CN115166619B - Intelligent electric energy meter running error monitoring system - Google Patents
Intelligent electric energy meter running error monitoring system Download PDFInfo
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Abstract
The invention discloses an intelligent electric energy meter operation error monitoring system, which comprises a data acquisition unit: circularly constructing power consumption data of the electric energy meter under the metering point based on the time window; an error calculation unit: generating an equation set according to the electricity data and the electric energy meter operation error mathematical model, and solving the equation set to determine the calculation error of the electric energy meter metering point; a confidence interval calculation unit: analyzing and calculating a confidence interval that the noise of the table region under the current measuring point randomly influences the calculation error based on a ridge regression model; a comparison unit: judging the relation between the calculation error and the confidence interval, and setting the calculation error falling into the confidence interval as the operation error of the electric energy meter; and a marking unit: setting the calculation error exceeding the confidence interval as the real out-of-tolerance, and identifying the measured out-of-tolerance electric energy meter. The intelligent electric energy meter operation error monitoring system provided by the invention can more accurately judge the state of the electric energy meter, lock out an out-of-tolerance household meter to maximally reduce misjudgment and automatically detect whether electricity is stolen or not.
Description
Technical Field
The invention relates to the technical field of electric power metering, in particular to an intelligent electric energy meter operation error monitoring system.
Background
Along with the popularization of the intelligent electric meters, the power grid data are enriched and expanded from the aspects of quantity, timeliness and the like. As the bottommost unit of power distribution management, fine and real-time management of a low-voltage transformer area is imperative. Meanwhile, local monitoring data of the intelligent electric energy meter are further enriched, and data support is provided for online monitoring of the electric energy meter.
In the process of calculating the error of the electric energy meter, the solving coefficient under an ideal condition according to the meaning of a physical equation is the real error of the electric energy meter, but in practical application, due to the existence of various noises, such as the quantization precision of a general meter/household meter, the thermal noise caused by line loss heating, the error/delay of data recording, high line loss load and the like, the solving coefficient can deviate from the real error to a certain extent, so that the abnormal error coefficient can be calculated for some normal electric meters, and further the state of the electric energy meter can be misjudged in the process of on-line monitoring.
Because the existing electric energy meter operation error monitoring system needs to be improved urgently, the design of an intelligent electric energy meter operation error monitoring system is necessary.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made keeping in mind the above problems occurring in the prior art.
Therefore, the technical problem to be solved by the invention is that in practical application, due to the existence of various noises, such as quantization precision of a general meter/household meter, thermal noise of line loss heating, errors/delay of data recording, high line loss load and the like, a certain deviation of a solving coefficient is generated relative to a real error, so that abnormal error coefficients are calculated for some normal electric meters, and the state of the electric energy meter is misjudged in an online monitoring process.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent electric energy meter operation error monitoring system comprises a monitoring device, a monitoring device and a control device, wherein the monitoring device comprises an acquisition master station, a concentrator, a distribution area electric energy meter master meter, a transformer and a resident electric energy meter household meter; and the number of the first and second groups,
the data acquisition unit is used for circularly acquiring the electricity consumption data of the electric energy meter under the metering point based on a time window;
the error calculation unit is used for generating an equation set according to the electricity utilization data and the electric energy meter operation error mathematical model and solving the equation set to determine the calculation error of the metering point of the electric energy meter;
the confidence interval calculation unit is used for analyzing and calculating a confidence interval of random influence of the noise of the table region under the current metering point on the calculation error based on a ridge regression model;
