Method, device and system for determining abnormality of electric energy meter in distribution station area

Abstract

The present application provides a method for determining abnormality of electric energy meters in a distribution station area, including: establishing an electric energy meter error analysis linear regression model based on the acquired total electric energy meter parameters, user electric energy meter parameters, line loss and fixed loss; determining the relative error evaluation value of the user electric energy meter based on the electric energy meter error analysis linear regression model; obtaining the confidence interval envelope surface of the relative error evaluation value of the user electric energy meter, and determining the upper and lower boundaries of the relative error evaluation value; drawing the upper and lower boundaries of the relative error evaluation value and the preset upper and lower limit planes of the relative error of the user electric energy meter in the coordinate system; judging whether the area formed by the upper and lower boundaries of the relative error evaluation value and the area formed by the upper and lower limit planes of the relative error of the user electric energy meter have an intersection, and if there is no intersection, it is determined that the user electric energy meter in this period has an abnormality. The above method intuitively analyzes and judges abnormal user electric energy meters, thereby improving analysis efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, system, storage medium and equipment for determining abnormality of an electric energy meter in a distribution station area. Background Art

[0002] Under the background of the continuous development of smart grid, most of the energy meters used in the power grid system are smart energy meters. Smart energy meters are key equipment supporting the advanced metering system of smart grid. The accuracy and reliability of their measurement are the basis for ensuring the fairness of electricity bill settlement and electricity trading. Smart energy meters can be put into operation on site only after they have passed the verification. However, during use, due to the influence of factors such as the natural environment and the quality of internal components, smart energy meters may malfunction, aging, and failure of components. At the same time, there is the phenomenon of human theft of electricity, which directly affects the accuracy of energy meter measurement. Therefore, on-site detection of energy meters is the key to ensuring the efficient operation of the power grid, and it is also the pain point and difficulty that power grid companies urgently need to solve. Remote online diagnosis of errors in smart meters in low-voltage distribution network stations is of great significance for maintaining the stability of the power grid, saving operation and maintenance costs, and protecting the rights and interests of users in electricity use.

[0003] In order to ensure that smart meters can operate efficiently, the online evaluation of smart meter errors has become a research hotspot. At present, the main method is to use the daily frozen meter code collection data of smart meters, and construct a multivariate linear regression equation through the total and sub-meter code data of the substation. By solving the equation, the error parameters of each smart meter are estimated. The error parameters include the user code of the low-voltage distribution substation and the collection time of the meter code. The error parameters obtained by collecting smart meters in different time periods and for different users will change. Generally, tabular data or plane curves are used to display the error parameters of user smart meters. Since the number of users in a low-voltage distribution substation is generally around 100-300 households, the amount of table data for the error parameters of user smart meters in a certain period of time in the low-voltage distribution substation is too large, and it is not easy to intuitively and clearly judge abnormal user electric energy meters; the plane curve cannot intuitively observe the connection between different electric energy meters in the two dimensions of error parameters and time series, and cannot intuitively analyze and judge abnormal user electric energy meters, which affects the efficiency of data analysis.

[0004] Therefore, considering the intrinsic connection between user electricity meters in terms of error parameters, different users and time series dimensions, how to intuitively analyze and judge abnormal user electricity meters and improve the efficiency of data analysis is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] The present invention provides a method, device, system, storage medium and equipment for determining abnormality of electric energy meters in a distribution station area, which can intuitively analyze and judge abnormal user electric energy meters based on the internal connection between user electric energy meters in error parameters, different users and time series dimensions, thereby improving the efficiency of data analysis.

[0006] In a first aspect, the present invention provides a method for determining an abnormality of an electric energy meter in a distribution station area, comprising:

[0007] Obtain fixed losses within the distribution area, as well as total energy meter parameters, user energy meter parameters and line losses for several time periods within the distribution area;

[0008] Establishing a linear regression model for error analysis of electric energy meters according to the total electric energy meter parameters, user electric energy meter parameters, line losses and fixed losses;

[0009] Determine the relative error evaluation value of the user's electric energy meter in several time periods according to the linear regression model of electric energy meter error analysis;

[0010] Obtaining a confidence interval envelope surface of the relative error evaluation value of the user's electric energy meter according to the relative error evaluation value, and determining an upper boundary and a lower boundary of the relative error evaluation value according to the confidence interval envelope surface of the relative error evaluation value;

[0011] Draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter in the coordinate system;

[0012] Determine whether the area formed by the upper and lower boundaries of the relative error evaluation value intersects with the area formed by the upper and lower limit planes of the relative error of the user's sub-electric energy meter. If no intersection exists, it is determined that there is an abnormality in the user's sub-electric energy meter during this period.

