CN116298550A - Method and device for measuring electric field strength - Google Patents
Method and device for measuring electric field strength Download PDFInfo
- Publication number
- CN116298550A CN116298550A CN202310559626.5A CN202310559626A CN116298550A CN 116298550 A CN116298550 A CN 116298550A CN 202310559626 A CN202310559626 A CN 202310559626A CN 116298550 A CN116298550 A CN 116298550A
- Authority
- CN
- China
- Prior art keywords
- electric field
- measured
- voltage
- relative humidity
- test
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005684 electric field Effects 0.000 title claims abstract description 152
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012360 testing method Methods 0.000 claims abstract description 111
- 238000013528 artificial neural network Methods 0.000 claims abstract description 52
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 7
- 239000008188 pellet Substances 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 238000005259 measurement Methods 0.000 description 20
- 238000010586 diagram Methods 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000000523 sample Substances 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 239000013618 particulate matter Substances 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0864—Measuring electromagnetic field characteristics characterised by constructional or functional features
- G01R29/0892—Details related to signal analysis or treatment; presenting results, e.g. displays; measuring specific signal features other than field strength, e.g. polarisation, field modes, phase, envelope, maximum value
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0807—Measuring electromagnetic field characteristics characterised by the application
- G01R29/0814—Field measurements related to measuring influence on or from apparatus, components or humans, e.g. in ESD, EMI, EMC, EMP testing, measuring radiation leakage; detecting presence of micro- or radiowave emitters; dosimetry; testing shielding; measurements related to lightning
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0864—Measuring electromagnetic field characteristics characterised by constructional or functional features
- G01R29/0871—Complete apparatus or systems; circuits, e.g. receivers or amplifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The embodiment of the disclosure relates to a method and a device for measuring electric field intensity. The method comprises the following steps: when a plurality of relative humidities are obtained, testing voltages of a plurality of test points in a test electric field under each relative humidity; for each relative humidity, sending the test voltage into the constructed neural network for training; optimizing the trained neural network based on the actual electric field strength and the actual voltage of the plurality of test points for each relative humidity; acquiring measured relative humidity and measured voltage of a target point in an electric field to be measured, and sending the measured relative humidity and the measured voltage into an optimized neural network; and outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network. According to the embodiment of the disclosure, the test voltage is trained and optimized under different environmental humidity through the neural network, and the optimized neural network is utilized, so that the measured electric field intensity of the target point is calculated and obtained when the measured relative humidity and the measured voltage of the target point are known.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of electric field intensity detection, in particular to a method and a device for measuring electric field intensity.
Background
The electric field strength is an important index for evaluating the electromagnetic environment near the direct current transmission line. The transmission line is located in an outdoor environment with variable meteorological conditions such as temperature, humidity and air particulate matter concentration, and great difficulty is brought to accurate measurement of electric field intensity.
The current-stage common direct-current electric field intensity detector is a field-grinding type direct-current electric field intensity detector, and the working principle is as follows: the probe of the field-grinding type direct-current field intensity detector consists of a rotatable metal shielding blade and a fixed metal sensing blade, when the direct-current field intensity is measured, the shielding blade rotates at a high speed, the exposed area of the sensing blade in a direct-current electric field is periodically changed, so that the sensing charge quantity gathered on the surface of the sensing blade is also periodically changed, and an alternating sensing current signal is generated; the direct current field strength can be determined by detecting the corresponding signal. Because the probe of the direct current field intensity detector contains more metal parts, the change of the relative humidity of the environment can influence the measurement result of the electric field intensity. Studies have shown that when the relative humidity of the environment in which the electric field intensity detector is located is greater than 80%, the electric field measurement error can reach 30%, and the measurement error increases with the increase of the relative humidity.
Accordingly, there is a need to improve one or more problems in the related art as described above.
It is noted that this section is intended to provide a background or context for the technical solutions of the present disclosure as set forth in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
It is an aim of embodiments of the present disclosure to provide a method and apparatus for measuring electric field strength, which overcome, at least in part, one or more of the problems due to the limitations and disadvantages of the related art.
