
Description
Technical Field
The invention belongs to the field of low-voltage distribution network series arc detection, and mainly relates to a low-voltage distribution line series arc detection method suitable for large-scale nonlinear load access.
Background
The firing of the distribution lines is a significant cause of electrical fires, with over 40% of electrical fires being initiated by the electrical lines each year. Among them, arc faults caused by terminal loosening, insulation breakage, and conductor damage are one of the causes of ignition of the distribution line.
The arc faults can be divided into series arc faults and parallel arc faults according to the occurrence conditions of the arc faults. In parallel arc faults, current is only limited by arc impedance, current magnitude is close to short-circuit current, most of overcurrent protection equipment can act as short-circuit faults, and meanwhile, a sensor can directly detect arc voltage and current waveforms with common characteristics, so that detection difficulty is low; in the series arc fault, the current is influenced by the arc impedance and the load impedance, the current is generally smaller than the normal working current of the load, the waveform is greatly influenced by the load type, the traditional circuit protection equipment cannot effectively protect the load, and meanwhile, the arc voltage is difficult to directly detect because the occurrence position of the arc fault is uncertain. Therefore, a special series fault arc detection method needs to be designed to protect the line.
Existing methods for detecting arc faults based on arc current have drawbacks in terms of reliability. Along with the diversification of load types at the user side, the waveform characteristics of low-voltage distribution network line current are diversified, and a large number of nonlinear loads are connected to cause the current similar to arc current in normal operation to have fault characteristics, so that the existing series arc fault detection method is extremely easy to misjudge, and the requirement for effectively protecting the distribution line is difficult to meet.
Disclosure of Invention
The invention provides a low-voltage distribution line fault arc detection method based on a waveform similarity algorithm, which aims to solve the problem of misjudgment of a traditional fault arc detection method caused by nonlinear load working current and enhance the reliability of fault arc detection method detection under a complex load environment.
The technical method adopted by the invention is as follows:
a low-voltage distribution line series fault arc detection method based on a waveform similarity algorithm comprises the following steps:
s1: testing voltage sag values of common different types of loads and determining maximum voltage drop u of different types of loads max And determining a start-up threshold U based on the maximum pressure drop set ；
S2: calculating the waveform similarity between the load end voltage waveform and the standard sine wave when the load of the asynchronous motor with the maximum possible capacity is connected, and determining a detection threshold H by combining the fault discrimination reliability coefficient set ；
S3: sampling and calculating the effective value of the voltage of the single-phase load end in real time, and calculating the difference value of the effective values of the voltage of the load end in the current period and the last period to obtain a voltage sag value u drop (t) according to the voltage sag value u drop (t) and a start threshold U set Comparing to judge whether fault arc detection is started or not;
s4: if the fault arc detection is started, calculating a Hausdorff distance H between the fault voltage of the load end and the standard sine wave;
s5: when Hausdorff distance H in S4 exceeds detection threshold H set When the arc fault occurs to the line, the arc fault is judged; otherwise, judging that the arc fault does not occur.
Further, the step S1 specifically includes:
4 representative linear and nonlinear loads are selected, wherein the selected representative loads are an electric heater, an electromagnetic oven, an electric water heater and a dust collector respectively, the electric heater is a 1000W linear load, and the power factor is the same as the electric heaterElectromagnetic oven is 1800W linearLoad, power factor->The electric water heater is 3000W linear load, and the power factor is +.>The dust collector is 1300W nonlinear load, and the power factor is +.>
Testing voltage sag values of 4 kinds of representative loads, respectively sampling and recording load terminal voltages of 4 kinds of representative loads when the loads are connected, sampling rate is 1kHz, calculating effective values of primary terminal voltages in real time when sampling of each power frequency period is finished, and simultaneously calculating difference values of the effective values of the load terminal voltages of the current period and the last period, and recording the difference values as voltage sag values u sag (t) the voltage sag value is calculated as follows:
u sag (t)＝u(t)-u(t-T) (1)
wherein u is sag (T) is a voltage sag value, u (T) is a load terminal voltage effective value of the current period, and u (T-T) is a load terminal voltage effective value of the previous period;
comparing the voltage sag values of the load ends after the 4 different types of loads are connected into a maximum voltage sag u max For the 4 representative loads, the voltage dip values generated during the connection are recorded as u respectively sag1 、u sag2 、u sag3 、u sag4 And comparing the voltage sag values of the load ends after the different loads are connected into a maximum voltage sag u max ：
u max ＝max[u sag1 ,u sag2 ,u sag3 ,u sag4 ] (2)
Wherein, max represents the maximum value of four types of load access voltage drops;
setting a reliability coefficient for detecting startSetting a starting threshold U for fault arc detection set Protecting and protectingThe representative load is reliable without misoperation when being accessed:
wherein: u (u) max The maximum pressure drop is connected to the load;for detecting the reliability coefficient of the start-up, it is preferably 1.1 to 1.3.