[0013] In a second aspect, the present invention provides a device for determining abnormality of an electric energy meter in a distribution station area, comprising:

[0014] The power parameter acquisition module is used to obtain the fixed loss in the distribution area, as well as the total power meter parameters, user power meter parameters and line losses in several time periods in the distribution area;

[0015] An error model building module is used to build an electric energy meter error analysis linear regression model based on the total electric energy meter parameters, user electric energy meter parameters, line loss and fixed loss;

[0016] The error evaluation value acquisition module is used to determine the relative error evaluation values ​​of the user's electric energy meters in several time periods according to the electric energy meter error analysis linear regression model;

[0017] An error evaluation value boundary acquisition module is used to obtain a relative error evaluation value confidence interval envelope surface of the user's electric energy meter according to the relative error evaluation value, and determine the upper boundary and the lower boundary of the relative error evaluation value according to the relative error evaluation value confidence interval envelope surface;

[0018] A visualization establishment module is used to draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter in the coordinate system;

[0019] The electric energy meter abnormality judgment module is used to judge whether the upper boundary or lower boundary of the relative error evaluation value intersects with the upper limit plane or lower limit plane of the relative error of the user's electric energy meter. If no intersection exists, it is determined that the user's electric energy meter in this period has an abnormality.

[0020] In a third aspect, the present invention provides a system for determining abnormality of electric energy meters in a distribution station area, comprising:

[0021] A total electric energy meter, a plurality of user electric energy meters, and a control terminal with a display device, wherein the total electric energy meter and the user electric energy meters are located in the same distribution station area;

[0022] The total electric energy meter is used to upload total electric energy meter parameters;

[0023] The user sub-electric energy meter is used to upload the user sub-electric energy meter parameters;

[0024] The control terminal with a display device is used to execute the method for determining abnormality of electric energy meters in distribution station areas described in the first aspect above.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining an abnormality of an electric energy meter in a distribution station area according to the first aspect.

[0026] In a fifth aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for determining an abnormality of an electric energy meter in a distribution station area according to the first aspect is executed.

[0027] The beneficial effects of adopting the above technical solution are: a method for determining abnormalities of electric energy meters in a distribution station area of ​​the present application can intuitively analyze the intrinsic relationship between the error parameters and time series of different users through the relative evaluation errors of the electric energy meters of different users in the coordinate system interface, and can intuitively judge and analyze abnormal user electric energy meters quickly by whether there is an intersection between the envelope surface of the confidence interval of the relative evaluation error value and the set limit plane, thereby improving the analysis efficiency of smart meters in low-voltage distribution stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0029] Figure 1 It is a table of error evaluation values ​​of electric energy meters in distribution station areas;

[0030] Figure 2 It is the plane curve form of the user error evaluation value in the low voltage distribution station area;

[0031] Figure 3 A schematic diagram of an application scenario for determining abnormality of electric energy meters in a distribution station area;

[0032] Figure 4 A schematic diagram of a method for determining abnormality of electric energy meters in a distribution station area;

[0033] Figure 5 A schematic diagram of the analysis results of the relative error evaluation value of the selected user's electric energy meter;

[0034] Figure 6 The present invention is a schematic diagram of a device for determining abnormality of electric energy meters in a distribution station area. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In order to explain the present invention in more detail, a method, device, system, storage medium and equipment for determining abnormality of electric energy meters in distribution station areas provided by the present invention are specifically described below in combination with the drawings.

[0036] During the use of smart meters, due to the influence of the natural environment and the quality of internal components, problems such as smart meter failure, device aging, and device failure may occur. At the same time, there is the phenomenon of human theft of electricity, which directly affects the measurement accuracy of the energy meter. In order to ensure the efficient operation of smart meters, the online evaluation of smart meter errors has become a research hotspot, which is of great significance for maintaining the stability of the power grid, saving operation and maintenance costs, and protecting the rights and interests of users in electricity use.

[0037] In the prior art, the data collected by daily freezing of smart meters is mainly used. The multivariate linear regression equation is constructed through the total and sub-meter code data of the distribution area. By solving the equation, the error parameters of each smart meter are estimated. The error parameters include the user code of the distribution area and the collection time of the meter code. The error parameters obtained by collecting smart meters in different time periods and for different users will change. Generally, the error parameters of user smart meters are displayed using tabular data or plane curves.