According to a first aspect of embodiments of the present disclosure, there is provided a method of measuring electric field strength, the method comprising:
when a plurality of relative humidities are obtained, testing voltages of a plurality of test points in a test electric field under each relative humidity;
for each relative humidity, sending the test voltage into a constructed neural network for training;
optimizing the trained neural network based on the actual electric field strength and the actual voltage of the plurality of test points for each relative humidity;
acquiring measured relative humidity and measured voltage of a target point in an electric field to be measured, and sending the measured relative humidity and the measured voltage into the optimized neural network;
and outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network.
In an embodiment of the disclosure, the step of optimizing the trained neural network based on the actual electric field intensities and the actual voltages of the plurality of test points for each relative humidity includes:
for each relative humidity, performing nonlinear compensation on the test voltage according to the actual voltage of each test point, and obtaining the relation between the test voltage and the actual voltage;
and obtaining the relation between the test voltage and the actual electric field intensity based on the relation between the test voltage and the actual voltage.
In an embodiment of the disclosure, the relationship between the test voltage and the actual voltage is:
wherein u is Real world For the actual voltage u o For the test voltage, S is a constant and K is a magnification.
In an embodiment of the disclosure, the step of outputting, by the optimized neural network, the measured electric field strength of the target point in the electric field to be measured includes:
the optimized neural network obtains the relation between the theoretical voltage in the electric field to be measured and the theoretical electric field strength under the measured relative humidity according to the measured relative humidity and the measured voltage of the target point;
substituting the measured voltage into the relation between the theoretical voltage and the theoretical electric field intensity to obtain the measured electric field intensity of the target point in the electric field to be measured.
In an embodiment of the disclosure, the method further comprises:
if the measured relative humidity is the same as any one of the relative humidities, the relationship between the test voltage and the actual electric field strength is the relationship between the measured voltage and the measured electric field strength in the electric field to be measured under the relative humidity which is the same as the measured relative humidity;
substituting the measured voltage to obtain the measured electric field intensity of the target point.
In an embodiment of the disclosure, the method further comprises:
and if the measured relative humidity is the same as any one of the relative humidities, and the test voltage is the same as the theoretical voltage under the relative humidity which is the same as the measured relative humidity, the actual electric field intensity corresponding to the test voltage is the measured electric field intensity of the target point.
In an embodiment of the disclosure, the relationship between the test voltage and the actual electric field strength is:
wherein u is o For the induced voltage of the sensor (i.e. the test voltage), E O The actual electric field intensity of the test point is C, the capacitance inscribed in the sensor is R, the radius of the sensor pellet is R, epsilon is the medium dielectric constant in the air, and pi is the circumference ratio.
According to a second aspect of embodiments of the present disclosure, there is provided a measurement device of electric field strength, the device comprising:
the first information acquisition module is used for acquiring test voltages corresponding to a plurality of test points in the test electric field under each relative humidity when the relative humidity is a plurality of relative humidities;
the neural network training module is used for sending the test voltage into the constructed neural network for training aiming at each relative humidity;
the optimization module is used for optimizing the trained neural network based on the actual electric field intensity and the actual voltage of the plurality of test points aiming at each relative humidity;
the second information acquisition module is used for acquiring the measured relative humidity and the measured voltage of a target point in the electric field to be measured and sending the measured relative humidity and the measured voltage into the optimized neural network;
and the output module is used for outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network.
In an embodiment of the disclosure, the optimization module includes:
the compensation unit is used for carrying out nonlinear compensation on the test voltage according to the actual voltage of each test point aiming at each relative humidity, and obtaining the relation between the test voltage and the actual voltage;
the first calculation unit is used for obtaining the relation between the test voltage and the actual electric field intensity based on the relation between the test voltage and the actual voltage.
In an embodiment of the disclosure, the output module includes:
the second calculation unit is used for obtaining the relation between the theoretical voltage in the electric field to be measured and the theoretical electric field strength under the measured relative humidity according to the measured relative humidity and the measured voltage of the target point after optimization;
and the output unit is used for substituting the measured voltage into the relation between the theoretical voltage and the theoretical electric field intensity to obtain the measured electric field intensity of the target point in the electric field to be measured.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the method and the device for measuring the electric field intensity, on one hand, the test voltage is trained and optimized under different environmental humidities through the neural network, and then the optimized neural network is utilized to calculate the measured electric field intensity of the target point when the measured relative humidity and the measured voltage of the target point are known. On the other hand, aiming at the environment with high humidity, the electric field intensity detector can measure accurate field intensity measurement, and the field intensity measurement precision under the environment conditions with different relative humidity is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a step diagram of a method of measuring electric field strength in an exemplary embodiment of the present disclosure;
fig. 2 shows a schematic diagram of a measuring apparatus of electric field strength in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of embodiments of the disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In the present exemplary embodiment, a method for measuring electric field strength is provided first. Referring to fig. 1, the method of measuring electric field strength may include: step S101 to step S105.