Further, the waveform similarity between the voltage waveform of the load end and the standard sine wave is Hausdorff distance between the two waveforms.
Further, the step S2 specifically includes:
when the load of the asynchronous motor with the maximum possible capacity is accessed, the sampling array u= [ u ] of the load terminal voltage of the asynchronous motor is recorded 1 ,u 2 ,…,u n ]The sampling rate is 10kHz, n is the sampling point number of one power frequency period, n=200 under the sampling rate of 10kHz, and the voltage maximum value u in the voltage array is recorded mag ＝max[u 1 ,u 2 ,…,u n ]；
From the sampled voltage maximum u mag Generating a magnitude u mag Is a standard sine wave of (2);
the data window length of 0.1T is used as the data input for calculating the waveform similarity, namely Hausdorff distance, wherein T is a power frequency period, so that 10 Hausdorff distance calculated values are sequentially calculated in one power frequency period to form a Hausdorff distance sequence H= [ H ] 1 ,H 2 ,…,H 10 ]The calculation formula of each Hausdorff data in the Hausdorff distance sequence is as follows:
wherein: I.I is the distance norm between array u and array y; min represents any point a in u i Sequentially calculating the distance norm values between the distance norm values and all the data points in y, and comparing to obtain the minimum value in all the distance norm values, ifThe u contains n data, and then the minimum value of n distance norms is obtained through calculation; max represents a maximum value among the n distance norm minimums; a, a i E u represents all elements in the voltage array u; b j The E y represents all elements in the standard sine wave array y;
calculating the waveform similarity Hausdorff distance series H= [ H ] 1 ,H 2 ,…,H 10 ]The maximum value of the internal element is denoted as H max Detection threshold H set Set to H max Multiplying reliability coefficients
In formula (5):generally 1.5 to 3.
Further, the voltage maximum value u obtained by sampling mag Generating a magnitude u mag The specific steps are as follows:
the first step: determining a sampling frequency f, and calculating a sampling period Ts=1/f according to the sampling frequency f;
and a second step of: generating a discrete time sequence T with a period of time t=0.02s, wherein the initial value of T is 0, the step length is a sampling period Ts, and generating a time sequence t= [0, ts,2 x Ts, (N-1) x Ts ] with a total length N starting from 0, wherein n=t/Ts is the length of the time sequence T;
and a third step of: for each time point t in the time series i Calculating the value y of the standard sine wave at the time point i ＝u mag sin(2*π*50*t i ) Thereby obtaining a discrete standard sine wave numerical sequence y= [ y ] 1 ,y 2 ,…,y n ]N is the number of sampling points per power frequency period at a sampling rate of 10kHz, n=200.
