CN118246757A - Charging pile conversion efficiency uncertainty evaluation method, system, medium and equipment - Google Patents
Charging pile conversion efficiency uncertainty evaluation method, system, medium and equipment Download PDFInfo
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Abstract
The invention provides a charging pile conversion efficiency uncertainty evaluation method, a charging pile conversion efficiency uncertainty evaluation system, a charging pile conversion efficiency uncertainty evaluation medium and charging pile conversion efficiency uncertainty evaluation equipment, and relates to the field of charging piles. The charging pile conversion efficiency uncertainty evaluation method comprises the steps of obtaining input power and output power of a detected charging pile at different power points, and obtaining an output power-efficiency scatter diagram of a full power range of the detected charging pile; processing an output power-efficiency scatter diagram of a full power range by a curve fitting method to obtain a efficacy curve of the detected charging pile; obtaining meter output power data when the charging pile actually operates; and determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates. The problem that the evaluation of the conversion efficiency of the charging pile based on model driving is difficult to realize is solved by considering that the available data in the charging station is limited.
Description
Technical Field
The invention relates to the technical field of charging piles, in particular to a charging pile conversion efficiency uncertainty evaluation method, a charging pile conversion efficiency uncertainty evaluation system, a charging pile conversion efficiency uncertainty evaluation medium and charging pile conversion efficiency uncertainty evaluation equipment.
Background
Along with the increase of the construction scale of the charging piles, verification work faces the practical problems of huge quantity of the charging piles and large detection working pressure, and the detection operation and maintenance cost of the charging piles is increased along with the increase of the construction scale of the charging piles, so that a calibration method for referring to the electric energy meter of the low-voltage transformer area is needed, and the metering error of the charging piles in the charging station is detected by an equation solution based on energy conservation. Compared with a low-voltage station area, the charging station area needs to consider the problem of the conversion efficiency of the charging pile and the uncertainty thereof in the process of solving the equation by using energy conservation.
Considering that the available data in the charging station is limited, the evaluation of the conversion efficiency of the charging pile based on model driving is difficult to realize, and therefore, a method for evaluating the conversion efficiency of the charging pile and the uncertainty thereof by using the output power of the charging pile is needed to be provided.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a charging pile conversion efficiency uncertainty evaluation method, a charging pile conversion efficiency uncertainty evaluation system, a charging pile conversion efficiency evaluation medium and charging pile conversion efficiency evaluation equipment, which solve the problem that the charging pile conversion efficiency evaluation based on model driving is difficult to realize in consideration of limited data available in a charging station.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a charging pile conversion efficiency uncertainty evaluation method comprises the following steps:
Acquiring input power and output power of the detected charging pile at different power points, and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
Processing an output power-efficiency scatter diagram of a full power range by a curve fitting method to obtain a efficacy curve of the detected charging pile;
obtaining meter output power data when the charging pile actually operates;
Determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
determining the test times of the Monte Carlo method, wherein M is a preset value, for example, the range of the value is 10 5-106, and M sample values P i, i=1, 2, … and M are selected from probability density distribution according to power;
The M sample values P i are brought into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M, and the efficiency values are arranged in a non-descending order to obtain a discrete representation G of the conversion efficiency probability density;
calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
and calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency.
Preferably, the efficacy curve of the detected charging pile is obtained by processing the output power-efficiency scatter diagram of the full power range by a curve fitting method, which specifically comprises the following steps:
The fitting method adopts rational component fitting, and the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
wherein the fitting parameter c 0,c1,c2 is obtained by a least square method; by detecting m data points (P i,ηi), i=1, 2, …, m, the above data can be brought into the fit equation:
The following substitutions were made:
fitting parameters were calculated as follows:
C＝(ATA)-1ATT。
Preferably, the determining the probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates specifically includes:
The charging pile output power data in the charging station is obtained through a metering unit in the charging pile, and the error sources are metering error P 1 and numerical value reduction error P 2 of the metering unit:
the metering errors are considered according to the maximum allowable error, and the metering errors are uniformly distributed in the maximum allowable error:
P1～U[-A％×P0,+A％×P0]
wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter;
the charging pile with the accuracy grade of A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
obtaining probability density distribution of output power P by using Monte Carlo method:
P＝P0+P1+P2。
Preferably, the determination of the number of trials of the monte carlo method is used to ensure that the output result has a 95% inclusion probability.