Wherein, step S101: and when a plurality of relative humidities are acquired, testing voltages of a plurality of test points in the electric field are tested under each relative humidity.
Step S102: for each of the relative humidities, the test voltages are fed into a constructed neural network for training.
Step S103: for each relative humidity, optimizing the neural network based on the actual electric field strength and the actual voltage of the plurality of test points.
Step S104: and acquiring the measured relative humidity and the measured voltage of a target point in the electric field to be measured, and sending the measured relative humidity and the measured voltage into the optimized neural network.
Step S105: and outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network.
According to the electric field intensity measuring method, on one hand, the neural network is trained through the test voltage, the actual electric field intensity and the actual voltage are optimized, the neural network after training is utilized, and when the measured relative humidity and the measured voltage of the target point are known, the measured electric field intensity of the target point is calculated. On the other hand, aiming at the environment with high humidity, the electric field intensity detector can measure accurate field intensity measurement, and the field intensity measurement precision under the environment conditions with different relative humidity is improved.
Next, each step of the above-described electric field intensity measuring method in the present exemplary embodiment will be described in more detail with reference to fig. 1 to 2.
In step S101, the relative humidity is h 1 When the electric field intensity is in full range, n test points e are obtained 1 ,e 2 ,e 3 ,...,e n Voltage output value (i.e. test voltage) U of sensor of each test point 11 ，U 12 , ... U 1n . Changing the relative humidity to h 2 Obtaining the relative humidity of h 2 When the voltage output value U of the sensor of each test point 21 ，U 22 , ... U 2n . Changing the relative humidity value until the relative humidity h is obtained m When the voltage output value U of the sensor of each test point m1 ，U m2 , ... U mn . Wherein h is 1 ,h 2 ,h 3 ,...,h m The m relative positionsHumidity may cover the entire range of relative humidity.
In steps S102 and S103, the relation of the test voltage to the actual electric field strength is different at different relative humidities. Therefore, the test voltage needs to be compensated, and the compensation amount of the test voltage is different when the relative humidity is different; and the compensation amount for each test voltage is different at the same relative humidity.
After the test voltage is compensated, the relation between the test voltage and the actual voltage can be obtained; and combining the relation between the actual voltage and the actual electric field intensity to obtain the relation between the test voltage and the actual electric field intensity.
The relation calculation step of the test voltage and the actual voltage comprises the following steps:
for amplifying the test voltage by using an amplifier, the expression of the relation between the input and the output of the compensation amplifier is as follows:
wherein u is o For the test voltage, K is the amplification,
is the output voltage of the amplifier.
Wherein u is f And x is the compensation amount for the actual voltage.
Test voltage u o The relation with the compensation quantity x is as follows:
the relationship between the test voltage and the actual voltage can be obtained:
wherein u is Real world For the actual voltage u o For the test voltage, S is a constant and K is a magnification.
In addition, the relation between the actual electric field strength and the test voltage is:
wherein u is o For the induced voltage of the sensor (i.e. the test voltage), E O The actual electric field intensity of the test point is C, the capacitance inscribed in the sensor is R, the radius of the sensor pellet is R, epsilon is the medium dielectric constant in the air, and pi is the circumference ratio.
And (3) obtaining the relation between the actual voltage and the measured electric field intensity of the test point according to the formula (1) and the formula (2).
In steps S104 and S105, there are two ways in which the measured electric field intensity of the target point can be calculated.
Method 1: after the relative humidity in the environment of the target point and the measured voltage of the target point are measured, inputting the relative humidity and the measured voltage of the sensor into the optimized neural network; calculating the compensation quantity of the test voltage under the relative humidity by utilizing a neural network established in advance, and compensating to obtain the relation between the theoretical voltage in the electric field to be tested and the theoretical electric field intensity; and finally, carrying the measured voltage into the relation between the theoretical voltage and the theoretical electric field intensity to obtain the measured electric field intensity of the target point.
