CN113296045B - Error correction method of micro current sensor based on sensing array - Google Patents

Error correction method of micro current sensor based on sensing array Download PDF

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CN113296045B
CN113296045B CN202110567187.3A CN202110567187A CN113296045B CN 113296045 B CN113296045 B CN 113296045B CN 202110567187 A CN202110567187 A CN 202110567187A CN 113296045 B CN113296045 B CN 113296045B
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CN113296045A (en
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唐欣
柴金超
彭超
尹子晨
何洋
蒋蕾
陈雅萱
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Changsha University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/02—Testing or calibrating of apparatus covered by the other groups of this subclass of auxiliary devices, e.g. of instrument transformers according to prescribed transformation ratio, phase angle, or wattage rating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R15/00—Details of measuring arrangements of the types provided for in groups G01R17/00 - G01R29/00, G01R33/00 - G01R33/26 or G01R35/00
    • G01R15/14—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks
    • G01R15/20—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks using galvano-magnetic devices, e.g. Hall-effect devices, i.e. measuring a magnetic field via the interaction between a current and a magnetic field, e.g. magneto resistive or Hall effect devices
    • G01R15/205—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks using galvano-magnetic devices, e.g. Hall-effect devices, i.e. measuring a magnetic field via the interaction between a current and a magnetic field, e.g. magneto resistive or Hall effect devices using magneto-resistance devices, e.g. field plates
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    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract

The invention provides an error correction method of a micro current sensor based on a sensing array, which comprises the following steps: collecting data of the dark compress depth, the electrifying current and the horizontal deviation of the micro current sensor under different working conditions to form a characteristic sample set, wherein the characteristic sample set comprises at least three groups of characteristic samples; acquiring magnetic induction intensities of three tunnel magneto-resistance sensors of the micro current sensor, and correcting the acquired magnetic induction intensities by correction factors; and calculating a correction factor by adopting an improved particle swarm algorithm, processing the characteristic sample set, the correction factor and the magnetic induction intensity measurement value by using a particle swarm model to obtain optimal data, and correcting the nonlinear error output by the sensor according to the optimal data.

Description

Error correction method of micro current sensor based on sensing array
Technical Field
The invention relates to the field of sensing correction, in particular to an error correction method of a micro current sensor based on a sensing array.
Background
Line current is one of the most important state quantities of the power grid, and current measurement is a precondition for guaranteeing power utilization safety and power grid stability. With the continuous development of material science and information science, the power grid is moving towards digitization and intellectualization. Advanced sensing technology is a key technology for providing accurate information for real-time monitoring and control of a power grid. Sensors for measuring line current are also developing in a direction of light weight and intellectualization.
The current traditional current measurement technology comprises a current transformer based on electromagnetic mutual inductance, a Rogowski coil and a Hall element based on Hall effect. These measures must either be clamped into the live conductor or directly into the conductor. With the development of social economy, more and more household power wiring is changed from a bright line to a dark line. Household electricity is used as a terminal of a power distribution network, and is a frequently-occurring ground of power leakage of the power distribution network. In order to detect the current of the energized wire in the wall and provide information basis for household safety electricity utilization, researchers have proposed a micro current detection device based on a TMR (tunneling magneto resistance) sensing array.
The TMR sensing array is an array consisting of magnetic field sensors based on the tunnel magnetoresistance effect, and the sensing element based on the tunnel magnetoresistance effect has the advantages of high sensitivity, low noise, small volume and the like. In the measurement of the dark-coated current conductor, the micro current sensor based on the TMR sensing array can really realize non-contact measurement. In practical use, because the hidden target magnetic field has uncertainty, the working electromagnetic environment has complexity, and the micro current sensor has the advantages of flexibility and portability in use and increases the difficulty in obtaining the specific magnetic field generated by the electrified lead. In the magnetic field induced by the TMR element, signal interferences such as a geomagnetic field, a power frequency excitation interference environment magnetic field, a high-frequency interference environment magnetic field and the like are superposed in addition to the magnetic field generated by the target lead. The power frequency excitation magnetic field refers to a magnetic field generated by ferromagnetic materials in the sensor carrier under the excitation of the power frequency magnetic field. In the process of signal conversion and transmission, a sensor sensitivity error, a magnetic field error generated by an electronic component, a hardware null shift error and the like are also introduced. In actual hand-held measurement, the manual operation does not necessarily achieve ideal position conditions, and therefore, measurement position deviation is also introduced.