Further, the step S3 specifically includes:
sampling single-phase load terminal voltage in real time at a sampling rate of 1kHz, calculating the effective value of the primary terminal voltage in real time when sampling of each power frequency period is finished, and simultaneously calculating the difference value of the effective values of the load terminal voltages of the current period and the previous period to obtain a voltage sag value u drop (t)；
Taking the overrun of the voltage sag value as a detected starting condition, and when the voltage sag value u is drop (t) is greater than the starting threshold U set When the fault arc is detected and started; voltage sag value u drop (t) is less than the starting threshold U set When fault arc detection is not started, the starting strategy is as follows:
after fault arc detection is started, the voltage of the load end is sampled, the sampling rate is 10kHz, the sampling data storage window length is the next power frequency period after detection is started, and therefore a load end voltage sampling array u is obtained arc ＝[u 1 ,u 2 ,…,u n ]N is the sampling point number of one power frequency period, n=200 at the sampling rate of 10kHz, and the voltage maximum value u in the voltage array is recorded peak ＝max[u 1 ,u 2 ,…,u n ]。
Compared with the prior art, the invention has the following technical effects:
the voltage starting threshold value provided by the invention can realize the distinction between the nonlinear load and the arc fault, thereby avoiding the problem of false starting caused by the high similarity of the operating current characteristics of the nonlinear load and the fault arc current characteristics; the invention calculates the distortion degree of the voltage waveform of the load end by utilizing the waveform similarity, realizes reliable detection of fault arc, can effectively identify the starting of the asynchronous motor, and overcomes the defects of similar fault arc detection methods. In conclusion, the method provided by the invention can effectively improve the accuracy of the series arc detection of the low-voltage distribution line, the criterion is simple, the terminal calculation force requirement is low, the effectiveness and the practicability are both considered, and the method has a relatively high engineering practical value.
Drawings
FIG. 1 is a flow chart of a method for detecting a series fault arc of a low-voltage distribution line based on a waveform similarity algorithm according to an embodiment of the present invention;
FIG. 2 is a block diagram of a residential building power distribution in accordance with an embodiment of the present invention;
FIG. 3 illustrates voltage dip levels for different load accesses in accordance with an embodiment of the present invention;
FIG. 4 is a Hausdorff distance of a load terminal voltage at the start of a three-phase asynchronous motor according to an embodiment of the present invention;
fig. 5 is a Hausdorff distance of the load terminal voltage at the occurrence of a series arc fault in accordance with an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
Residential buildings are selected as research objects, and the distribution network is of a three-level distribution network structure, as shown in fig. 2. The Matlab/Simulink simulation software is utilized to carry out simulation test on the series arc fault detection method based on the voltage sag characteristic and the Hausdorff distance algorithm, and the simulation circuit of the arc fault is built based on a Cassie arc model, and specific parameters of the model are as follows: the distribution network side is formed by equivalent 220V constant voltage source, and 3, 5 and 7 times of harmonic sources with effective values of 5V, 3V and 2V are added, and the primary phases of the harmonic sources are pi/6, pi/3 and pi/2 respectively. The length of the line r is 100m, and the inner diameter is 16mm 2 Line resistance r=0.1Ω, line inductance l=80 μh, line s 1 A length of 100m and an inner diameter of 10mm 2 Line resistance r=0.16Ω, line inductance l=95 μh, line L 1 A length of 100m and an inner diameter of 4mm 2 Line resistance r=0.5Ω, line inductance l=100 μh. The three-phase asynchronous motor is a squirrel-cage asynchronous motor with 7.5kW of input power, 400V rated voltage, 50Hz rated frequency and 1430RPM rated rotating speed. The load for arc fault test comprises an electric heater, an electric water heater, an electromagnetic oven and a dust collector, and the adaptability of the novel series arc fault detection method is analyzed by combining specific scenes.
Examples
And according to two scene unfolding example description of arc faults generated when different loads are connected in during normal operation and the electric heater is in load operation.
