CN112381476A - Method and device for determining electric energy meter with abnormal state - Google Patents

Method and device for determining electric energy meter with abnormal state Download PDF

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CN112381476A
CN112381476A CN202110059425.XA CN202110059425A CN112381476A CN 112381476 A CN112381476 A CN 112381476A CN 202110059425 A CN202110059425 A CN 202110059425A CN 112381476 A CN112381476 A CN 112381476A
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energy meter
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陈昊
张密
刘婧
林繁涛
于海波
周晖
彭楚宁
王齐
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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Abstract

A method and a device for determining an abnormal state of an electric energy meter are provided, the method comprises the following steps: the method comprises the steps of calculating line loss residual errors of a plurality of electric energy meters in a reference time period and line loss residual errors of a plurality of electric energy meters in a blind sample time period after power consumption is adjusted based on a line loss residual error model, then comparing the line loss residual errors of the reference time period and the blind sample time period according to preset indexes to obtain electric energy meters in suspicious states and adjustment amplitudes corresponding to the electric energy meters in the suspicious states, calculating index parameter values of the electric energy meters in the suspicious states, determining the electric energy meter with the minimum index parameter value as an electric energy meter with abnormal states, and setting the out-of-tolerance amplitude of the electric energy meter with the abnormal states as the opposite number of the adjustment amplitudes of the power consumption. The method and the device provided by the embodiment of the invention can quickly and efficiently determine the electric energy meter with abnormal state, and can accurately determine the out-of-tolerance range of the electric energy meter with abnormal state by setting various adjustment ranges of the power consumption.

Description

Method and device for determining electric energy meter with abnormal state
Technical Field
The invention relates to the technical field of electric energy meter diagnosis, in particular to a method and a device for determining an electric energy meter with abnormal state.
Background
The operation error of the electric energy meter can change in the operation process, and the change caused by the non-human factor is not sudden generally but slowly changed along with the increase of the service life. The traditional method for monitoring the state of the electric energy meter is to sample the electric energy meter in a regular sampling mode or according to indexes such as batches, operation years and the like, and if the out-of-tolerance electric energy meter exceeds a certain proportion, the electric energy meters in the same batch or in the same year are replaced. However, this method cannot quickly resolve various problems, and is heavy in workload, low in efficiency, and unable to cover the full-scale electric energy meter in operation.
At present, methods for monitoring the state of an electric energy meter in real time based on online collected power utilization information exist, but the method has the problem of poor timeliness, namely, the electric energy meter with sudden change and the out-of-tolerance range of the electric energy meter cannot be determined in time.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for determining an electric energy meter with abnormal state, which aim to solve the problem of poor timeliness of determining the abnormal state of the electric energy meter in the prior art.
In a first aspect, an embodiment of the present invention provides a method for determining an abnormal state of an electric energy meter, including: step S101: calculating line loss residual errors of the plurality of electric energy meters in the reference time period based on a line loss residual error model according to the archive information of each electric energy meter in the plurality of electric energy meters in the transformer area and the power supply quantity, the power consumption quantity, the voltage and the power factor of each electric energy meter in the reference time period; and sequentially selecting each electric energy meter in the plurality of electric energy meters as a target electric energy meter, and performing the steps S102-S104 until all the electric energy meters in the transformer area are traversed: step S102: adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of a preset step length as an adjustment range; step S103: calculating a line loss residual error of the target electric energy meter in the blind sample time period based on a line loss residual error model according to the archive information of the target electric energy meter, the power supply quantity, the voltage and the power factor of the target electric energy meter in the blind sample time period and the adjusted power consumption; step S104: according to preset indexes, comparing the line loss residual error of the reference time period with the line loss residual error of the blind sample time period to obtain the electric energy meter with the suspicious state in multiple adjustment amplitudes and the adjustment amplitude corresponding to the electric energy meter with the suspicious state, and calculating index parameter values of the electric energy meter with the suspicious state; step S105: judging the number of the electric energy meters with the index parameter values within the threshold range; step S106: if the number is larger than 1, sequencing all the electric energy meters with the index parameter values within the threshold value range according to the ascending order of the index grades, so as to determine the electric energy meter with the minimum index parameter value, and determining the electric energy meter with the minimum index parameter value as the electric energy meter with abnormal state, wherein the out-of-tolerance amplitude of the electric energy meter with abnormal state is the opposite number of the electric quantity adjustment amplitude in the blind sample time period of the electric energy meter with abnormal state.
Further, before obtaining line loss residuals of the plurality of electric energy meters in the reference time period by calculating according to the archive information of each electric energy meter in the plurality of electric energy meters in the distribution area and the power supply amount, the power consumption amount, the voltage and the power factor of each electric energy meter in the reference time period based on the line loss residual model, the method includes: acquiring archive information of all electric energy meters in distribution area and all electric energyDuring the collection periodNPower supply, electricity usage, voltage and power factor over the day; wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodREF_NDays, and the interval between the reference time period and the blind sample time period is a preset number of days; whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
Further, the calculating, according to the archive information of each of the plurality of electric energy meters in the distribution room and the power supply amount, the power consumption amount, the voltage and the power factor of each electric energy meter in the reference time period, and based on the line loss residual model, to obtain the line loss residual of the plurality of electric energy meters in the reference time period includes: acquiring archive information of all electric energy meters in distribution area and acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day; wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodKDay; whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；KAccording to the electricity consumption distribution characteristics of the blind sample time period, in the non-blind sample time periodN-LNAnd obtaining the target by adopting a K nearest neighbor algorithm within a day.