the comparison unit is used for setting the calculation error falling into the confidence interval as the running error of the electric energy meter in order to judge the relation between the calculation error and the confidence interval;
the marking unit is used for setting the calculation error exceeding the confidence interval as a real out-of-tolerance and marking the measured out-of-tolerance electric energy meter;
and the sending unit is used for sending the out-of-tolerance electric energy meter information and the real out-of-tolerance to a station area electric energy meter master meter or an acquisition master station.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the collecting main station is connected with the concentrator through GPRS, the distribution room electric energy meter master meter is arranged on a power supply cable from the transformer to the resident electric energy meter household meters and is installed near the transformer, and the distribution room electric energy meter master meter is connected with the concentrator through a power line carrier.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the electricity utilization data comprises daily freezing data of the electric energy meter, power factors, transformer areas and electric energy meter archive data.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system, the system comprises: the step of analyzing and calculating the confidence interval that the noise of the table region under the current measuring point randomly influences the calculation error based on the ridge regression model comprises the following steps:
estimating the noise of the statistical line loss of the distribution room according to the fitting residual error of the low-voltage line loss and the model freedom degree;
estimating a noise fluctuation variance on a solving coefficient according to the noise size and linear transformation of ridge regression solution;
the confidence interval is determined from a coefficient distribution function, the noise fluctuation variance, and an expected confidence calculation.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the solution coefficient corresponds to the calculation error.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the step of estimating the noise size of the statistical line loss of the distribution room according to the fitting residual error of the low-voltage line loss and the model degree of freedom comprises the following steps:
determining a ridge regression analytic solution formula for the coefficients: θ = (X) T X+R) -1 X T Y; wherein R is a regular matrix in ridge regression;
substituting the analytic solution formula into a data matrix X to obtain a statistical line loss estimation value of the distribution room
The statistical line loss estimation value is obtained
Converting the linear transformation:
H＝X(X T X+R) -1 X T ；
the statistical line loss medium noise distribution N (covariance matrix s) 2 I, s is noise standard deviation, var (N) = s 2 ) Synergy of residual noise after (I-H) conversionThe variance matrix is:
cov((I-H)N)＝(I-H)cov(N)(I-H) T
＝(I-H)s 2 I(I-H) T
＝s 2 (I-HT-H+HH T )
sse (sum squared error) calculated by fitting the residual noise is the trace of the covariance matrix and an estimate of the noise variance, and the noise magnitude is calculated from the noise variance:
sse＝trace(s 2 (I-H T -H+HH T ))
＝s 2 (trace(I)-trace(2H)+trace(HH T ))
＝s 2 (m-trace(2H-HH T ))
wherein trace (2H-HH) T ) The noise degrees of freedom are fitted to the model.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: said step of solving for noise fluctuation variance over said coefficients based on said noise magnitude and linear transformation estimate of ridge regression solution comprises:
let P = (X) T X+R) -1 X T Solving a noise covariance matrix on the coefficients:
cov(N θ )＝cov(PN)
＝Pcov(N)P T
＝s 2 PP T
＝s 2 (X T X+R) -1 X T X(X T X+R) -1
diagonal element of the noise covariance matrix [ cov (N) θ )] ii I.e. the corresponding coefficient theta i Variance of the fluctuation of the noise.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the step of determining the confidence interval from the coefficient distribution function, the noise fluctuation variance and the expected confidence calculation comprises:
according to theta i ～N(0，[cov(N θ )] ii ) Calculating the width b of a confidence interval by the coefficient distribution function and the expected confidence P, wherein P (-b is not more than theta) i ≤b)＝p；
And determining the confidence interval according to the confidence interval width.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the system for monitoring the operation error of the intelligent electric energy meter further comprises the intelligent electric energy meter, wherein the intelligent electric energy meter comprises a memory, a processor and a computer program which is stored in the memory and can be operated on the processor, and the processor realizes the method of any one of claims 1 to 8 when executing the computer program.
As a preferred scheme of the intelligent electric energy meter operation error monitoring system of the invention, wherein: the computer program comprises a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-8.