Preferably, the estimated value of the conversion efficiency of the charging pile is calculated as follows:
Wherein, And y r is the efficiency value obtained by the r-th test, which is the average value of the efficiency of the M tests.
Preferably, the standard uncertainty of the conversion efficiency of the charging pile is calculated as follows:
Preferably, the method further comprises calculating the inclusion interval and the expansion uncertainty when the inclusion probability is 95% according to the discrete representation G of the conversion efficiency probability density, and specifically comprises:
Calculating from G the inclusion interval [ y low,yhigh ] when the inclusion probability is 95%, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q; wherein p represents an inclusion probability;
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2。
In a second aspect, a charging pile conversion efficiency uncertainty evaluation system is provided, including the following modules:
The first acquisition module is used for acquiring the input power and the output power of the detected charging pile at different power points and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
The preprocessing module is used for processing the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile;
the second acquisition module is used for acquiring meter output power data when the charging pile actually operates;
The operation module is used for determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
The first processing module is configured to determine the number of tests of the monte carlo method, where M is a preset value, for example, the range of value is 10 5-106, and M sample values P i, i=1, 2, …, M are selected from probability density distribution according to power;
The second processing module is used for bringing the M sample values P i into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M;
The third processing module is used for arranging the efficiency values in a non-descending order to obtain a discrete representation G of the conversion efficiency probability density;
the first calculation module is used for calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
The second calculation module is used for calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency;
And an output module for calculating the inclusion interval and the expansion uncertainty when the inclusion probability is 95% according to the discrete representation G of the conversion efficiency probability density.
Preferably, the preprocessing module processes the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile, and specifically includes:
the fitting technology adopts rational component fitting, and the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
wherein the fitting parameter c 0,c1,c2 is obtained by a least square method; by detecting m data points (P i,ηi), i=1, 2, …, m, the above data can be brought into the fit equation:
The following substitutions were made:
fitting parameters were calculated as follows:
C＝(ATA)-1ATT。
preferably, the operation module determines probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates, and specifically includes:
The charging pile output power data in the charging station is obtained through a metering unit in the charging pile, and the error sources are metering error P 1 and numerical value reduction error P 2 of the metering unit:
the metering errors are considered according to the maximum allowable error, and the metering errors are uniformly distributed in the maximum allowable error:
P1～U[-A％×P0,+A％×P0]
wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter;
the charging pile with the accuracy grade of A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
obtaining probability density distribution of output power P by using Monte Carlo method:
P＝P0+P1+P2。
Preferably, the determination of the number of trials of the monte carlo method is used to ensure that the output result has a 95% inclusion probability.
Preferably, the estimated value of the conversion efficiency of the charging pile is calculated as follows:
Wherein, Y r is the efficiency value obtained in the r-th test, r=1, 2, …, M-q, which is the average of the efficiencies of the M tests.
Preferably, the standard uncertainty of the conversion efficiency of the charging pile is calculated according to the following formula:
Preferably, the method further comprises an output module, which is used for calculating the inclusion interval and the expansion uncertainty when the inclusion probability is 95% according to the discrete representation G of the conversion efficiency probability density, and specifically comprises the following steps:
Calculating from G an inclusion interval [ y low,yhigh ] when the inclusion probability is 95%, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q, where p represents the inclusion probability;
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2。
in a third aspect, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
In a fourth aspect, there is provided a computing device comprising:
One or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
(III) beneficial effects
According to the charging pile conversion efficiency uncertainty evaluation method, the power efficiency curve of the detected charging pile is obtained by processing the output power-efficiency scatter diagram of the full power range through the curve fitting technology, probability density distribution of the output power of the charging pile is determined according to meter output power data when the charging pile actually operates, the test times M of the Monte Carlo method are determined, M sample values P i are selected from the probability density distribution according to the output power, the M sample values P i are brought into the power efficiency curve of the detected charging pile to obtain corresponding efficiency values, the efficiency values are arranged according to a non-descending order to obtain discrete representation G of the conversion efficiency probability density, and therefore the problem that the conversion efficiency evaluation of the charging pile based on model driving is difficult to achieve is effectively solved due to the fact that available data in a charging station are limited.