Method 2: h is respectively according to relative humidity 1 ,h 2 ,h 3 ,...,h m And when the test voltage of each test point is related to the measured electric field intensity, fitting m curves by using a curve fitting method. In the actual measurement, after the relative humidity and the measured voltage of the target point in the environment are measured, the relative humidity and the measured voltage are input into the optimized neural network, and the neural network compares the relative humidity to make the relative humidity at h j And h j+1 Intermediate, i.e. h j <h<h j+1 The method comprises the steps of carrying out a first treatment on the surface of the According to h j And h j+1 By using a neural network established in advance, the measured voltages are respectively substituted into h j And h j+1 In the relationship of (2), the humidity is h j And h j+1 When the electric field intensity is correspondingly measured
And->
Is a value of (2). Then, the magnitude of the measured electric field intensity of the target point is calculated according to the relative humidity value of the target point:
wherein h is the measured relative humidity, e is the measured electric field intensity of the target point when the relative humidity is h,
for a relative humidity of h j Measurement electric field strength of target point at measurement voltage U, ">
For a relative humidity of h j+1 And measuring the electric field intensity of the target point when the measurement voltage is U.
In one embodiment, if the measured relative humidity is the same as any one of the relative humidities, the relationship between the test voltage and the actual electric field strength is the relationship between the measured voltage and the measured electric field strength in the electric field to be measured at the same relative humidity as the measured relative humidity; substituting the measured voltage to obtain the measured electric field intensity of the target point. Specifically, when the measured relative humidity obtained by measurement is equal to the relative humidity h of the ith test point i In this case, h can be directly used i And outputting the corresponding relation expression to obtain the measured electric field intensity of the target point.
In one implementationIn an example, if the measured relative humidity is the same as any one of the relative humidities, and the test voltage is the same as the theoretical voltage at the same relative humidity as the measured relative humidity, the actual electric field intensity corresponding to the test voltage is the measured electric field intensity of the target point. Specifically, when the measured relative humidity and the measured voltage are equal to the relative humidity h of the ith test point i And when the voltage is tested, the measured electric field intensity of the target point can be directly obtained.
According to a second aspect of embodiments of the present disclosure, there is provided a measurement device 100 of electric field strength, the device comprising: a first information acquisition module 110, a neural network training module 120, an optimization module 130, a second information acquisition module 140, and an output module 150. The first information obtaining module 110 is configured to obtain, when a plurality of relative humidities are obtained, test voltages corresponding to a plurality of test points in the test electric field under each of the relative humidities; a neural network training module 120, configured to send the test voltage to the constructed neural network for training for each relative humidity; an optimizing module 130, configured to optimize the trained neural network based on the actual electric field intensities and the actual voltages of the plurality of test points for each relative humidity; the second information obtaining module 140 is configured to obtain a measured relative humidity and a measured voltage of a target point in the electric field to be measured, and send the measured relative humidity and the measured voltage to the optimized neural network; and the output module 150 is configured to output, by using the optimized neural network, the measured electric field strength of the target point in the electric field to be measured.
In one embodiment, the optimization module 130 further includes a compensation unit and a first calculation unit. Specifically, the compensation unit is used for carrying out nonlinear compensation on the test voltage according to the actual voltage of each test point aiming at each relative humidity, and obtaining the relation between the test voltage and the actual voltage; the first calculation unit is used for obtaining the relation between the test voltage and the actual electric field intensity based on the relation between the test voltage and the actual voltage.
In one embodiment, the output module 150 further includes a second computing unit and an output unit. Specifically, the neural network optimized by the second computing unit obtains the relation between the theoretical voltage in the electric field to be measured and the theoretical electric field strength under the measurement of the relative humidity according to the measurement relative humidity and the measurement voltage of the target point; the output unit is used for obtaining the measured electric field intensity of the target point in the electric field to be measured according to the measured voltage.
According to the method and the device for measuring the electric field intensity, on one hand, the neural network is used for training and optimizing the test voltage under different environmental humidity, and the optimized neural network is used for calculating the measured electric field intensity of the target point when the measured relative humidity and the measured voltage of the target point are known. On the other hand, aiming at the environment with high humidity, the electric field intensity detector can measure accurate field intensity measurement, and the field intensity measurement precision under the environment conditions with different relative humidity is improved.