The invention has the beneficial effects that: the invention provides an electric energy meter operation error mathematical model established according to an energy conservation law, the calculation error is solved by collecting power consumption data and substituting the power consumption data into a calculation model, a confidence interval of a dynamic fluctuation range possibly caused by station noise to the calculation error is analyzed and calculated based on a ridge regression model, the calculation error is secondarily calculated according to the confidence interval, and the error caused by a low-power factor to a calculation result is compensated, so that an intelligent electric energy meter operation error monitoring system of an out-of-tolerance household meter is locked, the state of the electric energy meter is more accurately judged, the misjudgment of the state of the electric energy meter is reduced to the maximum degree, the monitoring accuracy of the electric energy meter is greatly improved, and the electric energy meter is more conveniently monitored and controlled.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a diagram of a master station and station area circuit topology of the present invention;
FIG. 2 is a software block diagram of the electric energy meter operation error monitoring system of the present invention;
FIG. 3 is a block flow diagram of a method executed by the system for monitoring the running error of the electric energy meter according to the present invention;
fig. 4 is a schematic diagram of a hardware structure of the intelligent electric energy meter according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "including" and "having," and any variations thereof, in the description and claims of this application and the drawings described above, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example 1
Referring to fig. 1 to 4, a first embodiment of the present invention provides an operation error monitoring system for an intelligent electric energy meter, which is suitable for an operation error model of a low power factor electric energy meter.
The intelligent electric energy meter operation error monitoring system comprises a monitoring device 100, wherein the monitoring device 100 comprises a collection main station 101, a concentrator 102, a distribution area electric energy meter master table 103, a transformer 104 and a residential electric energy meter user meter 105, the collection main station 101 is communicated with the concentrator 102 through GPRS, the distribution area electric energy meter master table 103 is arranged on a power supply cable from the transformer 104 to the residential electric energy meter user meter 105 and is installed near a public transformer 104, and the distribution area electric energy meter master table 103 is directly communicated with the concentrator 102 through a power line carrier 103 a.
Specifically, as shown in fig. 1, the operation error model is applied to the residential electric energy meter 105 in the form of a software module, and the electric meters used for the residential electric energy meter 105 do not exceed the error range specified by the country, and may also be applied to the station electric energy meter total 103 or the collection master station 101, but the application situations of the station electric energy meter total 103 or the collection master station 101 are relatively few. In the embodiment, an electric energy meter operation error mathematical model is established according to an energy conservation law to output a calculation error, a confidence interval is calculated, the confidence interval represents a dynamic fluctuation range possibly caused by a station noise to the calculation error, secondary operation is performed on the calculation error according to the confidence interval, and an error caused by a low power factor to a calculation result is compensated, so that the state of the electric energy meter is judged more accurately, misjudgment is reduced to the maximum extent, an out-of-tolerance household meter is locked accurately, the error of the electric energy meter is exceeded, reminding information is set in a system to be replaced, specific position data is provided, and the electric energy meter operation error monitoring system can automatically detect whether an electricity stealing condition exists.
The on-line monitoring system for the intelligent electric meter further comprises a data acquisition unit 200, an error calculation unit 300, a confidence interval calculation unit 400, a comparison unit 500, a marking unit 600 and a sending unit 700.
The data acquisition unit 200 is used for circularly acquiring the electricity consumption data of the electric energy meter under the metering point based on a time window;
the error calculation unit 300 is configured to generate an equation set according to the electricity consumption data and the electric energy meter operation error mathematical model, and solve the equation set to determine a calculation error of a metering point of the electric energy meter;
the confidence interval calculation unit 400 is configured to calculate a confidence interval that the noise of the region under the current measurement point randomly affects the calculation error based on the ridge regression model analysis; and
the comparing unit 500 is configured to determine a relationship between the calculation error and the confidence interval, and set the calculation error falling into the confidence interval as an operation error of the electric energy meter;
the marking unit 600 is configured to set the calculation error exceeding the confidence interval as the true out-of-tolerance, and identify the out-of-tolerance electric energy meter measured this time.
The sending unit 700 is configured to send the out-of-tolerance electric energy meter information and the real out-of-tolerance to a distribution room master meter or a master station; the electricity utilization data comprises daily freezing data of the electric energy meter, power factors, transformer areas and electric energy meter archive data.
As shown in fig. 2, in the present embodiment, when monitoring the operation error of the electric energy meter, the following steps need to be performed according to the above units:
s1: and circularly constructing the electricity consumption data of the electric energy meter under the metering point based on the time window.