Further, by counting the line loss Δy(n) And estimating line loss
Figure 463502DEST_PATH_IMAGE001
Comparing to obtain the line loss residual error modeldYerr(n)：
Figure 267510DEST_PATH_IMAGE002
Wherein the content of the first and second substances,n=1,2，…，N-LN。
further, the statistical line loss Δ is calculated by the following formulay(n)：
Figure 465273DEST_PATH_IMAGE003
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
Further, the estimated line loss is calculated in the following way
Figure 747350DEST_PATH_IMAGE004
：
Figure 212704DEST_PATH_IMAGE005
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure 453193DEST_PATH_IMAGE006
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure 872673DEST_PATH_IMAGE007
is as followskAn estimate of the relative error of each power meter,
Figure 224020DEST_PATH_IMAGE008
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure 45345DEST_PATH_IMAGE009
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
Further, the estimated value is obtained in the following manner
Figure 722314DEST_PATH_IMAGE006
、
Figure 629090DEST_PATH_IMAGE007
、
Figure 518549DEST_PATH_IMAGE010
And
Figure 725539DEST_PATH_IMAGE008
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure 806365DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 934858DEST_PATH_IMAGE012
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) For the station area total electric energy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure 628008DEST_PATH_IMAGE013
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 158346DEST_PATH_IMAGE007
Fixed loss of platform areaε 0 Is estimated value of
Figure 177118DEST_PATH_IMAGE014
Coefficient of line loss of eachβ jk Is estimated value of
Figure 58486DEST_PATH_IMAGE015
。
Further, the adjusting of multiple adjustment ranges of the power consumption of the target electric energy meter in the blind sample time period within the preset range by taking the multiple of the preset step length as the adjustment range includes: multiplying the electricity consumption metering value of the target electric energy meter by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
Further, the index includes: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
Further, according to a preset index, comparing the line loss residual of the reference time period with the line loss residual of the blind sample time period to obtain the electric energy meter with the suspicious state in the multiple adjustment ranges and the adjustment range corresponding to the electric energy meter with the suspicious state, and calculating the index parameter value of the electric energy meter with the suspicious state, the method includes: establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the number of points of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the number of points; respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative value number, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative value number, the standard deviation, the skewness and the four-quadrant spacing; if the parameter values of K indexes in the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-bit distance of the electric energy meter are smaller than a preset threshold value, the electric energy meter is in a suspicious state, and the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded; and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the index of the electric energy meter in the suspicious state.
Further, the index is the inverse of the probability density value product:
Figure 555327DEST_PATH_IMAGE016
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining an abnormal state of an electric energy meter, where the apparatus includes: the line loss residual calculation unit of the reference time interval is used for calculating line loss residual of the plurality of electric energy meters in the reference time interval based on a line loss residual model according to the archive information of each electric energy meter in the plurality of electric energy meters in the transformer area and the power supply quantity, the power consumption quantity, the voltage and the power factor of each electric energy meter in the reference time interval; and each electric energy meter in the plurality of electric energy meters is sequentially selected as a target electric energy meter, and data processing is performed sequentially through the power consumption amplitude modulation unit, the line loss residual error calculation unit in the blind sample period and the index parameter value calculation unit until all the electric energy meters in the transformer area are traversed: the power consumption amplitude modulation unit is used for adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of a preset step length as an adjustment amplitude; the line loss residual calculation unit is used for calculating and obtaining the line loss residual of the target electric energy meter in the blind sample time period based on a line loss residual model according to the archive information of the target electric energy meter, the power supply quantity, the voltage and the power factor of the target electric energy meter in the blind sample time period and the adjusted power consumption; the index parameter value calculation unit is used for comparing the line loss residual error of the reference time period with the line loss residual error of the blind sample time period according to a preset index to obtain the electric energy meter with the suspicious state in multiple adjustment amplitudes and the adjustment amplitude corresponding to the electric energy meter with the suspicious state, and calculating the index parameter value of the electric energy meter with the suspicious state; the judging unit is used for judging the number of the electric energy meters with the index parameter values within the threshold range after the power consumption amplitude modulation unit, the line loss residual error calculating unit in the blind sample period and the index parameter value calculating unit traverse all the electric energy meters in the transformer area; and the sorting and screening unit is used for sorting all the electric energy meters with the index parameter values within the threshold value range according to the ascending order of the index grades when the quantity obtained by the judging unit is greater than 1, so as to determine the electric energy meter with the minimum index parameter value, and determining the electric energy meter with the minimum index parameter value as the electric energy meter with the abnormal state, wherein the out-of-tolerance amplitude of the electric energy meter with the abnormal state is the opposite number of the amplitude adjusted by using the electric quantity in the blind sample time period of the electric energy meter with the abnormal state.
Further, the apparatus further comprises: a data acquisition unit for acquiring the file information of all the electric energy meters in the distribution area and the acquisition period of all the electric energy metersNPower supply, electricity usage, voltage and power factor over the day; wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodREF_NDays, and the interval between the reference time period and the blind sample time period is a preset number of days; whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
Further, the apparatus further comprises: a data acquisition unit for acquiring the file information of all the electric energy meters in the distribution area and all the electric energy metersDuring the collection periodNPower supply, electricity usage, voltage and power factor over the day; wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodKDay; whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；KAccording to the electricity consumption distribution characteristics of the blind sample time period, in the non-blind sample time periodN-LNAnd obtaining the target by adopting a K nearest neighbor algorithm within a day.