It is to be understood that the terms "center," "longitudinal," "transverse," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," and the like in the above description are directional or positional relationships as indicated based on the drawings, merely to facilitate description of the embodiments of the present disclosure and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be configured and operated in a particular orientation, and thus are not to be construed as limiting the embodiments of the present disclosure.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present disclosure, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the presently disclosed embodiments, the terms "mounted," "connected," "secured," and the like are to be construed broadly, as well as being either fixedly connected, detachably connected, or integrally formed, unless otherwise specifically indicated and defined; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the terms in this disclosure will be understood by those of ordinary skill in the art as the case may be.
In the presently disclosed embodiments, unless expressly stated and limited otherwise, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features not being in direct contact but being in contact through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, one skilled in the art can combine and combine the different embodiments or examples described in this specification.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method for measuring electric field strength, the method comprising:
when a plurality of relative humidities are obtained, testing voltages of a plurality of test points in a test electric field under each relative humidity;
for each relative humidity, sending the test voltage into a constructed neural network for training;
optimizing the trained neural network based on the actual electric field strength and the actual voltage of the plurality of test points for each relative humidity;
acquiring measured relative humidity and measured voltage of a target point in an electric field to be measured, and sending the measured relative humidity and the measured voltage into the optimized neural network;
and outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network.
2. The method of measuring electric field strength according to claim 1, wherein the step of optimizing the neural network after training based on the actual electric field strength and the actual voltage of the plurality of test points for each of the relative humidities comprises:
for each relative humidity, performing nonlinear compensation on the test voltage according to the actual voltage of each test point, and obtaining the relation between the test voltage and the actual voltage;
and obtaining the relation between the test voltage and the actual electric field intensity based on the relation between the test voltage and the actual voltage.
3. The method of measuring electric field strength according to claim 2, wherein the relation between the test voltage and the actual voltage is:
wherein u is Real world For the actual voltage u o For the test voltage, S is a constant and K is a magnification.
4. The method according to claim 3, wherein the step of outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network comprises:
the optimized neural network obtains the relation between the theoretical voltage in the electric field to be measured and the theoretical electric field strength under the measured relative humidity according to the measured relative humidity and the measured voltage of the target point;
substituting the measured voltage into the relation between the theoretical voltage and the theoretical electric field intensity to obtain the measured electric field intensity of the target point in the electric field to be measured.
5. The method for measuring electric field strength according to claim 4, further comprising:
if the measured relative humidity is the same as any one of the relative humidities, the relationship between the test voltage and the actual electric field strength is the relationship between the measured voltage and the measured electric field strength in the electric field to be measured under the relative humidity which is the same as the measured relative humidity;
substituting the measured voltage to obtain the measured electric field intensity of the target point.
6. The method for measuring electric field strength according to claim 4, further comprising:
and if the measured relative humidity is the same as any one of the relative humidities, and the test voltage is the same as the theoretical voltage under the relative humidity which is the same as the measured relative humidity, the actual electric field intensity corresponding to the test voltage is the measured electric field intensity of the target point.
7. The method of claim 4, wherein the relationship between the test voltage and the actual electric field strength is:
wherein u is o For the induced voltage of the sensor (i.e. the test voltage), E O The actual electric field intensity of the test point is C, the capacitance inscribed in the sensor is R, the radius of the sensor pellet is R, epsilon is the medium dielectric constant in the air, and pi is the circumference ratio.
8. An apparatus for measuring the strength of an electric field, the apparatus comprising:
the first information acquisition module is used for acquiring test voltages corresponding to a plurality of test points in the test electric field under each relative humidity when the relative humidity is a plurality of relative humidities;
the neural network training module is used for sending the test voltage into the constructed neural network for training aiming at each relative humidity;
the optimization module is used for optimizing the trained neural network based on the actual electric field intensity and the actual voltage of the plurality of test points aiming at each relative humidity;
the second information acquisition module is used for acquiring the measured relative humidity and the measured voltage of a target point in the electric field to be measured and sending the measured relative humidity and the measured voltage into the optimized neural network;
and the output module is used for outputting the measured electric field intensity of the target point in the electric field to be measured by the optimized neural network.