S2: and generating an equation set according to the electricity data and the electric energy meter operation error mathematical model, and solving the equation set to determine the calculation error of the electric energy meter metering point.
S3: and analyzing and calculating a confidence interval that the noise of the table region under the current measuring point randomly influences the calculation error based on the ridge regression model.
S4: and judging the relation between the calculation error and the confidence interval, and setting the calculation error falling into the confidence interval as the operation error of the electric energy meter.
S5: setting the calculation error exceeding the confidence interval as the real out-of-tolerance, and identifying the measured out-of-tolerance electric energy meter.
And after the steps are completed, the information of the out-of-tolerance electric energy meter and the real out-of-tolerance are sent to a distribution area general meter or a main station.
The power consumption data can comprise daily freezing data of the electric energy meter, power factors, transformer areas and electric energy meter archive data.
For S3: the step of analyzing and calculating the confidence interval that the noise of the platform region under the current measuring point randomly influences the calculation error based on the ridge regression model also comprises the following steps;
s3-1: and estimating the noise of the statistical line loss of the transformer area according to the fitting residual error of the low-voltage line loss and the model degree of freedom.
S3-2: estimating a variance of noise fluctuation on a solution coefficient according to the noise magnitude and a linear transformation estimate of a ridge regression solution, wherein the coefficient corresponds to the calculation error.
S3-3: the confidence interval is determined from a coefficient distribution function, the variance of the noise fluctuation, and an expected confidence calculation.
In S3-1, the process of estimating the noise level of the line loss in the distribution room according to the fitting residual of the low-voltage line loss and the model degree of freedom is as follows:
it should be noted that: the calculation method of the confidence interval in the embodiment is not limited to the scenario of the error estimation of the electric energy meter, but is applicable to a general solution based on ridge regression, so that the symbols used in the embodiment are general mathematical symbols and are not specialized for corresponding physical quantities.
Preferably, X is a data matrix used for solving, represented as a data matrix X, representing a power consumption matrix, possibly including line loss terms and the like, and has a shape of (m, n);
y is the fitting target and also represents the statistical line loss of the plateau region, with a shape of (m, 1).
Determining a ridge regression analytic solution formula for the coefficient: θ = (X) T X+R) -1 X T Y；
Wherein, R is a regular matrix in ridge regression.
The regular matrix is generally in the form of a diagonal: λ I, shape is (n, n).
Substituting the analytic solution formula into a data matrix X to obtain a statistical line loss estimation value of the distribution room
The statistical line loss estimation value is calculated
Converting the linear transformation:
H＝X(X T X+R) -1 X T ；
the statistical line loss noise distribution N (covariance matrix s) 2 I, s is noise standard deviation, var (N) = s 2 ) The covariance matrix of the (I-H) -transformed residual noise is:
cov((I-H)N)＝(I-H)cou(N)(I-H) T
＝(I-H)s 2 I(I-H) T
＝s 2 (I-H T -H+HH T )
it should be noted that sse (sum squared error) calculated by fitting the residual noise is the corresponding statistic, i.e. the sum of diagonal elements of the covariance matrix. I.e. the trace of the covariance matrix and an estimate of the noise variance. Calculating the noise size according to the noise variance:
sse＝trace(s 2 (I-H T -H+HH T ))
＝s 2 (trace(I)-trace(2H)+trace(HH T ))
＝s 2 (m-trace(2H-HH T ))
wherein trace (2H-HH) T ) The noise degrees of freedom are fitted to the model.
This gives the variance s of the noise in the original target 2 Note here that the model fitting noise degree of freedom trace (2H-HH) is added T ) Rather than considering the number of data points alone.
Preferably, in S3-2, the step of solving for the variance of the noise fluctuation on the coefficients based on the noise magnitude and the linear transformation estimate solved for by ridge regression further comprises:
let P = (X) T X+R) -1 X T Solving the noise covariance matrix on the coefficient:
cov(N θ )＝cov(PN)
＝Pcov(N)P T
＝s 2 PP T
＝s 2 (X T X+R) -1 X T X(X T X+R) -1
diagonal element of the noise covariance matrix [ cov (N) θ )] ii I.e. the corresponding coefficient theta i Variance of the fluctuation of the noise.