[0038] Since the number of users in a distribution area is generally around 100-300 households, if the error parameters of the user smart meters in the distribution area within a certain period of time are observed in a table, the amount of data is too large and it is not easy to intuitively and clearly judge the abnormal user electricity meter situation. Figure 1 However, the plane curve cannot directly observe the relationship between the error parameters and time series of different electric energy meters, and it is easy to have a large amount of data overlap, which makes it impossible to directly analyze and judge the abnormal user electric energy meters, affecting the efficiency of data analysis. See Appendix Figure 2 .

[0039] In view of the above situation, the present invention provides a method, device, system, storage medium and equipment for determining abnormality of electric energy meters in a distribution station area.

[0040] Please see attached Figure 3 , attached Figure 3 A schematic diagram showing an application scenario of a method for determining abnormality of an electric energy meter in a distribution station area provided in an embodiment of the present application is shown. The application scenario includes a total electric energy meter provided in an embodiment of the present application, a plurality of user electric energy meters and a control terminal.

[0041] The total electric energy meter is a smart meter, which is used to measure the overall electric energy parameters in the distribution area, and has the function of remotely transmitting the measured electric energy parameters to the control end. The user electric energy meter is a smart meter at the end of the power grid transmission, which is used to measure the specific electric energy parameters of each user in the distribution area, and has the function of transmitting the measured specific electric energy parameters of the user to the control end. The user includes but is not limited to residential users, shop users and vehicle charging piles in the distribution area. The control end can be various electronic terminal devices with a display screen, including but not limited to smart phones and computer devices, wherein the computer device can be at least one of a large computer, a desktop computer, a portable computer, a laptop computer, a tablet computer and the like. The user operates the control end to obtain the electric energy parameters of the total electric energy meter and the electric energy parameters of the user electric energy meter in different distribution areas, and determine the abnormal electric energy meter according to the distribution area electric energy meter analysis data on the display interface. The control end executes the distribution area electric energy meter abnormality determination method of the present application. For the specific process, please refer to the embodiment of the distribution area electric energy meter abnormality determination method.

[0042] Based on this, a method for determining abnormality of an electric energy meter in a distribution station area is provided in an embodiment of the present application. Figure 4 As shown, attached Figure 4 A flow chart of a method for determining abnormality of an electric energy meter in a distribution station area provided in an embodiment of the present application is provided, and the method is applied to a control terminal as an example for explanation, including the following steps:

[0043] Step S1: Obtain fixed losses in the distribution area, as well as total electric energy meter parameters, user electric energy meter parameters and line losses in several time periods in the distribution area;

[0044] In the power system, the distribution area refers to the power supply range or area of ​​a distribution transformer, which is one of the important components of the power system. The distribution area discussed in this embodiment refers to the distribution area corresponding to the low-voltage distribution system. The low-voltage distribution system refers to the distribution system at 220V-380V, which is at the end of the power system and the closest to the terminal power user, and is also the general residential power system.

[0045] In this embodiment, the electricity parameters of the total electricity meter in the distribution station area for several time periods specifically include the indication increment of the total electricity meter for several time periods and the relative error of the total electricity meter for several time periods; the electricity parameters of the user electricity meters in the distribution station area for several time periods specifically include the indication increment of the user electricity meters for several time periods.

[0046] In order to understand the above terms more clearly, the indication increment and relative error are further explained:

[0047] The indication increment of the total electric energy meter is the amount of electricity increased by the total electric energy meter during the period, and y(i) can be used to represent the amount of electricity increased by the total electric energy meter during the i-th period. The relative error of the total electric energy meter is the ratio of the difference between the amount of electricity increased by the total electric energy meter during the period and the actual amount of electricity increased to the actual amount of electricity increased, and the specific expression is: where δ y (i) is the relative error of the total electric energy meter in the i-th period, y(i) 实际 is the actual increase in electricity value of the total electric energy meter in the i-th period.

[0048] The indication increment of the user's electric energy meter is the value of the electric energy added during the period measured by the user's electric energy meter, which can be expressed as x j (i) is the increased electricity value measured by the j-th user's electricity meter in the i-th time period.

[0049] Step S2: Establishing a linear regression model for error analysis of electric energy meters based on the acquired total electric energy meter parameters, user electric energy meter parameters, line losses, and fixed losses.

[0050] Among them, the linear regression model of the electric energy meter error analysis mainly represents the relationship between the increment of the electric energy value and the relative error in the distribution station area. It is used to calculate the relative error of each user's electric energy meter. The staff can judge whether the operation of the electric energy meter is abnormal based on the relative error.