Example 1: before the load is connected, the line is not loaded, and four different types of loads are connected into the line at the moment of 0.06 s. As shown in fig. 1:
s1: testing voltage sag values of common loads (electric heater, electromagnetic oven, electric water heater and dust collector) of different types when the loads are connected to determine maximum voltage drop u of the loads of different types when the loads are connected to the electric heater, electromagnetic oven, electric water heater and dust collector max And determining a start-up threshold U based on the maximum pressure drop set 。
The specific implementation process of the step S1 is as follows:
step one: load access test for load type of table 1:
table 1 test load main parameters
Step two: the test result of the test load voltage sag is shown in figure 3, and the sag value u of the load A drop1 Load B dip value u=4.22V drop2 Load C dip value u=4.68v drop3 =8.61V, load D dip value u drop4 ＝6.13V。
Step three: load C has the maximum starting voltage sag, u in four loads max ＝max[u drop1 ,u drop2 ,u drop3 ,u drop4 ]＝8.61V。
Step four: the start-up threshold is calculated according to equation (3). k (k) r 1 el 1.2, setting the obtained starting threshold U set ＝10.33V。
S2: calculating the waveform similarity between the load end voltage waveform and the standard sine wave when the load of the asynchronous motor with the maximum possible capacity is connected, and determining a detection threshold H by combining the fault discrimination reliability coefficient set . The specific implementation process is as follows:
step one: when the load of the 7.5kW asynchronous motor is connected, a sampling array u= [ u ] of the load end voltage of the asynchronous motor is recorded 1 ,u 2 ,…,u n ]，n＝200, and recording the voltage maximum value u in the voltage array peak ＝max[u 1 ,u 2 ,…,u n ]＝292.9V。
Step two: from the sampled voltage maximum u mag Generating a magnitude u mag The steps are as follows:
the first step: the sampling frequency f=10 kHz is determined, from which the sampling period ts=1/f=0.1 ms is calculated.
And a second step of: a discrete time sequence T of a period of time t=0.02 s is generated, where the initial value of T is 0 and the step size is 0.1ms of the sampling period. The generation results in a time series t= [0, ts,2 x Ts, (N-1) x Ts ], with a total length of N starting from 0, where n=200.
And a third step of: for each time point t in the time series i Calculating the value y of the standard sine wave at the time point i ＝292.9sin(2*π*50*t i ) Thereby obtaining a discrete standard sine wave numerical sequence y= [ y ] 1 ,y 2 ,…,y n ]，n＝200。
Step three: the data window length of 0.1T is used as data input for calculating the Hausdorff distance of the waveform similarity, wherein T is a power frequency period, so that 10 Hausdorff distance calculated values can be obtained in one power frequency period to form a Hausdorff distance sequence H= [ H ] 1 ,H 2 ,…,H 10 ]。
Step four: calculating the Hausdorff distance series H= [ H ] 1 ,H 2 ,…,H 10 ]Maximum value of internal element, H max =4.81, as shown in fig. 4,taking 2, detecting threshold +.>
Example 2: before a fault, the distribution line normally operates with an electric heater load; at 0.06s, line l 1 A series arc fault occurs.
The implementation of S1 and S2 is the same as in example 1, whereby the start-up threshold U will be determined set ＝10.33V, detection threshold
S3: sampling and calculating the effective value of the voltage of the single-phase load end in real time, and calculating the difference value of the effective values of the voltage of the load end in the current period and the last period to obtain a voltage sag value u drop (t) according to the voltage sag value u drop (t) and a start threshold U set And comparing to judge whether the fault arc detection is started or not. The specific implementation process is as follows:
step one: sampling the voltage of a single-phase load terminal in real time at a sampling rate of 1kHz, calculating the effective value of the voltage of the primary terminal in real time when the sampling of each power frequency period is finished, and simultaneously calculating the difference value of the effective values of the voltage of the load terminal in the current period and the load terminal in the previous period according to the similar method of S1 to obtain a voltage sag value u drop (t)＝23.8V。
Step two: u (u) drop (t)＞U set And detecting start-up.
Step three: after fault arc detection is started, the voltage of the load end is sampled, the sampling rate is 10kHz, the sampling data storage window length is the next power frequency period after detection is started, and therefore a load end voltage sampling array u is obtained arc ＝[u 1 ,u 2 ,…,u n ]N=200, and records the voltage maximum u in the voltage array peak ＝max[u 1 ,u 2 ,…,u n ]＝276.63V。
S4: from the sampled voltage maximum u peak = 276.63V, the amplitude u is generated according to the similar procedure to the generation of standard sine wave peak And calculates Hausdorff distance H between the load side fault voltage and the standard sine wave according to a similar method to the Hausdorff distance calculation described above, as shown in fig. 5.