Preferably, in S3-3, the step of determining the confidence interval according to the coefficient distribution function, the variance of the noise fluctuation and the expected confidence calculation is as follows:
wherein the expected confidence is a desired value, specified and set by a person.
It should be noted that, since the noise on the coefficient is superimposed by the noise map of a plurality of data points, it approaches a gaussian distribution according to the central limit theorem if the data amount is sufficient, and can be assumed to be a t distribution more accurately if the data points are few.
Preferably, the Gaussian distribution is used as an example, and the noise mean value in the target is assumed to be 0Mean of lower noise projection
Also 0, and the noise variance over the coefficient is calculated in (3-2).
According to theta i ～N(0，[cov(N θ )] ii ) Calculating the width b of the confidence interval by the coefficient distribution function and the expected confidence P, wherein P (-b is not more than theta) i ≤b)＝p。
And determining the confidence interval according to the confidence interval width.
It should be noted that the confidence interval is a possible variation range of the solving coefficient caused by noise, and when a certain meter coefficient is not in the confidence interval, it is unlikely that the value is caused by noise fluctuation under the corresponding confidence, so that we can judge the error coefficient is caused by true out-of-tolerance with more confidence.
The calculation of the confidence interval takes the influence of many physical factors into consideration, so that the fluctuation range of the coefficient noise under different conditions can be correctly reflected, and the comparability of the calculation results of the distribution room under different qualities is further improved, and the different qualities can be exemplified as follows: the transformer is arranged in the transformer area to convert high-voltage electricity into low-voltage electricity for residents to use, theoretically, the number of household meters under the jurisdiction of one transformer area is large, data is more real, and a model is more reliable. However, some areas are overloaded, and some areas have few jurisdictional user tables, so that physical influence factors of areas with different qualities can be generated.
While the change in confidence interval reflects the change in solution quality/difficulty under different circumstances. The confidence interval is broken down to illustrate the role of various physical factors therein.
For simplicity of explanation, the derivation was done with no regularized ridge regression (i.e., R = O). In addition, a minimum two-interval algorithm and the like also belong to one of realizable modes.
Taking into account the coefficient theta i Corresponding noise fluctuation variance:
[cov(N θ )] ii ＝s 2 [(X T X+O) -1 X T X(X T X+O) -1 ] ii
＝s 2 [(X T X) -1 ] ii
for convenience of presentation, the following presentation methods and conventions are introduced:
i) The method comprises the following steps Suppose X i First column of X (rearrangement of data columns has no effect on the calculation result)
ii)：X -i Matrix formed by removing remaining columns from ith column for X
iii)：θ *i Is represented by X -i Fitting X i The resulting regression coefficient, sse i For corresponding fitted residual errors
iv)：r＝X T X，
According to Schur completion/Schur theorem:
the r-related representation is expanded by definition:
with X -i Fitting X i Goodness of fit when defined as:
two sides are transformed to obtain:
coefficient of substitutionSse in the noise variance equation i The following can be obtained:
the influence of specific physical factors on the width of the confidence interval can be intuitively analyzed based on the decomposition:
1) S: according to the noise size in the statistical line loss estimated by the fitting residual error, the noise size (standard deviation measurement) is in direct proportion to the confidence interval degree, the width of the confidence interval of the station area with high noise is increased, and the reliability of the solving coefficient is reduced. The residual error in the statistical line loss is related to many factors, and various noises are contained in the statistical line loss, such as table-summary quantization precision, distribution quantization precision, clock delay, abnormal data recording and the like, which cause the fitting residual error to become large.
2)
The term represents the effect of the data volume, the confidence interval width is inversely proportional to the root signal of the data volume, the confidence interval width is about narrow when the data volume is larger, and the influence of noise on model solution is smaller.