Drawings
Fig. 1 is a schematic flow chart of a method for evaluating the conversion efficiency uncertainty of a charging pile according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, there is provided a charging pile conversion efficiency uncertainty evaluation method, including:
Acquiring input power and output power of the detected charging pile at different power points, and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
Processing an output power-efficiency scatter diagram of a full power range by a curve fitting method to obtain a efficacy curve of the detected charging pile;
obtaining meter output power data when the charging pile actually operates;
Determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
determining the test times of the Monte Carlo method, wherein M is a preset value, for example, the range of the value is 10 5-106, and M sample values P i, i=1, 2, … and M are selected from probability density distribution according to power;
Bringing the M sample values P i into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M;
the efficiency values are arranged in a non-decreasing order to obtain a discrete representation G of the probability density of the conversion efficiency;
calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency;
The inclusion interval and the expansion uncertainty at an inclusion probability of 95% are calculated from the discrete representation G of the conversion efficiency probability density.
Specifically, as shown in fig. 1, the method includes steps 1 to 10, as follows:
And step 1, respectively detecting the input power and the output power of the charging pile at different power points by using an electric energy meter with higher precision level (such as 0.5 level), and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile.
Step 2, obtaining the efficacy curve of the charging pile and a mathematical model thereof by using a rational fitting method, wherein the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
Wherein the fitting parameter c 0,c1,c2 is obtained by a least square method. Let m (P i,ηi) data points obtained by detection, i=1, 2, …, m. The data can be brought into a fitting equation to obtain:
The following substitutions were made:
The fitting parameters were calculated as follows:
C＝(ATA)-1ATT
And step 3, substituting the fitting parameters obtained by the calculation in the step 2 into an initial fitting equation to obtain a mathematical model of the power-efficiency of the detected charging pile, namely an efficacy curve.
And 4, obtaining meter output power data when the charging pile actually operates, and calculating the efficiency and uncertainty of the charging pile based on the meter output power data.
And 5, determining probability density distribution of output power of the charging pile. The output power data of the charging pile in the charging station is obtained through the metering unit in the charging pile, so that the sources of errors are metering errors P 1 and numerical reduction errors P 2 of the metering unit.
Considering the reliability of the final assessment result, the metering errors are considered as maximum allowable errors, and the metering errors are uniformly distributed within the maximum allowable errors:
P1～U[-A％×P0,+A％×P0]
Wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter.
According to the national standard, the charging pile with the accuracy grade A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
therefore, the probability density distribution of the output power P can be obtained by using the monte carlo method according to the following formula:
P＝P0+P1+P2
And 6, determining the test times of the Monte Carlo method, and taking the test times M=10 6 to ensure that the output result has 95% of inclusion probability.
Step 7, taking m=10 6 sample values P i, i=1, 2, …, M from the probability density distribution of the output power P.
Step 8, the M sample values P i (i=1, 2, …, M) are put into the mathematical model of the efficacy curve to obtain the corresponding efficiency values η i, i=1, 2, …, M. The efficiency values are arranged in a non-decreasing order to obtain a discrete representation G of the probability density of the conversion efficiency.
Step 9, calculating an estimated value of the conversion efficiency of the charging pile according to the following formula:
The standard uncertainty of the conversion efficiency of the charging pile is calculated according to the following formula:
Step 10, calculating the inclusion interval [ y low,yhigh ] when the inclusion probability is 95% from G, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q. p represents the inclusion probability.
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2
Through the steps, the estimated value and uncertainty of the conversion efficiency of the charging pile can be obtained by utilizing the output power of the charging pile during operation.
In another embodiment of the present invention, a charging pile conversion efficiency uncertainty evaluation system is provided, including the following modules:
The first acquisition module is used for acquiring the input power and the output power of the detected charging pile at different power points and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
The preprocessing module is used for processing the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile;
the second acquisition module is used for acquiring meter output power data when the charging pile actually operates;
The operation module is used for determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
The first processing module is configured to determine the number of tests of the monte carlo method, where M is a preset value, for example, the range of value is 10 5-106, and M sample values P i, i=1, 2, …, M are selected from probability density distribution according to power;
The second processing module is used for bringing the M sample values P i into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M;
The third processing module is used for arranging the efficiency values in a non-descending order to obtain a discrete representation G of the conversion efficiency probability density;
the first calculation module is used for calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
The second calculation module is used for calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency;
And an output module for calculating the inclusion interval and the expansion uncertainty when the inclusion probability is 95% according to the discrete representation G of the conversion efficiency probability density.