Further, by counting the line loss Δy(n) And estimating line loss
Figure 205751DEST_PATH_IMAGE017
Comparing to obtain the line loss residual error modeldYerr(n)：
Figure 628380DEST_PATH_IMAGE018
Wherein the content of the first and second substances,n=1,2，…，N-LN。
further, the statistical line loss Δ is calculated by the following formulay(n)：
Figure 997044DEST_PATH_IMAGE019
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
Further, the estimated line loss is calculated in the following way
Figure 297575DEST_PATH_IMAGE020
：
Figure 68085DEST_PATH_IMAGE021
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure 897501DEST_PATH_IMAGE022
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure 753462DEST_PATH_IMAGE007
is as followskAn estimate of the relative error of each power meter,
Figure 857684DEST_PATH_IMAGE023
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure 217121DEST_PATH_IMAGE009
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
Further, the estimated value is obtained in the following manner
Figure 748597DEST_PATH_IMAGE022
、
Figure 826274DEST_PATH_IMAGE007
、
Figure 232722DEST_PATH_IMAGE014
And
Figure 712245DEST_PATH_IMAGE023
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure 414622DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 714016DEST_PATH_IMAGE025
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) For the station area total electric energy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure 894462DEST_PATH_IMAGE022
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 228491DEST_PATH_IMAGE007
Fixed loss of platform areaε 0 Is estimated value of
Figure 836190DEST_PATH_IMAGE009
Coefficient of line loss of eachβ jk Is estimated value of
Figure 154039DEST_PATH_IMAGE023
。
Further, the power consumption amplitude modulation unit is further configured to: the power consumption of the target electric energy meterThe quantitative value multiplied by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
Further, the index includes: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
Further, the index parameter value calculation unit is further configured to: establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the number of points of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the number of points; respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative value number, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative value number, the standard deviation, the skewness and the four-quadrant spacing; if the parameter values of K indexes in the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-bit distance of the electric energy meter are smaller than a preset threshold value, the electric energy meter is in a suspicious state, and the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded; and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the index of the electric energy meter in the suspicious state.
Further, the index is the inverse of the probability density value product:
Figure 872596DEST_PATH_IMAGE026
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is used, when executed by a processor, to implement the method provided in the embodiments of the present invention.
According to the method and the device for determining the electric energy meter with the abnormal state, provided by the embodiment of the invention, the electric energy meter with the abnormal state and the out-of-tolerance range thereof are determined by comparing the difference of the line loss residual errors of the blind sample time period and the reference time period, namely based on the characteristic change of the line loss residual errors, so that the electric energy meter with the abnormal state can be determined quickly and efficiently, and the out-of-tolerance range of the electric energy meter with the abnormal state can be determined accurately by setting various adjustment ranges of the power consumption. In addition, according to the method and the device for determining the electric energy meter with the abnormal state, provided by the embodiment of the invention, each electric energy meter in the transformer area is monitored and analyzed in real time, so that all the electric energy meters in the transformer area can be covered on the basis of greatly reducing manual on-site checking work, and the low cost and high efficiency of the real-time diagnosis and checking process of the electric energy meters are really realized.
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FIG. 1 illustrates an exemplary flow chart of a method for determining an abnormal state power meter according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus of an electric energy meter for determining a state abnormality according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 illustrates an exemplary flow chart of a method for determining an abnormal state power meter according to an embodiment of the present invention.
As shown in fig. 1, the method includes:
step S101: calculating line loss residual errors of the plurality of electric energy meters in the reference time period based on a line loss residual error model according to the archive information of each electric energy meter in the plurality of electric energy meters in the transformer area and the power supply quantity, the power consumption quantity, the voltage and the power factor of each electric energy meter in the reference time period;
and sequentially selecting each electric energy meter in the plurality of electric energy meters as a target electric energy meter, and performing the steps S102-S104 until all the electric energy meters in the transformer area are traversed:
step S102: adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of the preset step length as an adjustment range;
step S103: calculating a line loss residual error of the target electric energy meter in the blind sample time period based on a line loss residual error model according to the archive information of the target electric energy meter, the power supply quantity, the voltage and the power factor of the target electric energy meter in the blind sample time period and the adjusted power consumption;
step S104: according to preset indexes, comparing the line loss residual error of the reference time period with the line loss residual error of the blind sample time period to obtain the electric energy meter with the suspicious state in various adjustment amplitudes and the adjustment amplitude corresponding to the electric energy meter with the suspicious state, and calculating index parameter values of the electric energy meter with the suspicious state;
step S105: judging the number of the electric energy meters with index parameter values within a threshold range;
step S106: if the quantity is larger than 1, sequencing all the electric energy meters with the index parameter values within the threshold value range according to the ascending sequence of the index grades, so as to determine the electric energy meter with the minimum index parameter value, and determining the electric energy meter with the minimum index parameter value as the electric energy meter with the abnormal state, wherein the out-of-tolerance range of the electric energy meter with the abnormal state is the electric quantity adjustment range in the blind sample time period of the electric energy meter with the abnormal state.
In the embodiment of the present invention, in step S102, the plurality of adjustment ranges are adjustment ranges of all multiples of a preset step length within a preset range, for example, if the preset step length is m, the adjustment range E ism=1+ m a, where a is a positive integer, i.e. a multiple of a predetermined step size, EmThe preset range of (A) is [ -100%, 100%]And the adjustment of the multiple adjustment ranges is adjusted according to the proportion of [ -100%, 100%]Each of which adjusts the amplitude EmAre all adjusted. Step S105 determines the number of the electric energy meters with the index parameter values within the threshold range, and if the number is zero, that is, there is no electric energy meter with the index parameter within the threshold range, it indicates that there is no electric energy meter with abnormal state in the distribution room.
In the above embodiment, the electric energy meter with the abnormal state and the out-of-tolerance range thereof are determined by comparing the difference between the line loss residual errors of the blind sample time period and the reference time period, that is, based on the characteristic change of the line loss residual error, so that the electric energy meter with the abnormal state can be determined quickly and efficiently, and the out-of-tolerance range of the electric energy meter with the abnormal state can be determined accurately by setting various adjustment ranges of the power consumption. In addition, according to the method for determining the electric energy meter with the abnormal state provided by the embodiment of the invention, each electric energy meter in the transformer area is monitored and analyzed in real time, so that all electric energy meters in the transformer area can be covered on the basis of greatly reducing manual on-site checking work, and the low cost and high efficiency of the real-time diagnosis and checking process of the electric energy meters are really realized.