For magnetoresistive sensor arrays without physical shielding, it appears to be necessary to correct the measurement errors algorithmically. Especially when the sensor carrier has certain soft magnetic properties, soft magnetic errors and non-linear errors can cause large errors in the measurement results.
Disclosure of Invention
The invention provides an error correction method of a micro current sensor based on a sensing array, which can improve the measurement accuracy of the sensing array device by one order of magnitude.
In order to achieve the above object, an embodiment of the present invention provides an error correction method for a micro current sensor based on a sensing array, where the micro current sensor is composed of three tunneling magneto-resistance sensors, the three tunneling magneto-resistance sensors are arranged on a main sensitive axis, the sensor arranged in the middle is a three-axis sensor, and the sensors on both sides are a first single-axis sensor and a second single-axis sensor; the error correction method includes:
step 1, collecting data of dark compress depth, electrifying current and horizontal deviation of the micro current sensor under different working conditions to form a characteristic sample set, wherein the characteristic sample set comprises at least three groups of characteristic samples;
step 2, obtaining the magnetic induction intensity of three tunnel magneto-resistance sensors of the micro current sensor, and correcting the obtained magnetic induction intensity by a correction factor;
and 3, calculating a correction factor by adopting an improved particle swarm algorithm, processing the characteristic sample set, the correction factor and the magnetic induction intensity measurement value by using a particle swarm model to obtain optimal data, and correcting the nonlinear error output by the sensor according to the optimal data.
The scheme of the invention has the following beneficial effects:
the invention provides an error correction method of a micro current sensor based on a sensing array, i.e. the micro current sensor device based on the sensing array can realize the current measurement function of completely non-contact, namely a paste and use mode, and the function of accurately positioning a dark-application electrified conductor. The blank of the non-contact measurement field of the hidden conductor is filled. The correction factor can be used to correct the nonlinear error in the measurement process, K11、K21、K31Is a linear error correction factor, K12、K22、K32Is a non-linear error correction factor. Through actual measurement, the error correction method can improve the measurement precision to the pure alignment2 times in the case of sexual error correction. Compared with the device without error correction, the measurement accuracy of the sensing array device can be improved by one order of magnitude. Meanwhile, a penalty function with the dark compress depth as a target is introduced, a penalty threshold value is processed, an optimal solution with the electrified current and the dark compress depth as multi-objective optimization is solved, and specific error correction is carried out according to the error type on the basis of analyzing the measurement error, so that the method has wider applicability.
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FIG. 1 is a schematic flow chart of an error correction method according to the present invention;
FIG. 2 is an array schematic of a sensor array of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides an error correction method for a micro current sensor based on a sensing array, where the micro current sensor is composed of three tunneling magneto-resistance sensors, the three tunneling magneto-resistance sensors are arranged on a main sensitive axis, the sensor arranged in the middle is a three-axis sensor, and the sensors on both sides are a first single-axis sensor and a second single-axis sensor; the error correction method includes:
step 1, collecting data of dark compress depth, electrifying current and horizontal deviation of the micro current sensor under different working conditions to form a characteristic sample set, wherein the characteristic sample set comprises at least three groups of characteristic samples;
step 2, obtaining the magnetic induction intensity of three tunnel magneto-resistance sensors of the micro current sensor, and correcting the obtained magnetic induction intensity by a correction factor;
and 3, calculating a correction factor by adopting an improved particle swarm algorithm, processing the characteristic sample set, the correction factor and the magnetic induction intensity measurement value by using a particle swarm model to obtain optimal data, and correcting the nonlinear error output by the sensor according to the optimal data.