9. The apparatus for measuring electric field strength according to claim 8, wherein the optimizing module comprises:
the compensation unit is used for carrying out nonlinear compensation on the test voltage according to the actual voltage of each test point aiming at each relative humidity, and obtaining the relation between the test voltage and the actual voltage;
the first calculation unit is used for obtaining the relation between the test voltage and the actual electric field intensity based on the relation between the test voltage and the actual voltage.
10. The apparatus according to claim 8, wherein the output module includes:
the second calculation unit is used for obtaining the relation between the theoretical voltage in the electric field to be measured and the theoretical electric field strength under the measured relative humidity according to the measured relative humidity and the measured voltage of the target point after optimization;
and the output unit is used for substituting the measured voltage into the relation between the theoretical voltage and the theoretical electric field intensity to obtain the measured electric field intensity of the target point in the electric field to be measured.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310559626.5A CN116298550A (en) | 2023-05-18 | 2023-05-18 | Method and device for measuring electric field strength |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310559626.5A CN116298550A (en) | 2023-05-18 | 2023-05-18 | Method and device for measuring electric field strength |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116298550A true CN116298550A (en) | 2023-06-23 |
Family
ID=86781902
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310559626.5A Pending CN116298550A (en) | 2023-05-18 | 2023-05-18 | Method and device for measuring electric field strength |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116298550A (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112881818A (en) * | 2021-01-15 | 2021-06-01 | 广州穗能通能源科技有限责任公司 | Electric field intensity measuring method, electric field intensity measuring device, computer equipment and storage medium |
-
2023
- 2023-05-18 CN CN202310559626.5A patent/CN116298550A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112881818A (en) * | 2021-01-15 | 2021-06-01 | 广州穗能通能源科技有限责任公司 | Electric field intensity measuring method, electric field intensity measuring device, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8036841B2 (en) | Measuring method and apparatus for potentiometric measuring probes | |
| CN105806371B (en) | The temperature and pressure dynamic compensation method of sensor | |
| JP2005518527A (en) | Methods and apparatus for assays for electrochemical properties | |
| CN106093575A (en) | A kind of method measuring conductor resistivity at room temperature and temperature-coefficient of electrical resistance under alternating temperature | |
| CN108037309B (en) | Differential type plane capacitance sensor array measuring method for particle velocity distribution | |
| CN100533134C (en) | Method and apparatus for testing water ratio of corn | |
| CN104142431B (en) | Eddy current conductivity measuring sensor | |
| EP0068101A2 (en) | Electrochemical analytical apparatus | |
| Struckmann et al. | A combined in situ monitoring approach for half cell state of charge and state of health of vanadium redox flow batteries | |
| CN105911106A (en) | Insulating gas state detection system for GIS (gas insulated switchgear) equipment | |
| CN107728097A (en) | A kind of rotary transformer verifying attachment and its method for inspection | |
| CN105806915A (en) | Device and method for detecting concentration of potassium and sodium ions in nutrient solution | |
| CN109163769B (en) | Detection method of pipeline flow electromagnetic array sensor | |
| CN116298550A (en) | Method and device for measuring electric field strength | |
| CN103460057B (en) | Contactlessly determine method and the equipment of electromotive force | |
| CN112946343B (en) | Overhead line power frequency voltage measuring device and measuring method | |
| CN111912469B (en) | LZP flowmeter and flow calculation method | |
| KR100491019B1 (en) | Multipoint thickness measurement system | |
| JP2002116220A (en) | Strain-type anemometer for selection of construction location point of windmill for wind turbine generator | |
| CN111123406A (en) | Handheld meteorological instrument temperature data fitting method | |
| Asakai et al. | High-throughput and precise measurement method for electrolytic conductivity in a higher conductivity range (10 S m− 1 to 0.1 S m− 1) by chromatography system with conductivity detector | |
| CN102095792B (en) | Online gas detection device based on carbon-nano-tube membrane micron-nano electrode | |
| CN204028228U (en) | Eddy conductivity survey sensor | |
| Louis et al. | Integrated Electrical System Based AD5933 Impedance analyzer: Towards Multi-Selective Detection of Complex Gas Mixtures | |
| CN110988089B (en) | Wide-range digital electrochemical analysis device and detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230623 |
|
| RJ01 | Rejection of invention patent application after publication |