Further, before step S101, the method further includes:
step S100: acquiring archive information of all electric energy meters in distribution area and acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the latest time in the collection periodLNDay, reference period is the period of the acquisition cycle prior to the blind periodREF_NDays, and the interval between the reference time period and the blind sample time period is a preset number of days;
whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
In the embodiment of the invention, the archive information of all the electric energy meters in the transformer area can be acquired from a marketing system of a power grid, and the power supply quantity, the power consumption quantity, the voltage and the power factor can be acquired from a utilization system of the power grid, wherein all the electric energy meters in the transformer area comprise all general meters and sub-meters in the transformer area. For the directly acquired data, preprocessing can be performed to improve the quality of subsequent data analysis, wherein the preprocessing includes data preprocessing operations such as data missing processing and file error data processing. The reference time period and the blind sample time period are both certain time periods in the acquisition cycle, and the blind sample time period can select the later time period in the acquisition cycle according to the requirementLNThe time period of the day, the reference time period may be inN-LNThe selection within the range of the day can be obtained according to the range of the blind sample time period, and it should be noted that, in order to more accurately grasp the characteristic change of the power consumption, the reference time period is usually selected to be a time period which is closer to the blind sample time period and has a certain interval therebetween.
For example, the acquisition period is 300 days, the blind sample period is the last 30 days of the acquisition period, and the reference period may be selected to be 30 days before 7 days of the blind sample period, i.e., the time range from 67 days to 37 days after the acquisition period.
Further, before step S101, the method further includes:
step S100: acquiring archive information of all electric energy meters in distribution area and acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the latest time in the collection periodLNDay, reference period is the period of the acquisition cycle prior to the blind periodKDay;
whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；
KAccording to the electricity consumption distribution characteristics of the blind sample time periodsN-LNAdopting K nearest neighbor in the skyAnd (5) obtaining an algorithm.
In the embodiment of the invention, the archive information of all the electric energy meters in the transformer area can be acquired from a marketing system of a power grid, and the power supply quantity, the power consumption quantity, the voltage and the power factor can be acquired from a utilization system of the power grid, wherein all the electric energy meters in the transformer area comprise all general meters and sub-meters in the transformer area. For the directly acquired data, preprocessing can be performed to improve the quality of subsequent data analysis, wherein the preprocessing includes data preprocessing operations such as data missing processing and file error data processing. The reference time period and the blind sample time period are both certain time periods in the acquisition cycle, and the blind sample time period can select the later time period in the acquisition cycle according to the requirementLNThe time period of the day, the reference time period isN-LNSelecting within the range of days to findKData point closest to power consumption distribution characteristics of each day in blind sample time periodN 1 ，N 2 ，…，N k I.e. thatKAnd (5) day.
In the embodiment, the appropriate reference time period is searched for the blind sample time period by adopting the K-nearest neighbor algorithm, so that the problem of inaccurate diagnosis due to large line loss residual variation caused by the electric energy consumption variation of the electric energy meter in the reference time period is solved effectively.
Further, by counting the line loss Δy(n) And estimating line loss
Figure 90826DEST_PATH_IMAGE017
Comparing to obtain a line loss residual error modeldYerr(n)：
Figure 869426DEST_PATH_IMAGE027
Wherein the content of the first and second substances,n=1,2，…，N-LN。
in the embodiment of the present invention, the first and second substrates,nis as followsnDay, day-to-day line loss residual by day-to-day statistical line loss Δy(n) And estimated line loss per day
Figure 674571DEST_PATH_IMAGE017
The difference of (a) is obtained.
Further, the statistical line loss Δ is calculated by the following formulay(n)：
Figure 196819DEST_PATH_IMAGE028
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
Further, the estimated line loss is calculated in the following manner
Figure 505441DEST_PATH_IMAGE017
：
Figure 720521DEST_PATH_IMAGE029
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure 481804DEST_PATH_IMAGE006
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure 73322DEST_PATH_IMAGE007
is as followskAn estimate of the relative error of each power meter,
Figure 236450DEST_PATH_IMAGE008
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure 622432DEST_PATH_IMAGE009
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
Further, the estimated value is obtained in the following manner
Figure 635125DEST_PATH_IMAGE006
、
Figure 764755DEST_PATH_IMAGE007
、
Figure 782390DEST_PATH_IMAGE010
And
Figure 604853DEST_PATH_IMAGE008
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure 340727DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 274048DEST_PATH_IMAGE012
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) For the station area total electric energy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure 146189DEST_PATH_IMAGE013
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 139553DEST_PATH_IMAGE007
Fixed loss of platform areaε 0 Is estimated value of
Figure 362724DEST_PATH_IMAGE014
Coefficient of line loss of eachβ jk Is estimated value of
Figure 834157DEST_PATH_IMAGE015
。
Further, step S102 includes:
multiplying the electricity consumption metering value of the target electric energy meter by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
Further, the index includes: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
Further, step S104 includes:
step S1041: establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the point of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the point;
step S1042: respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative values, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative values, the standard deviation, the skewness and the four-quadrant spacing;
step S1043: if the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-quadrant spacing of the electric energy meter existKIf the parameter value of each index is smaller than a preset threshold value, the electric energy meter is in a suspicious state, and meanwhile, the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded;
step S1044: and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the indexes of the electric energy meter in the suspicious state.
In the implementation of the invention, the point number and the parameter value are in inverse proportion to the histogram index, and the parameter value can be obtained according to the point number. In step S1043, the adjustment range corresponding to the electric energy meter in the suspicious state is the adjustment range of the electric energy consumption in the blind sample time period in step S102.
Further, the index is the inverse of the probability density value product:
Figure 826384DEST_PATH_IMAGE016
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
In the implementation of the invention, line loss residual errors are fitted to reference time segment data selected by adopting a K nearest neighbor algorithmdYerr(n) Normal distribution density functionfAnd calculating the function of the normal distribution densityfLower density valuef(dYerr(n) Will be sent forward toN-LN+ 1And multiplying the density values of the line loss residuals of each day, and then taking the reciprocal of the multiplied density values to obtain the index of the reference time period selected by the K nearest neighbor algorithm.
Fig. 2 is a schematic structural diagram of an apparatus of an electric energy meter for determining a state abnormality according to an embodiment of the present invention.