Wherein, the step 1 specifically comprises:
presetting different dark compress depths D, different electrifying currents I and different horizontal deviations delta x as different working conditions, and taking the dark compress depths D, the different electrifying currents I and the different horizontal deviations delta x under the different working conditions as a characteristic sample set
Figure BDA0003081333540000031
The feature sample set
Figure BDA0003081333540000032
Is a 3 Xn multidimensional array, the row vectors are respectively the dark compress depth D, the energizing current I and the horizontal deviation delta x, the column vectors are respectively different preset groups, wherein the experimental group number n is more than or equal to 3, 3 characteristic quantities under n groups of running conditions are defined to form a characteristic sample set
Figure BDA0003081333540000033
Figure BDA0003081333540000034
Wherein x istThree characteristic quantities of dark compress depth D (t), current I (t) and horizontal deviation delta x (t) are set for the t-th set of preset experiments.
Wherein, the step 2 specifically comprises:
respectively acquiring the magnetic induction intensity of three tunnel magneto-resistance sensors of each preset group to be B'1x、B′2xAnd B'3x；
Correcting the three sensors by adopting correction factors, wherein the magnetic induction intensities output by the three tunnel magneto-resistance sensors after correction are respectively B1x、B2xAnd B3x；
B1x＝K11*B′1x+K12*B′1x 2
B2x＝K21*B′2x+K22*B′2x 2
B3x＝K31*B′3x+K32*B′3x 2
Wherein, K11、K12、K21、K22、K31、K32To correct for the factor, it is the quantity to be sought.
Wherein the step 3 comprises:
step 31, processing the characteristic sample set by adopting a particle swarm model
Figure BDA0003081333540000041
Obtaining the optimal solution by using the correction factors and the magnetic induction measurement values, and determining the optimal solution as K11、K12、K21、K22、K31、K32；
Step 32, the positions of the particles are respectively K11、K12、K21、K22、K31、K32Judging whether the position of the particle is a high-quality solution or not through a penalty function, and if the position of the particle is in a non-high-quality solution range, punishing the fitness of the particle; if the position of the particle is in the high-quality solution range, calculating the fitness of the particle according to a fitness formula;
step 33, calculating the fitness of each particle from the feature sample set
Figure BDA0003081333540000042
Calculating a penalty function and an adaptive value calculation formula;
step 34, iteratively updating the speed and the position of each particle based on the individual optimal position of the particle, the global optimal position of the particle swarm, the inertia weight, the first learning factor, the second learning factor, the maximum iteration times and the convergence precision to obtain the position of each particle after iterative update,
and step 35, the global optimal particle position after the iteration is terminated is an optimal solution, and the nonlinear error output by the sensor is corrected according to the optimal solution.
Wherein, the step 3 specifically comprises:
setting basic parameters of the improved particle swarm, including the swarm size N, the inertia weight omega and the learning factor 1c1Learning factor 2c2Iteration convergence precision eps and maximum iteration number imaxUpper limit value v of particle flight speedmaxAnd a lower limit value vminUpper limit of particle position popmaxAnd lower limit value of popminPenalty threshold upper limit value hmaxAnd a lower limit value hmin；
Initializing each particle position according to the set upper limit value pop of the particle positionmaxAnd lower limit value of popminRandomly distributing the particle populations on the position intervals by using a rand function;
if the fitness of the particles P (j) is less than the individual optimal fitness Pbest(j) Then assigning the fitness of the particle to the individual optimal fitness Pbest(j) (ii) a If the fitness of the particles P (j) is less than the global optimal fitness PgbestThen assigning the fitness of the particle to the global optimal fitness Pgbest；
Updating and iterating the position and the speed of each particle, wherein the iteration is according to the formula:
Figure BDA0003081333540000051
Figure BDA0003081333540000052
judgment B1xAnd B3xThe value of the two is more assigned to the variable A, and the value of the two is less assigned to the variable C; calculating to respectively obtain a first dark compress depth, a second dark compress depth and a third dark compress depth:
Figure BDA0003081333540000053
Figure BDA0003081333540000054