3)
This term represents the magnitude of the meter semaphore (measured as a standard deviation), where it more clearly indicates that the standard deviation of the corresponding meter, rather than the mean, affects the quality of the solution, the confidence interval width is inversely proportional to the standard deviation of the power consumption of the corresponding meter, and meters with larger standard deviations are less susceptible to noise fluctuations, and therefore the solution coefficient for high-semaphore meters is more reliable.
It should be noted that it is preferable that,
also known as variance enlarging factor-VIF;
this term measures the effect of linear dependence, i column data X when linear dependence is high i Can be used for other data columns X -i More fully fitted and therefore goodness of fit
More towards 1, the larger the confidence interval width, the more widely the term is, the more highly correlated factors are, which can be easily understood from the viewpoint of goodness of fit, such as: when the electricity utilization curves of the electric meters have correlation, the fitting goodness is high; more factors over a large land area always lead to relatively better fit results making the goodness of fit higher, reflecting the difficulties encountered over multi-meter land areas. The introduction of the regular coefficient is mainly used for relieving the influence caused by the term.
In summary, in the intelligent electric energy meter operation error monitoring system in this embodiment, an electric energy meter operation error mathematical model is established according to the law of conservation of energy to output a calculation error, and a confidence interval is calculated, where the confidence interval represents a dynamic fluctuation range of a station noise to the calculation error, and then a secondary operation is performed on the calculation error according to the confidence interval to compensate an error caused by a low power factor to a calculation result, so as to more accurately judge the state of the electric energy meter, maximally reduce erroneous judgment and accurately lock an out-of-tolerance household meter, set a warning message in the system to be replaced when the electric energy meter exceeds the error, and provide specific position data.
Example 2
Referring to fig. 4, a second embodiment of the present invention is based on the previous embodiment and differs therefrom in that: the intelligent power meter operation error monitoring system also includes an intelligent power meter 800, the intelligent power meter 800 including a Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or loaded from a memory module 805 into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input/output (I/O) interface 802 is also connected to the bus.
Various components in the device are connected to the I/O interface 802, including: an input module 803, such as a keyboard, mouse, etc.; an output module 804, such as various types of displays, speakers, etc.; a storage module 805 such as a magnetic disk, optical disk, or the like; and a communications module 806, such as a network card, modem, wireless communications transceiver, or the like. The communication module 806 allows the device to exchange information/data with other devices over a computer network, such as the internet, and/or various telecommunications networks.
The processing unit executes the respective methods and processes described above, such as the methods S1 to S5. For example, in some embodiments, methods S1-S5 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage module 805. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via ROM and/or a communication module. When the computer program is loaded into RAM and executed by the CPU, one or more of the steps of methods S1-S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S5 in any other suitable manner (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
It should be noted that program code for implementing the methods of the present invention can be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is important to note that the construction and arrangement of the present application as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperatures, pressures, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims.
Moreover, in an effort to provide a concise description of the exemplary embodiments, all features of an actual implementation may not be described (i.e., those unrelated to the presently contemplated best mode of carrying out the invention, or those unrelated to enabling the invention).