As shown in fig. 2, the apparatus includes:
the line loss residual calculation unit 201 is configured to calculate, according to archive information of each of the plurality of electric energy meters in the distribution room and the power supply amount, the power consumption amount, the voltage and the power factor of each of the plurality of electric energy meters in the reference time period, and based on a line loss residual model, line loss residuals of the plurality of electric energy meters in the reference time period; and each electric energy meter in the plurality of electric energy meters is sequentially selected as a target electric energy meter, and data processing is performed sequentially through the power consumption amplitude modulation unit, the line loss residual error calculation unit in the blind sample period and the index parameter value calculation unit until all the electric energy meters in the transformer area are traversed:
the power consumption amplitude modulation unit 202 is used for adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of the preset step length as an adjustment amplitude;
the line loss residual calculation unit 203 in the blind sample time period is used for calculating the line loss residual of the target electric energy meter in the blind sample time period based on a line loss residual model according to the archive information of the target electric energy meter, the power supply quantity, the voltage, the power factor and the adjusted power consumption of the target electric energy meter in the blind sample time period;
the index parameter value calculation unit 204 is configured to compare the line loss residual of the reference time period with the line loss residual of the blind sample time period according to a preset index to obtain an electric energy meter with a suspicious state in multiple adjustment ranges and an adjustment range corresponding to the electric energy meter with the suspicious state, and calculate an index parameter value of the electric energy meter with the suspicious state;
the judging unit 205 is configured to judge the number of the electric energy meters with the index parameter values within the threshold range after the power consumption amplitude modulation unit, the line loss residual error calculation unit at the blind sample time period, and the index parameter value calculation unit traverse all the electric energy meters in the distribution area;
and the sorting and screening unit 206 is configured to, when the number obtained by the determining unit is greater than 1, sort all the electric energy meters with the index parameter values within the threshold range according to an ascending order of the index levels, so as to determine the electric energy meter with the minimum index parameter value, and determine the electric energy meter with the minimum index parameter value as the electric energy meter with the abnormal state, where an out-of-tolerance range of the electric energy meter with the abnormal state is an opposite number of the electric quantity adjustment range in a blind sample time period of the electric energy meter with the abnormal state.
In the embodiment of the present invention, in the power consumption amplitude modulation unit 202, the plurality of adjustment ranges are adjustment ranges of all multiples of the preset step length within the preset range, for example, if the preset step length is m, the adjustment range E ism=1+ m a, where a is a positive integer, i.e. a multiple of a predetermined step size, EmThe preset range of (A) is [ -100%, 100%]And the adjustment of the multiple adjustment ranges is adjusted according to the proportion of [ -100%, 100%]Each of which adjusts the amplitude EmAre all adjusted. The determining unit 205 is configured to determine the number of the electric energy meters with the index parameter values within the threshold range, and if the number is zero, that is, there is no electric energy meter with the index parameter within the threshold range, it indicates that there is no electric energy meter with abnormal state in the distribution room.
In the above embodiment, the electric energy meter with the abnormal state and the out-of-tolerance range thereof are determined by comparing the difference between the line loss residual errors of the blind sample time period and the reference time period, that is, based on the characteristic change of the line loss residual error, so that the electric energy meter with the abnormal state can be determined quickly and efficiently, and the out-of-tolerance range of the electric energy meter with the abnormal state can be determined accurately by setting various adjustment ranges of the power consumption. In addition, according to the device for determining the electric energy meter with the abnormal state, which is provided by the embodiment of the invention, by monitoring and analyzing each electric energy meter in the transformer area in real time, on the basis of greatly reducing manual on-site checking work, all electric energy meters in the transformer area can be covered, and the low cost and high efficiency of the real-time diagnosis and checking process of the electric energy meters are really realized.
Further, the apparatus further comprises:
a data obtaining unit 200, configured to obtain archive information of all electric energy meters in the distribution room and an acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the latest time in the collection periodLNDay, reference period is the period of the acquisition cycle prior to the blind periodREF_NDays, and the interval between the reference time period and the blind sample time period is a preset number of days;
whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
In the embodiment of the invention, the archive information of all the electric energy meters in the transformer area can be acquired from a marketing system of a power grid, and the power supply quantity, the power consumption quantity, the voltage and the power factor can be acquired from a utilization system of the power grid, wherein all the electric energy meters in the transformer area comprise all general meters and sub-meters in the transformer area. For the directly acquired data, preprocessing can be performed to improve the quality of subsequent data analysis, wherein the preprocessing includes data preprocessing operations such as data missing processing and file error data processing. The reference time period and the blind sample time period are both certain time periods in the acquisition cycle, and the blind sample time period can select the later time period in the acquisition cycle according to the requirementLNThe time period of the day, the reference time period may be inN-LNThe selection within the range of the day can be obtained according to the range of the blind sample time period, and it should be noted that, in order to more accurately grasp the characteristic change of the power consumption, the reference time period is usually selected to be a time period which is closer to the blind sample time period and has a certain interval therebetween.
For example, the acquisition period is 300 days, the blind sample period is the last 30 days of the acquisition period, and the reference period may be selected to be 30 days before 7 days of the blind sample period, i.e., the time range from 67 days to 37 days after the acquisition period.
Further, before step S101, the method further includes:
a data obtaining unit 200, configured to obtain archive information of all electric energy meters in the distribution room and an acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the latest time in the collection periodLNDay, reference period is the period of the acquisition cycle prior to the blind periodKDay;
whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；
KAccording to the electricity consumption distribution characteristics of the blind sample time periodsN-LNAnd obtaining the target by adopting a K nearest neighbor algorithm within a day.
In the embodiment of the invention, the archive information of all the electric energy meters in the transformer area can be acquired from a marketing system of a power grid, and the power supply quantity, the power consumption quantity, the voltage and the power factor can be acquired from a utilization system of the power grid, wherein all the electric energy meters in the transformer area comprise all general meters and sub-meters in the transformer area. For the directly acquired data, preprocessing can be performed to improve the quality of subsequent data analysis, wherein the preprocessing includes data preprocessing operations such as data missing processing and file error data processing. The reference time period and the blind sample time period are both certain time periods in the acquisition cycle, and the blind sample time period can select the later time period in the acquisition cycle according to the requirementLNThe time period of the day, the reference time period isN-LNSelecting within the range of days to findKData point closest to power consumption distribution characteristics of each day in blind sample time periodN 1 ，N 2 ，…，N k I.e. thatKAnd (5) day.