Figure BDA0003081333540000055
Djmean(t)＝(Dj1(t)+Dj2(t)+Dj3(t))/3
the first dark depth is obtained by outputting correction value B from the first single-axis sensor1xThe second uniaxial sensor outputs a correction value B3xFeature sample set
Figure BDA0003081333540000056
And the sensor distance d is calculated;
the second dark compress depth is obtained by outputting a correction value B by a variable A and a three-axis sensor2xFeature sample set
Figure BDA0003081333540000057
And the sensor distance d is calculated;
the third dark compress depth is the corrected value B output by the three-axis sensor and the variable C2xFeature sample set
Figure BDA0003081333540000058
And the sensor distance d is calculated;
wherein, the value of the horizontal deviation delta x is constantly not negative, and the direction of the horizontal deviation is B1xAnd B3xIs determined if B is1x＞B3xThe electrified lead deflects to the first single-axis sensor; if B is1x＜B3xThe electrified lead deflects to the second single-axis sensor;
judging whether the position of the particle is in a high-quality solution range by using a penalty function pf(j)：
Figure BDA0003081333540000059
The judgment conditions are as follows: p is a radical off(j) H is more than or equal to h, and h is a penalty threshold;
Figure BDA0003081333540000061
if the penalty function value of the particle p (j) is larger than or equal to the penalty threshold value, giving a larger penalty value to the fitness of the particle;
if the penalty function value of the particle p (j) is smaller than the penalty threshold, the particle is in the range of the quality solution, and the fitness value of the particle is obtained by calculating a fitness function; fitness function is f (p (j))
Figure BDA0003081333540000062
Figure BDA0003081333540000063
Figure BDA0003081333540000064
Figure BDA0003081333540000065
The iteration times reach the maximum iteration times or the global optimal fitness PgbestAnd if the iteration convergence precision eps is smaller than the iteration convergence precision eps, terminating the iteration calculation, otherwise, continuing to calculate and update.
As shown in fig. 2, the micro current Sensor based on the sensing array is in an array form formed by two single-axis sensors and one three-axis Sensor, and can position the current conducting wire and measure the current of the current conducting wire by using the signals output by the sensors, the schematic diagram of the array is shown in fig. 2, and the Sensor1, the Sensor2 and the Sensor3 are respectively a first TMR (tunneling magneto resistance) Sensor, a second TMR Sensor and a third TMR Sensor on a main sensitive axis. And establishing a rectangular coordinate system by taking the second TMR sensor as a center. The Sensor1 and the Sensor3 are single-axis sensors, the Sensor2 is a three-axis Sensor, and three sensitive axes of the Sensor2 correspond to x, y and z axes of a rectangular coordinate system respectively. The sensitive axes of the Sensor1 and the Sensor3 are on the same straight line with the x sensitive axis of the Sensor2, the Sensor1, the Sensor2 and the Sensor3 are on the straight line, the straight line is called as the main sensitive axis of the sensing array, and the y sensitive axis and the z sensitive axis of the Sensor2 are auxiliary sensitive axes. Sensor1 and Sensor3 are equidistant from Sensor2 and are both at Sensor spacing d. And the xoy plane in the rectangular coordinate system is the plane where the sensor is located. The micro current sensor can realize accurate positioning of the relative position of the electrified conducting wire under the condition that the specific position of a measurement target is unknown, and has the advantages of completely non-contact high-precision current measurement and no need of clamping the conducting wire or connecting the conducting wire.
In order to verify the correctness of the correction method provided by the text, a physical test is carried out on the basis of a developed measuring device. Different dark compress depths D, energizing currents I and horizontal deviations Δ x are set in preset experimental groups, respectively, wherein the dark compress depths D are set to a range of values between 20mm and 40mm, the energizing currents are set to a range of values between 100mA and 4000mA, and the horizontal deviations are set to a range of values between 0 and 10 mm. The distance d between the sensors of the measuring device is set to be 15mm, and a characteristic sample set is formed according to actually measured data
Figure BDA0003081333540000071
And carrying out iterative solution by improving a particle swarm algorithm. The following table is a partial iteration of the modified particle swarm algorithm.