It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure, without undue experimentation.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (4)
1. An intelligent electric energy meter operation error monitoring system is characterized by comprising,
the monitoring device (100) comprises an acquisition master station (101), a concentrator (102), a transformer area electric energy meter master meter (103), a transformer (104) and a resident electric energy meter user meter (105); and the number of the first and second groups,
the data acquisition unit (200) is used for circularly acquiring the electricity consumption data of the electric energy meter under the metering point based on the time window;
the error calculation unit (300) generates an equation set according to the electricity utilization data and the electric energy meter operation error mathematical model, and solves the equation set to determine the calculation error of the electric energy meter metering point;
a confidence interval calculation unit (400) which calculates a confidence interval that the noise of the table region under the current measuring point randomly influences the calculation error based on the analysis of the ridge regression model;
the comparison unit (500) judges the relation between the calculation error and the confidence interval, and sets the calculation error falling into the confidence interval as the running error of the electric energy meter;
the marking unit (600) is used for setting the calculation error exceeding the confidence interval as the real out-of-tolerance and marking the measured out-of-tolerance electric energy meter;
the sending unit (700) is used for sending the out-of-tolerance electric energy meter information and the real out-of-tolerance to the station electric energy meter general meter (103) or the acquisition master station (101);
the collection master station (101) is connected with the concentrator (102) through GPRS, the distribution area electric energy meter master table (103) is arranged on a power supply cable from a transformer (104) to a resident electric energy meter user meter (105) and is installed near the transformer (104), and the distribution area electric energy meter master table (103) is connected with the concentrator (102) through a power line carrier (103 a);
the power utilization data comprises daily freezing data of the electric energy meter, power factors, a transformer area and electric energy meter archive data, and the step of analyzing and calculating the confidence interval of the random influence calculation errors of the noise of the transformer area under the current measuring point based on the ridge regression model comprises the following steps of:
estimating the noise of the statistical line loss of the distribution room according to the fitting residual error of the low-voltage line loss and the model degree of freedom;
estimating a noise fluctuation variance on a solving coefficient according to the noise size and linear transformation of ridge regression solution;
calculating and determining the confidence interval according to a coefficient distribution function, the noise fluctuation variance and an expected confidence;
the solution coefficient corresponds to the calculation error.
2. The system for monitoring the running error of the intelligent electric energy meter according to claim 1, characterized in that: the step of estimating the noise size of the statistical line loss of the distribution room according to the fitting residual error of the low-voltage line loss and the model degree of freedom comprises the following steps:
determining a ridge regression analytic solution formula for the coefficients: θ = (X) T X+R) -1 X T Y; wherein R is a regular matrix in ridge regression;
substituting the analytic solution formula into a data matrix X to obtain a statistical line loss estimation value of the distribution room
The statistical line loss estimation value is obtained
Converting the linear transformation:
H＝X(X T X+R) -1 X T ；
the noise distribution N in the statistical line loss is obtained, and the covariance matrix of the noise distribution N is s 2 I, s is noise standard deviation, var (N) = s 2 The covariance matrix of the residual noise after I-H transformation is:
cov((I-H)N)＝(I-H)cov(N)(I-H) T
＝(I-H)s 2 I(I-H) T
＝s 2 (I-H T -H+HH T )
sse (sum squared error) calculated by fitting the residual noise is the trace of the covariance matrix and an estimate of the noise variance, and the noise magnitude is calculated from the noise variance:
sse＝trace(s 2 (I-H T -H+HH T ))
＝s 2 (trace(I)-trace(2H)+trace(HH T ))
＝s 2 (m-trace(2H-HH T ))
wherein trace (2H-HH) T ) The noise degrees of freedom are fitted to the model.
3. The intelligent electric energy meter operation error monitoring system according to claim 2, characterized in that: said step of solving for noise fluctuation variance over said coefficients based on said noise magnitude and linear transformation estimate of ridge regression solution comprises:
let P = (X) T X+R) -1 X T Solving a noise covariance matrix on the coefficients:
cov(N θ )＝cov(PN)
＝Pcov(N)P T
＝s 2 PP T
＝s 2 (X T X+R) -1 XTX(X T X+R) -1
diagonal element of the noise covariance matrix [ cov (N) θ )] ii I.e. the corresponding coefficient theta i Variance of the fluctuation of the noise.
4. The system for monitoring the operation error of the intelligent electric energy meter according to claim 3, characterized in that: the step of determining the confidence interval from the coefficient distribution function, the noise fluctuation variance and the expected confidence calculation comprises:
according to theta i ～N(0，[cov(N θ )] ii ) Calculating the width b of a confidence interval by the coefficient distribution function and the expected confidence P, wherein P (-b is not more than theta) i ≤b)＝p；
And determining the confidence interval according to the confidence interval width.
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| CN115542236B (en) * | 2022-11-24 | 2023-06-06 | 北京志翔科技股份有限公司 | Electric energy meter operation error estimation method and device |
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