In the embodiment, the appropriate reference time period is searched for the blind sample time period by adopting the K-nearest neighbor algorithm, so that the problem of inaccurate diagnosis due to large line loss residual variation caused by the electric energy consumption variation of the electric energy meter in the reference time period is solved effectively.
Further, by counting the line loss Δy(n) And estimating line loss
Figure 489184DEST_PATH_IMAGE020
Comparing to obtain a line loss residual error modeldYerr(n)：
Figure 934072DEST_PATH_IMAGE018
Wherein the content of the first and second substances,n=1,2，…，N-LN。
in the embodiment of the present invention, the first and second substrates,nis as followsnDay, day-to-day line loss residual by day-to-day statistical line loss Δy(n) And the estimated line of each dayDecrease in the thickness of the steel
Figure 209195DEST_PATH_IMAGE017
The difference of (a) is obtained.
Further, the statistical line loss Δ is calculated by the following formulay(n)：
Figure 321508DEST_PATH_IMAGE030
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
Further, the estimated line loss is calculated in the following manner
Figure 125516DEST_PATH_IMAGE020
：
Figure 588858DEST_PATH_IMAGE021
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure 136514DEST_PATH_IMAGE022
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure 368912DEST_PATH_IMAGE007
is as followskAn estimate of the relative error of each power meter,
Figure 343821DEST_PATH_IMAGE023
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure 294460DEST_PATH_IMAGE009
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
Further, the estimated value is obtained in the following manner
Figure 144342DEST_PATH_IMAGE022
、
Figure 231247DEST_PATH_IMAGE007
、
Figure 642636DEST_PATH_IMAGE014
And
Figure 814992DEST_PATH_IMAGE023
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure 704450DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 911441DEST_PATH_IMAGE025
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) For the station area total electric energy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure 228153DEST_PATH_IMAGE022
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 887804DEST_PATH_IMAGE007
Fixed loss of platform areaε 0 Is estimated value of
Figure 580954DEST_PATH_IMAGE009
Coefficient of line loss of eachβ jk Is estimated value of
Figure 376871DEST_PATH_IMAGE023
。
Further, the power consumption amplitude modulation unit is also used for:
multiplying the electricity consumption metering value of the target electric energy meter by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
Further, the index includes: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
Further, the index parameter value calculating unit is further configured to:
establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the point of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the point;
respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative values, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative values, the standard deviation, the skewness and the four-quadrant spacing;
if the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-quadrant spacing of the electric energy meter existKIf the parameter value of each index is smaller than a preset threshold value, the electric energy meter is in a suspicious state, and meanwhile, the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded;
and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the indexes of the electric energy meter in the suspicious state.
In the implementation of the invention, the point number and the parameter value are in inverse proportion to the histogram index, and the parameter value can be obtained according to the point number. And the adjustment amplitude corresponding to the electric energy meter in the suspicious state is the adjustment amplitude of the electric energy used by the electric energy amplitude modulation unit in the blind sample time period.
Further, the index is the inverse of the probability density value product:
Figure 395643DEST_PATH_IMAGE031
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
In the implementation of the invention, line loss residual errors are fitted to reference time segment data selected by adopting a K nearest neighbor algorithmdYerr(n) Normal distribution density functionfAnd calculating the function of the normal distribution densityfLower density valuef(dYerr(n) Will be sent forward toN-LN+ 1And multiplying the density values of the line loss residuals of each day, and then taking the reciprocal of the multiplied density values to obtain the index of the reference time period selected by the K nearest neighbor algorithm.
The present invention also provides a computer readable storage medium storing one or more programs which, when executed by one or more processors, implement any of the above-described methods for determining an abnormal status of an electric energy meter.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (23)

1. A method for determining an abnormal condition of an electric energy meter, the method comprising:
step S101: calculating line loss residual errors of the plurality of electric energy meters in the reference time period based on a line loss residual error model according to the archive information of each electric energy meter in the plurality of electric energy meters in the transformer area and the power supply quantity, the power consumption quantity, the voltage and the power factor of each electric energy meter in the reference time period;
and sequentially selecting each electric energy meter in the plurality of electric energy meters as a target electric energy meter, and performing the steps S102-S104 until all the electric energy meters in the transformer area are traversed:
step S102: adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of a preset step length as an adjustment range;
step S103: calculating a line loss residual error of the target electric energy meter in the blind sample time period based on a line loss residual error model according to the archive information of the target electric energy meter, the power supply quantity, the voltage and the power factor of the target electric energy meter in the blind sample time period and the adjusted power consumption;
step S104: according to preset indexes, comparing the line loss residual error of the reference time period with the line loss residual error of the blind sample time period to obtain the electric energy meter with the suspicious state in multiple adjustment amplitudes and the adjustment amplitude corresponding to the electric energy meter with the suspicious state, and calculating index parameter values of the electric energy meter with the suspicious state;
step S105: judging the number of the electric energy meters with the index parameter values within the threshold range;
step S106: if the number is larger than 1, sequencing all the electric energy meters with the index parameter values within the threshold value range according to the ascending order of the index grades, so as to determine the electric energy meter with the minimum index parameter value, and determining the electric energy meter with the minimum index parameter value as the electric energy meter with abnormal state, wherein the out-of-tolerance amplitude of the electric energy meter with abnormal state is the opposite number of the electric quantity adjustment amplitude in the blind sample time period of the electric energy meter with abnormal state.