Figure BDA0003081333540000072
Figure BDA0003081333540000081
In table, K11、K12、K21、K22、K31、K32To await a quantity, Derror(%) is the error percentage of the dark compress depth, Ierror(%) is the percent error of the wire currentRatio, Derror(%) and Ierror(%) together constitutes an index for measuring the degree of the algorithm to solve the problems, Derror(%) and IerrorThe smaller the (%), the better the solution for improving the particle swarm algorithm. When the iteration times of the experiment reach 100 times, the algorithm meets the precision requirement, and the global optimal particle fitness is obtained. At this time K11、K12、K21、K22、K31、K32For a global optimal solution, the dark compress depth error percentage and the wire current error percentage at this time are-0.2139% and 0.168748%, respectively. And the obtained global optimal solution is substituted into the first three formulas to finish the correction of the nonlinear error of the sensor output.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (5)

1. The error correction method of the micro current sensor based on the sensing array is characterized in that the micro current sensor consists of three tunnel magneto-resistance sensors, the three tunnel magneto-resistance sensors are arranged on a main sensitive shaft, the sensor arranged in the middle is a three-shaft sensor, and the sensors on two sides are a first single-shaft sensor and a second single-shaft sensor; the error correction method includes:
step 1, collecting data of dark compress depth, electrifying current and horizontal deviation of the micro current sensor under different working conditions to form a characteristic sample set, wherein the characteristic sample set comprises at least three groups of characteristic samples;
step 2, obtaining the magnetic induction intensity of three tunnel magneto-resistance sensors of the micro current sensor, and correcting the obtained magnetic induction intensity by a correction factor;
and 3, calculating a correction factor by adopting an improved particle swarm algorithm, processing the characteristic sample set, the correction factor and the magnetic induction intensity measurement value by using a particle swarm model to obtain optimal data, and correcting the nonlinear error output by the sensor according to the optimal data.
2. The method for correcting the error of the micro current sensor based on the sensing array according to claim 1, wherein the step 1 specifically comprises:
presetting different dark compress depths D, different electrifying currents I and different horizontal deviations delta x as different working conditions, and taking the dark compress depths D, the different electrifying currents I and the different horizontal deviations delta x under the different working conditions as a characteristic sample set
Figure FDA0003081333530000011
The feature sample set
Figure FDA0003081333530000012
Is a 3 Xn multidimensional array, the row vectors are respectively the dark compress depth D, the energizing current I and the horizontal deviation delta x, the column vectors are respectively different preset groups, wherein the experimental group number n is more than or equal to 3, and 3 characteristic quantities under n groups of running conditions are defined to form a characteristic sample set
Figure FDA0003081333530000013
Figure FDA0003081333530000014
Wherein x istThree characteristic quantities of dark compress depth D (t), current I (t) and horizontal deviation delta x (t) are set for the t-th set of preset experiments.
3. The method for correcting the error of the micro current sensor based on the sensing array according to claim 1, wherein the step 2 specifically comprises:
respectively acquiring the magnetic induction intensity of three tunnel magneto-resistance sensors of each preset group to be B'1x、B′2xAnd B'3x；
Adopt the schoolThe positive factors correct the three sensors, and the magnetic induction intensities output by the three corrected tunnel magneto-resistance sensors are respectively B1x、B2xAnd B3x；
B1x＝K11*B′1x+K12*B′1x 2
B2x＝K21*B′2x+K22*B′2x 2
B3x＝K31*B′3x+K32*B′3x 2
Wherein, K11、K12、K21、K22、K31、K32To correct for the factor, it is the quantity to be sought.