S5: according to the calculation result of S4, H max ＝56.35＞H set The arc fault is determined to occur and is connected with the line l 1 The actual situation of the occurrence of the series arc fault is consistent, and the detection result is accurate.
In summary, the present embodiment verifies the correctness and superiority of the present invention. The invention realizes the accurate discrimination of the series arc faults under the connection of the asynchronous motor and the nonlinear load, solves the problem that the traditional detection method is easy to generate detection misdiscrimination, and improves the reliability of fault detection results.
Claims (6)
1. The method for detecting the series fault arc of the low-voltage distribution line based on the waveform similarity algorithm is characterized by comprising the following steps of:
s1: testing voltage sag values of common different types of loads and determining maximum voltage drop u of different types of loads max And determining a start-up threshold U based on the maximum pressure drop set ；
S2: calculating the waveform similarity between the load end voltage waveform and the standard sine wave when the load of the asynchronous motor with the maximum possible capacity is connected, and determining a detection threshold H by combining the fault discrimination reliability coefficient set ；
S3: sampling and calculating the effective value of the voltage of the single-phase load end in real time, and calculating the difference value of the effective values of the voltage of the load end in the current period and the last period to obtain a voltage sag value u drop (t) according to the voltage sag value u drop (t) and a start threshold U set Comparing to judge whether fault arc detection is started or not;
s4: if the fault arc detection is started, calculating a Hausdorff distance H between the fault voltage of the load end and the standard sine wave;
s5: when Hausdorff distance H in S4 exceeds detection threshold H set When the arc fault occurs to the line, the arc fault is judged; otherwise, judging that the arc fault does not occur.
2. The method for detecting a series fault arc of a low-voltage distribution line based on a waveform similarity algorithm according to claim 1, wherein the step S1 specifically comprises:
4 representative linear and nonlinear loads are selected, wherein the selected representative loads are an electric heater, an electromagnetic oven, an electric water heater and a dust collector respectively, the electric heater is a 1000W linear load, and the power factor is the same as the electric heaterThe electromagnetic oven is 1800W linear load, and the power factor is +.>The electric water heater is 3000W linear load, and the power factor is +.>The dust collector is 1300W nonlinear load, and the power factor is +.>
Testing voltage sag values of 4 kinds of representative loads, respectively sampling and recording load terminal voltages of 4 kinds of representative loads when the loads are connected, sampling rate is 1kHz, calculating effective values of primary terminal voltages in real time when sampling of each power frequency period is finished, and simultaneously calculating difference values of the effective values of the load terminal voltages of the current period and the last period, and recording the difference values as voltage sag values u sag (t) the voltage sag value is calculated as follows:
u sag (t)＝u(t)-u(t-T) (1)
wherein u is sag (T) is a voltage sag value, u (T) is a load terminal voltage effective value of the current period, and u (T-T) is a load terminal voltage effective value of the previous period;
comparing the voltage sag values of the load ends after the 4 different types of loads are connected into a maximum voltage sag u max For the 4 representative loads, the voltage dip values generated during the connection are recorded as u respectively sag1 、u sag2 、u sag3 、u sag4 And comparing the voltage sag values of the load ends after the different loads are connected into a maximum voltage sag u max ：
u max ＝max[u sag1 ,u sag2 ,u sag3 ,u sag4 ] (2)
Wherein, max represents the maximum value of four types of load access voltage drops;
setting a reliability coefficient for detecting startSetting a starting threshold U for fault arc detection set The representative load is ensured to be reliably and not to malfunction when being accessed:
wherein: u (u) max The maximum pressure drop is connected to the load;for detecting the reliability coefficient of the start-up, it is preferably 1.1 to 1.3.
3. The method for detecting the series fault arc of the low-voltage distribution line based on the waveform similarity algorithm according to claim 1, wherein the waveform similarity between the voltage waveform of the load end and the standard sine wave is Hausdorff distance between the two waveforms.