2. The method of claim 1, wherein the step of calculating the line loss residuals of the plurality of electric energy meters in the reference time period according to the profile information of each of the plurality of electric energy meters in the distribution room and the power supply amount, the power consumption amount, the voltage and the power factor of each electric energy meter in the reference time period based on the line loss residual model comprises:
acquiring archive information of all electric energy meters in distribution area and acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodREF_NAnd said ginsengThe interval between the examination time period and the blind sample time period is preset days;
whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
3. The method of claim 1, wherein calculating line loss residuals of the plurality of electric energy meters in the reference time period based on the line loss residual model according to the profile information of each of the plurality of electric energy meters in the distribution room and the power supply amount, the power consumption amount, the voltage and the power factor of each of the plurality of electric energy meters in the reference time period comprises:
acquiring archive information of all electric energy meters in distribution area and acquisition period of all electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodKDay;
whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；
KAccording to the electricity consumption distribution characteristics of the blind sample time period, in the non-blind sample time periodN-LNAnd obtaining the target by adopting a K nearest neighbor algorithm within a day.
4. A method according to claim 2 or 3, characterized by counting the line loss Δy(n) And estimating line loss
Figure DEST_PATH_IMAGE001
Comparing to obtain the line loss residual error modeldYerr(n)：
Figure DEST_PATH_IMAGE002
Wherein the content of the first and second substances,n=1,2，…，N-LN。
5. the method of claim 4, wherein the statistical line loss Δ is calculated using the following equationy(n)：
Figure DEST_PATH_IMAGE003
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
6. The method of claim 4, wherein the estimated line loss is calculated as follows
Figure 818463DEST_PATH_IMAGE001
：
Figure DEST_PATH_IMAGE004
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure DEST_PATH_IMAGE005
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure DEST_PATH_IMAGE006
is as followskAn estimate of the relative error of each power meter,
Figure DEST_PATH_IMAGE007
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure DEST_PATH_IMAGE008
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
7. The method of claim 6, wherein the estimated value is obtained by
Figure 250450DEST_PATH_IMAGE005
、
Figure 499029DEST_PATH_IMAGE006
、
Figure DEST_PATH_IMAGE009
And
Figure 97500DEST_PATH_IMAGE007
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) Is total power of the platform areaEnergy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure DEST_PATH_IMAGE012
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 285774DEST_PATH_IMAGE006
Fixed loss of platform areaε 0 Is estimated value of
Figure DEST_PATH_IMAGE013
Coefficient of line loss of eachβ jk Is estimated value of
Figure DEST_PATH_IMAGE014
。
8. The method according to claim 1, wherein the power consumption of the target electric energy meter in the blind sample time period is adjusted by multiple of the preset step length as adjustment ranges in multiple adjustment ranges, and the method comprises the following steps:
multiplying the electricity consumption metering value of the target electric energy meter by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
9. The method of claim 2, wherein the metrics comprise: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
10. The method according to claim 9, wherein the comparing, according to a preset index, the line loss residual of the reference time period and the line loss residual of the blind sample time period to obtain an electric energy meter with a suspicious state and an adjustment amplitude corresponding to the electric energy meter with the suspicious state in a plurality of adjustment amplitudes, and calculating an index parameter value of the electric energy meter with the suspicious state includes:
establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the number of points of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the number of points;
respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative value number, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative value number, the standard deviation, the skewness and the four-quadrant spacing;
if the parameter values of K indexes in the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-bit distance of the electric energy meter are smaller than a preset threshold value, the electric energy meter is in a suspicious state, and the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded;
and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the index of the electric energy meter in the suspicious state.
11. The method of claim 3, wherein the indicator is the inverse of the probability density value product:
Figure DEST_PATH_IMAGE015
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
12. An apparatus for determining an abnormal condition of an electric energy meter, the apparatus comprising:
the line loss residual calculation unit of the reference time interval is used for calculating line loss residual of the plurality of electric energy meters in the reference time interval based on a line loss residual model according to the archive information of each electric energy meter in the plurality of electric energy meters in the transformer area and the power supply quantity, the power consumption quantity, the voltage and the power factor of each electric energy meter in the reference time interval;
and each electric energy meter in the plurality of electric energy meters is sequentially selected as a target electric energy meter, and data processing is performed sequentially through the power consumption amplitude modulation unit, the line loss residual error calculation unit in the blind sample period and the index parameter value calculation unit until all the electric energy meters in the transformer area are traversed:
the power consumption amplitude modulation unit is used for adjusting the power consumption of the target electric energy meter in the blind sample time period within a preset range by taking the multiple of a preset step length as an adjustment amplitude;
the line loss residual calculation unit is used for calculating and obtaining the line loss residual of the target electric energy meter in the blind sample time period based on a line loss residual model according to the archive information of the target electric energy meter, the power supply quantity, the voltage and the power factor of the target electric energy meter in the blind sample time period and the adjusted power consumption;
the index parameter value calculation unit is used for comparing the line loss residual error of the reference time period with the line loss residual error of the blind sample time period according to a preset index to obtain the electric energy meter with the suspicious state in multiple adjustment amplitudes and the adjustment amplitude corresponding to the electric energy meter with the suspicious state, and calculating the index parameter value of the electric energy meter with the suspicious state;
the judging unit is used for judging the number of the electric energy meters with the index parameter values within the threshold range after the power consumption amplitude modulation unit, the line loss residual error calculating unit in the blind sample period and the index parameter value calculating unit traverse all the electric energy meters in the transformer area;
and the sorting and screening unit is used for sorting all the electric energy meters with the index parameter values within the threshold value range according to the ascending order of the index grades when the quantity obtained by the judging unit is greater than 1, so as to determine the electric energy meter with the minimum index parameter value, and determining the electric energy meter with the minimum index parameter value as the electric energy meter with the abnormal state, wherein the out-of-tolerance amplitude of the electric energy meter with the abnormal state is the opposite number of the amplitude adjusted by using the electric quantity in the blind sample time period of the electric energy meter with the abnormal state.