[0051] Furthermore, an implementation method for constructing a linear regression model for error analysis of an electric energy meter is given, which is specifically described as follows:

[0052] According to the power parameters of the total power meter in the distribution station area for several periods, the power parameters of the user's power meter in the distribution station area for several periods, the line loss and fixed loss in several periods, and based on the law of conservation of energy, the power meter error solution model is established:

[0053]

[0054] Among them, y(i) represents the increment of the total electric energy meter y in the area in the i-th period, δ y (i) represents the relative error of the total electric energy meter y in the area in the i-th period, x j (i) represents the increment of the indication of the jth user's electric energy meter in the i-th period, δ j (i) represents the relative error of the jth user's electric energy meter in the i-th period, ε L y(i) represents the line loss in the i-th period of the station area, ε 0 represents the fixed loss of the substation, N is the number of user sub-electricity meters in the substation; the relative error of the user sub-electricity meter is the ratio of the difference between the increased power value measured by the user sub-electricity meter during the period and the actual increased power value to the actual increased power value, and the specific expression is: where δ j (i) is the relative error of the j-th user's electric energy meter in the i-th period, x j (i) 实际 The actual amount of electricity added by the electric energy meter of the j-th user in the i-th time period.

[0055] The above-mentioned electric energy meter error solution model is adjusted to more clearly represent the relationship between the total electric energy meter and the user's electric energy meter in the distribution station area, specifically:

[0056]

[0057] Furthermore, in order to facilitate the subsequent solution of the relative error evaluation value of the electric energy meter, the linear regression model of the electric energy meter error analysis is expressed by adjusting the deformation and using the multivariate linear regression equation. The final expression is:

[0058]

[0059] Where i is the i-th period, i=1,2,…,m, there are m periods in total; j=1,2,…N; regression coefficient

[0060] Step S3: Determine the relative error evaluation values ​​of the user's electric energy meters for several time periods according to the electric energy meter error analysis linear regression model.

[0061] According to the linear regression model of the electric energy meter error analysis in the above step S2, to obtain the relative error evaluation value of the user's electric energy meter, it is necessary to first calculate the optimal solution of the regression coefficient β in the linear regression model, and then calculate the relative error evaluation value of the user's electric energy meter based on the optimal solution of the regression coefficient. The specific calculation process is as follows:

[0062] First, according to the least squares method, when the independent variable x j (i), that is, in this embodiment, when the increment of the user's electric energy is expressed and there is no multicollinearity, the optimal solution of the linear regression model for the error analysis of the electric energy meter is for:

[0063]

[0064] The sample difference estimate of the residual series is:

[0065]

[0066] Among them, the regression coefficient matrix Indication increment matrix of total electric energy meter Indication increment matrix of user's electric energy meter

[0067] Then, according to the optimal solution of the linear regression model of the electric energy meter error analysis, the relative error evaluation value δ of the user electric energy meter in the distribution station area for several time periods is obtained. j (i), the specific expression is:

[0068]

[0069] Step S4: obtaining a confidence interval envelope surface of the relative error evaluation value of the user's electric energy meter according to the relative error evaluation value, and determining an upper boundary and a lower boundary of the relative error evaluation value according to the confidence interval envelope surface of the relative error evaluation value.

[0070] Since the relative error evaluation value is obtained based on the optimal solution of the regression coefficient, in order to improve the credibility of the relevant parameters, it is necessary to first obtain the confidence interval of the optimal solution of the regression coefficient, specifically:

[0071] Setting the optimal solution Under the normal error assumption, the corresponding distribution is:

[0072]

[0073] Take the matrix (X T X) -1 The diagonal elements of are defined as:

[0074] diag(X T X) -1 =v k,k ,k=0,1,…N

[0075] According to the principles of probability statistics, since the population standard deviation σ is an unknown parameter, it is necessary to use the sample difference estimate of the residual sequence to replace it. The t distribution is used. The optimal solution β can be obtained from the definition of the t distribution. i Two-sided 1-α confidence interval for :

[0076]

[0077] Here, α reflects the confidence level of the calculation results, which is generally 0.05 or 0.1. In order to prevent the underreporting rate of this scheme from being too high, the value here is 0.1.

[0078] Then, according to the confidence interval of the regression coefficient and the functional relationship between the regression coefficient and the relative error evaluation value of the user's electric energy meter, the numerical range of the relative error evaluation value is obtained, δ j (i) Specifically expressed as:

[0079]

[0080] Therefore, the relative error evaluation value δ of the user's electric energy meter can be obtained: j (i) The specific upper and lower boundaries of the confidence interval envelope surface,

[0081] Upper Boundary:

[0082]

[0083] Lower Boundary:

[0084]

[0085] Step S5: Draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meters in the coordinate system.