Further, the preprocessing module processes the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile, and specifically comprises the following steps:
the fitting technology adopts rational component fitting, and the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
wherein the fitting parameter c 0,c1,c2 is obtained by a least square method; by detecting m data points (P i,ηi), i=1, 2, …, m, the above data can be brought into the fit equation:
The following substitutions were made:
fitting parameters were calculated as follows:
C＝(ATA)-1ATT。
further, the operation module determines probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates, and specifically includes:
The charging pile output power data in the charging station is obtained through a metering unit in the charging pile, and the error sources are metering error P 1 and numerical value reduction error P 2 of the metering unit:
the metering errors are considered according to the maximum allowable error, and the metering errors are uniformly distributed in the maximum allowable error:
P1～U[-A％×P0,+A％×P0]
wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter;
the charging pile with the accuracy grade of A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
obtaining probability density distribution of output power P by using Monte Carlo method:
P＝P0+P1+P2。
Further, the determination of the number of tests of the Monte Carlo method is used for ensuring that the output result has a 95% inclusion probability.
Further, the estimated value of the conversion efficiency of the charging pile is calculated as follows:
Wherein, Y r is the efficiency value obtained in the r-th test, r=1, 2, …, M-q, which is the average of the efficiencies of the M tests.
Preferably, the standard uncertainty of the conversion efficiency of the charging pile is calculated according to the following formula:
Further, the method further comprises an output module, which is used for calculating the inclusion interval and the expansion uncertainty when the inclusion probability is 95% according to the discrete representation G of the conversion efficiency probability density, and specifically comprises the following steps:
Calculating from G an inclusion interval [ y low,yhigh ] when the inclusion probability is 95%, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q, where p represents the inclusion probability;
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2。
Yet another embodiment of the invention provides a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
Yet another embodiment of the present invention provides a computing device comprising:
One or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
Embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (16)
1. A charging pile conversion efficiency uncertainty evaluation method, characterized by comprising:
Acquiring input power and output power of the detected charging pile at different power points, and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
Processing an output power-efficiency scatter diagram in a full power range by a curve fitting technology to obtain a efficacy curve of the detected charging pile;
obtaining meter output power data when the charging pile actually operates;
Determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
Determining the test times M of the Monte Carlo method, wherein M is a preset value, and selecting M sample values P i, i=1, 2, … and M from probability density distribution according to output power;
The M sample values P i are brought into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M, and the efficiency values are arranged in a non-descending order to obtain a discrete representation G of the conversion efficiency probability density;
calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
and calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency.
2. The charging pile conversion efficiency uncertainty evaluation method according to claim 1, characterized in that: processing an output power-efficiency scatter diagram of a full power range by a curve fitting method to obtain an efficacy curve of the detected charging pile, wherein the method specifically comprises the following steps:
the fitting technology adopts rational component fitting, and the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
wherein the fitting parameter c 0,c1,c2 is obtained by a least square method; by detecting m data points (P i,ηi), i=1, 2, …, m, the above data can be brought into the fit equation:
The following substitutions were made:
fitting parameters were calculated as follows:
C＝(ATA)-1ATT。
3. The charging pile conversion efficiency uncertainty evaluation method according to claim 2, characterized in that: the probability density distribution of the output power of the charging pile is determined according to the meter output power data when the charging pile actually operates, and the method specifically comprises the following steps:
The charging pile output power data in the charging station is obtained through a metering unit in the charging pile, and the error sources are metering error P 1 and numerical value reduction error P 2 of the metering unit:
the metering errors are considered according to the maximum allowable error, and the metering errors are uniformly distributed in the maximum allowable error:
P1～U[-A％×P0,+A％×P0]
wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter;
the charging pile with the accuracy grade of A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
obtaining probability density distribution of output power P by using Monte Carlo method:
P＝P0+P1+P2。
4. a charging pile conversion efficiency uncertainty evaluation method according to claim 3, characterized in that: the determination of the number of trials of the Monte Carlo method is used to ensure that the output result has a 95% inclusion probability.
5. The charge pile conversion efficiency uncertainty evaluation method according to claim 4, wherein: the estimated value of the conversion efficiency of the charging pile is calculated as follows:
Wherein, Y r is the efficiency value obtained in the r-th test, r=1, 2, …, M-q, which is the average of the efficiencies of the M tests.