13. The apparatus of claim 12, further comprising:
a data acquisition unit for acquiring the file information of all the electric energy meters in the distribution area and the acquisition period of all the electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodREF_NDays, and the interval between the reference time period and the blind sample time period is a preset number of days;
whereinN、LNAndREF_Nis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toREF_N。
14. The apparatus of claim 12, further comprising:
a data acquisition unit for acquiring the file information of all the electric energy meters in the distribution area and the acquisition period of all the electric energy metersNPower supply, electricity usage, voltage and power factor over the day;
wherein the blind sample time period is the most recent in the acquisition cycleLNDay, the reference time period being in the acquisition cycle before the blind time periodKDay;
whereinN、LNAndKis a natural number, and is provided with a plurality of groups,Ngreater than or equal toLN， NGreater than or equal toK；
KAccording to the electricity consumption distribution characteristics of the blind sample time period, in the non-blind sample time periodN-LNAnd obtaining the target by adopting a K nearest neighbor algorithm within a day.
15. Device according to claim 13 or 14, characterised in that the line loss Δ is calculated by countingy(n) And estimating line loss
Figure 249182DEST_PATH_IMAGE001
Comparing to obtain the line loss residual error modeldYerr(n)：
Figure DEST_PATH_IMAGE016
Wherein the content of the first and second substances,n=1,2，…，N-LN。
16. the apparatus of claim 15, wherein the statistical line loss Δ is calculated using the following equationy(n)：
Figure DEST_PATH_IMAGE017
Wherein the content of the first and second substances,y(n) Is the total electric energy meter in the platform areanThe power supply amount of the day is measured,Φ j (n) Is the first in the platform areajAn electric energy meternThe electricity consumption of the day is measured,j=1,2,…,p，pindicating the number of meters in the area.
17. The apparatus of claim 16, wherein the estimated line loss is calculated as follows
Figure DEST_PATH_IMAGE018
：
Figure DEST_PATH_IMAGE019
Wherein the content of the first and second substances,Φ j (n)、U j (n)、cosφ j (n) Are respectively the first in the platform areajAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor, j=1,2,…,p，pthe number of the electric energy meters of the users in the transformer area,
Figure DEST_PATH_IMAGE020
is as followsjAn estimate of the relative error of each power meter,Φ k (n)、U k (n)、cosφ k (n) Respectively is a station areakAn electric energy meternThe daily electricity consumption metering value, the voltage and the power factor,k=1,2,…,q，qthe number of the electric energy meters of the users in the transformer area,
Figure 93379DEST_PATH_IMAGE006
is as followskAn estimate of the relative error of each power meter,
Figure DEST_PATH_IMAGE021
for the userjAnd the userkThe corresponding estimated value of the line loss coefficient,
Figure 964383DEST_PATH_IMAGE008
the estimated value of the fixed loss of the electric energy meter of the station area is obtained.
18. The apparatus of claim 17, wherein the estimate is obtained by
Figure 367682DEST_PATH_IMAGE020
、
Figure 95467DEST_PATH_IMAGE006
、
Figure 318638DEST_PATH_IMAGE013
And
Figure 23026DEST_PATH_IMAGE021
：
according to the time period of non-blind samplesN-LNThe method comprises the following steps of constructing a statistical line loss equation set by using archive information, power supply quantity, power consumption quantity, voltage and power factors of all electric energy meters in a distribution room in the sky:
Figure DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
is the first stage areanThe statistical line loss of the antenna is calculated,n=1,2,…,N-LN，y(n) For the station area total electric energy meternThe power supply amount of the day is measured,ε j is as followsjThe relative error of each electric energy meter is determined,ε k is as followskThe relative error of each electric energy meter is determined,β jk for the userjAnd the userkThe corresponding line loss coefficient is calculated according to the line loss coefficient,ε 0 the fixed loss of the electric energy meter in the transformer area is obtained;
solving the statistical line loss equation set to obtain the secondjRelative error of electric energy meterε j Is estimated value of
Figure 218515DEST_PATH_IMAGE020
The first stepkRelative error of electric energy meterε k Is estimated value of
Figure 117201DEST_PATH_IMAGE006
Fixed loss of platform areaε 0 Is estimated value of
Figure 827668DEST_PATH_IMAGE008
Coefficient of line loss of eachβ jk Is estimated value of
Figure 837213DEST_PATH_IMAGE021
。
19. The apparatus of claim 12, wherein the power usage amplitude modulation unit is further configured to:
multiplying the electricity consumption metering value of the target electric energy meter by 1+ EmPerforming traversal adjustment, wherein EmTo adjust the amplitude, Em0.2% of a, a is a positive integer, 0.2% is a preset step length, E is more than or equal to-99%m≤100%。
20. The apparatus of claim 13, wherein the indicator comprises: mean, positive and negative number, standard deviation, skewness, quartering distance and histogram.
21. The apparatus of claim 20, wherein the index parameter value calculation unit is further configured to:
establishing a histogram of the line loss residual of the reference time period/the line loss residual of the blind sample time period, obtaining the number of points of the line loss residual of the blind sample time period/the line loss residual of the reference time period falling on the histogram, and obtaining the parameter value of the histogram according to the number of points;
respectively calculating absolute values of differences of the line loss residual error of the reference time period and the line loss residual error of the blind sample time period in the five indexes of mean value, positive and negative value number, standard deviation, skewness and four-quadrant spacing to obtain parameter values of the five indexes of the mean value, the positive and negative value number, the standard deviation, the skewness and the four-quadrant spacing;
if the parameter values of K indexes in the six indexes of the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the four-bit distance of the electric energy meter are smaller than a preset threshold value, the electric energy meter is in a suspicious state, and the adjustment amplitude corresponding to the electric energy meter in the suspicious state is recorded;
and calculating the sum of the parameter values of the six indexes, namely the histogram, the mean value, the number of positive and negative values, the standard deviation, the skewness and the quartile range to obtain the total parameter value of the index of the electric energy meter in the suspicious state.
22. The apparatus of claim 14, wherein the indicator is an inverse of a product of probability density values:
Figure DEST_PATH_IMAGE024
wherein the content of the first and second substances,f(dYerr(n) Is the line loss residualdYerr(n) And fitting the density value of the obtained normal distribution density function.
23. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 11.
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