4. The method for error correction of a micro current sensor based on a sensor array according to claim 3, wherein the step 3 comprises:
step 31, processing the characteristic sample set by adopting a particle swarm model
Figure FDA0003081333530000021
Obtaining the optimal solution by using the correction factors and the magnetic induction measurement values, and determining the optimal solution as K11、K12、K21、K22、K31、K32；
Step 32, the positions of the particles are respectively K11、K12、K21、K22、K31、K32Judging whether the position of the particle is a high-quality solution or not through a penalty function, and if the position of the particle is in a non-high-quality solution range, punishing the fitness of the particle; if the position of the particle is in the high-quality solution range, calculating the fitness of the particle according to a fitness formula;
step 33, calculating the fitness of each particle from the feature sample set
Figure FDA0003081333530000022
Penalty functionCalculating with an adaptive value calculation formula;
step 34, iteratively updating the speed and the position of each particle based on the individual optimal position of the particle, the global optimal position of the particle swarm, the inertia weight, the first learning factor, the second learning factor, the maximum iteration times and the convergence precision to obtain the position of each particle after iterative update,
and step 35, the global optimal particle position after the iteration is terminated is an optimal solution, and the nonlinear error output by the sensor is corrected according to the optimal solution.
5. The method for correcting the error of the micro current sensor based on the sensing array according to claim 4, wherein the step 3 specifically comprises:
setting basic parameters of the improved particle swarm, including the swarm size N, the inertia weight omega and the learning factor 1c1Learning factor 2c2Iteration convergence precision eps and maximum iteration number imaxUpper limit value v of particle flight speedmaxAnd a lower limit value vminUpper limit of particle position popmaxAnd lower limit value of popminPenalty threshold upper limit value hmaxAnd a lower limit value hmin；
Initializing each particle position according to the set upper limit value pop of the particle positionmaxAnd lower limit value of popminRandomly distributing the particle populations on the position intervals by using a rand function;
if the fitness of the particles P (j) is less than the individual optimal fitness Pbest(j) Then assigning the fitness of the particle to the individual optimal fitness Pbest(j) (ii) a If the fitness of the particles P (j) is less than the global optimal fitness PgbestThen assigning the fitness of the particle to the global optimal fitness Pgbest；
Updating and iterating the position and the speed of each particle, wherein the iteration is according to the formula:
Figure FDA0003081333530000031
Figure FDA0003081333530000032
judgment B1xAnd B3xThe value of the two is more assigned to the variable A, and the value of the two is less assigned to the variable C; calculating to respectively obtain a first dark compress depth, a second dark compress depth and a third dark compress depth:
Figure FDA0003081333530000033
Figure FDA0003081333530000034
Figure FDA0003081333530000035
Djmean(t)＝(Dj1(t)+Dj2(t)+Dj3(t))/3
the first dark depth is obtained by outputting correction value B from the first single-axis sensor1xThe second uniaxial sensor outputs a correction value B3xFeature sample set
Figure FDA0003081333530000036
And the sensor distance d is calculated;
the second dark compress depth is obtained by outputting a correction value B by a variable A and a three-axis sensor2xFeature sample set
Figure FDA0003081333530000039
And the sensor distance d is calculated;
the third dark compress depth is the corrected value B output by the three-axis sensor and the variable C2xFeature sample set
Figure FDA00030813335300000310
And the sensor distance d is calculated;
wherein, the value of the horizontal deviation delta x is constantly not negative, and the direction of the horizontal deviation is B1xAnd B3xIs determined if B is1x＞B3xThe electrified lead deflects to the first single-axis sensor; if B is1x＜B3xThe electrified lead deflects to the second single-axis sensor;
judging whether the position of the particle is in a high-quality solution range by using a penalty function pf(j)：
Figure FDA0003081333530000037
The judgment conditions are as follows: p is a radical off(j) H is more than or equal to h, and h is a penalty threshold;
Figure FDA0003081333530000038
if the penalty function value of the particle p (j) is larger than or equal to the penalty threshold value, giving a larger penalty value to the fitness of the particle;
if the penalty function value of the particle p (j) is smaller than the penalty threshold, the particle is in the range of the quality solution, and the fitness value of the particle is obtained by calculating a fitness function; fitness function is f (p (j))
Figure FDA0003081333530000041
Figure FDA0003081333530000042
Figure FDA0003081333530000043
Figure FDA0003081333530000044
The iteration times reach the maximum iteration times or the global optimal fitness PgbestAnd if the iteration convergence precision eps is smaller than the iteration convergence precision eps, terminating the iteration calculation, otherwise, continuing to calculate and update.
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