4. The method for detecting a series fault arc of a low-voltage distribution line based on a waveform similarity algorithm according to claim 1, wherein the step S2 specifically comprises:
when the load of the asynchronous motor with the maximum possible capacity is accessed, the sampling array u= [ u ] of the load terminal voltage of the asynchronous motor is recorded 1 ,u 2 ,…,u n ]The sampling rate is 10kHz, n is the sampling point number of one power frequency period, n=200 under the sampling rate of 10kHz, and the voltage maximum value u in the voltage array is recorded mag ＝max[u 1 ,u 2 ,…,u n ]；
From the sampled voltage maximum u mag Generating a magnitude u mag Is a standard sine wave of (2);
data input for calculating waveform similarity, i.e. Hausdorff distance, by taking a data window length of 0.1T, wherein T isThe power frequency period, thereby, 10 Hausdorff distance calculation values are calculated in sequence in one power frequency period to form a Hausdorff distance sequence H= [ H ] 1 ,H 2 ,…,H 10 ]The calculation formula of each Hausdorff data in the Hausdorff distance sequence is as follows:
wherein: I.I is the distance norm between array u and array y; min represents any point a in u i Sequentially calculating distance norm values between the data points in y and the data points in y, comparing to obtain the minimum value in all the distance norm values, and if n data are contained in u, calculating to obtain the minimum value of n distance norms; max represents a maximum value among the n distance norm minimums; a, a i E u represents all elements in the voltage array u; b j The E y represents all elements in the standard sine wave array y;
calculating the waveform similarity Hausdorff distance series H= [ H ] 1 ,H 2 ,…,H 10 ]The maximum value of the internal element is denoted as H max Detection threshold H set Set to H max Multiplying reliability coefficients
In formula (5):generally 1.5 to 3.
5. The method for detecting a series fault arc of a low voltage distribution line based on a waveform similarity algorithm as claimed in claim 4, wherein the voltage maximum value u obtained by sampling is mag Generating a magnitude u mag The specific steps are as follows:
the first step: determining a sampling frequency f, and calculating a sampling period Ts=1/f according to the sampling frequency f;
and a second step of: generating a discrete time sequence T with a period of time t=0.02s, wherein the initial value of T is 0, the step length is a sampling period Ts, and generating a time sequence t= [0, ts,2 x Ts, (N-1) x Ts ] with a total length N starting from 0, wherein n=t/Ts is the length of the time sequence T;
and a third step of: for each time point t in the time series i Calculating the value y of the standard sine wave at the time point i ＝u mag sin(2*π*50*t i ) Thereby obtaining a discrete standard sine wave numerical sequence y= [ y ] 1 ,y 2 ,…,y n ]N is the number of sampling points per power frequency period at a sampling rate of 10kHz, n=200.
6. The method for detecting a series fault arc of a low-voltage distribution line based on a waveform similarity algorithm according to claim 1, wherein the step S3 specifically comprises:
sampling single-phase load terminal voltage in real time at a sampling rate of 1kHz, calculating the effective value of the primary terminal voltage in real time when sampling of each power frequency period is finished, and simultaneously calculating the difference value of the effective values of the load terminal voltages of the current period and the previous period to obtain a voltage sag value u drop (t)；
Taking the overrun of the voltage sag value as a detected starting condition, and when the voltage sag value u is drop (t) is greater than the starting threshold U set When the fault arc is detected and started; voltage sag value u drop (t) is less than the starting threshold U set When fault arc detection is not started, the starting strategy is as follows:
after fault arc detection is started, sampling voltage of a load end, sampling rate being 10kHz, and sampling data storage window length being detectionThe next power frequency period after starting is used for obtaining a load end voltage sampling array u arc ＝[u 1 ,u 2 ,…,u n ]N is the sampling point number of one power frequency period, n=200 at the sampling rate of 10kHz, and the voltage maximum value u in the voltage array is recorded peak ＝max[u 1 ,u 2 ,…,u n ]。
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