6. The charging pile conversion efficiency uncertainty evaluation method according to claim 5, characterized in that: the standard uncertainty of the conversion efficiency of the charging pile is calculated according to the following formula:
7. The method for evaluating the uncertainty of the conversion efficiency of the charging pile according to claim 6, further comprising calculating an inclusion interval and an expansion uncertainty when the inclusion probability is 95% based on the discrete representation G of the probability density of the conversion efficiency, and specifically comprising:
Calculating from G an inclusion interval [ y low,yhigh ] when the inclusion probability is 95%, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q, where p represents the inclusion probability;
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2。
8. The charging pile conversion efficiency uncertainty evaluation system is characterized by comprising the following modules:
The first acquisition module is used for acquiring the input power and the output power of the detected charging pile at different power points and obtaining an output power-efficiency scatter diagram of the full power range of the detected charging pile;
The preprocessing module is used for processing the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile;
the second acquisition module is used for acquiring meter output power data when the charging pile actually operates;
The operation module is used for determining probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates;
The first processing module is used for determining the test times of the Monte Carlo method, wherein M is a preset value, and M sample values P i, i=1, 2, … and M are selected from probability density distribution according to power;
The second processing module is used for bringing the M sample values P i into the efficacy curve of the detected charging pile to obtain corresponding efficiency values eta i, i=1, 2, … and M;
The third processing module is used for arranging the efficiency values in a non-descending order to obtain a discrete representation G of the conversion efficiency probability density;
the first calculation module is used for calculating an estimated value of the conversion efficiency of the detected charging pile according to the discrete representation G of the probability density of the conversion efficiency;
And the second calculation module is used for calculating the standard uncertainty of the conversion efficiency of the detected charging pile according to the estimated value of the conversion efficiency of the detected charging pile and the discrete representation G of the probability density of the conversion efficiency.
9. The charging pile conversion efficiency uncertainty evaluation system according to claim 8, wherein: the preprocessing module processes the output power-efficiency scatter diagram of the full power range by a curve fitting method to obtain the efficacy curve of the detected charging pile, and the preprocessing module specifically comprises the following steps:
the fitting technology adopts rational component fitting, and the fitting form is as follows:
η＝(c0+c1P+c2P2)/P
wherein the fitting parameter c 0,c1,c2 is obtained by a least square method; by detecting m data points (P i,ηi), i=1, 2, …, m, the above data can be brought into the fit equation:
The following substitutions were made:
fitting parameters were calculated as follows:
C＝(ATA)-1ATT。
10. the charging pile conversion efficiency uncertainty evaluation system according to claim 9, wherein: the operation module determines probability density distribution of the output power of the charging pile according to the meter output power data when the charging pile actually operates, and specifically comprises the following steps:
The charging pile output power data in the charging station is obtained through a metering unit in the charging pile, and the error sources are metering error P 1 and numerical value reduction error P 2 of the metering unit:
the metering errors are considered according to the maximum allowable error, and the metering errors are uniformly distributed in the maximum allowable error:
P1～U[-A％×P0,+A％×P0]
wherein A is the accuracy grade of the charging pile, and P 0 is the power value of the charging pile meter;
the charging pile with the accuracy grade of A is repaired at the interval of 0.2% x A, the interval half width is 0.1% x A, and the interval oral administration is uniformly distributed:
P2～U[-0.1％×A×P0,+0.1％×A×P0]
obtaining probability density distribution of output power P by using Monte Carlo method:
P＝P0+P1+P2。
11. The charging pile conversion efficiency uncertainty evaluation system according to claim 10, wherein: the determination of the number of trials of the Monte Carlo method is used to ensure that the output result has a 95% inclusion probability.
12. The charging pile conversion efficiency uncertainty evaluation system according to claim 11, wherein: the estimated value of the conversion efficiency of the charging pile is calculated as follows:
Wherein, Y r is the efficiency value obtained in the r-th test, r=1, 2, …, M-q, which is the average of the efficiencies of the M tests.
13. The charging pile conversion efficiency uncertainty evaluation system according to claim 12, wherein: the standard uncertainty of the conversion efficiency of the charging pile is calculated according to the following formula:
14. The charging pile conversion efficiency uncertainty evaluation system according to claim 13, further comprising an output module for calculating an inclusion interval and an expanded uncertainty with an inclusion probability of 95% from the discrete representation G of the conversion efficiency probability density, specifically comprising:
Calculating from G an inclusion interval [ y low,yhigh ] when the inclusion probability is 95%, assuming q=pm, for any r=1, 2, …, M-q, y low＝yr,yhigh＝yr+q, where p represents the inclusion probability;
The extended uncertainty including a probability of 95% is determined by:
Up＝(yhigh-ylow)/2。
15. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
16. A computing device, comprising:
One or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
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