[0086] In this embodiment, the above relative error evaluation value and the relative error upper line plane and lower limit plane of the user's electric energy meter are drawn using a three-dimensional coordinate system. Further, in order to improve the readability of the drawn coordinate system content and improve the efficiency of staff in analyzing abnormalities of user electric energy meters, this embodiment also uses color parameters to distinguish the upper and lower boundaries of different relative error evaluation values, specifically:

[0087] First, plot the upper and lower bounds of the relative error estimate in the coordinate system:

[0088] Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the first interval. If so, set the relative error curve of the user's electric energy meter in this time period to green;

[0089] Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the second interval. If so, set the relative error curve of the user's electric energy meter in this time period to orange;

[0090] It is determined whether the relative error evaluation value of the user's electric energy meter in each time period falls into the third interval. If so, the relative error curve of the user's electric energy meter in this time period is set to red.

[0091] Thus, the staff can intuitively and quickly judge the severity of the error of the user's electric energy meter according to the color of the relative error curve. The first interval, the second interval and the third interval can be set according to the relative error evaluation value standard required for analysis, for example, the first interval is set to [-2%, 2%], the second interval is [-10%, -2%] U [2%, 10%], and the third interval is [-∞, -10] U [10%, +∞]. It should be noted that the above-mentioned intervals are only reference values ​​and can be specifically set according to the requirements for relative error evaluation values ​​in actual applications.

[0092] Then, draw the upper limit plane and lower limit plane of the relative error of the user's electric energy meter:

[0093] The upper limit plane and lower limit plane of the relative error of the user sub-electric energy meter are determined according to the accuracy level of the residential electric energy meter. For example, if the standard level of the residential electric energy meter is set to level 2.0, the upper limit plane of the relative error of the user sub-electric energy meter is Z=2%, and the lower limit plane of the relative error of the user sub-electric energy meter is Z=-2%.

[0094] Step S6: Determine whether the area formed by the upper and lower boundaries of the relative error evaluation value intersects with the area formed by the upper and lower limit planes of the relative error of the user's sub-electric energy meter. If no intersection exists, it is determined that an abnormality exists in the user's sub-electric energy meter during this period.

[0095] Different colors are set in the three-dimensional coordinate system to mark the relative error evaluation values ​​of the user's electric energy meter and the relative error limit plane of the user's electric energy meter. According to whether there is an intersection and the different colors of the user's electric energy meter, it is intuitively and quickly determined whether the user's electric energy meter is abnormal, and the staff are arranged to inspect and process the user's electric energy meter according to the degree of abnormality. Through the three-dimensional space coordinates and combined with the color attributes of the envelope surface of the confidence interval of the relative error evaluation value, the analysis and judgment can be clearly and intuitively carried out even when the number of users is large. Compared with the data table or plane curve in the prior art, the accurate electric energy meter error analysis results can be obtained much faster and more effectively.

[0096] The confidence interval envelope surface of the relative error evaluation value of the user's electric energy meter can also reflect the changing relationship between users. According to the similarity of electricity consumption behaviors between users, if one user steals electricity, the relative error evaluation values ​​of other users with similar behaviors will fluctuate over a large range.

[0097] In order to more intuitively illustrate the low-voltage distribution network smart meter error visualization diagnosis method of the present invention, as shown in the attached Figure 5 As shown, the electric energy meters of 12 users in the substation are selected, and the time interval for data acquisition is 1 day, and the analysis curve of the relative error evaluation value of the electric energy meters of users in a single day is given.

[0098] By the attached Figure 5 It can be quickly analyzed and determined that among the 12 selected users, the upper and lower boundaries of the relative error evaluation values ​​of the electric energy meters of users No. 2 and No. 8 do not intersect with the upper and lower limit planes of the set relative errors of the user's electric energy meters. It is determined that the electric energy meters of users No. 2 and No. 8 on that day have excessive abnormalities, and their data can be further monitored or the electric energy meters of the users can be checked in time.

[0099] Furthermore, since the drawn envelope surface of the confidence interval of the relative error assessment value of the user's electricity meter also includes the time dimension, on this basis, the staff can also obtain the out-of-tolerance suspicion coefficient of the user's electricity meter based on the time period when there is no intersection between the area formed by the upper and lower boundaries of the displayed relative error assessment envelope surface and the area formed by the upper and lower limit planes of the set electricity meter relative error and all time periods of relative error assessment.

[0100] Taking 10 consecutive days as an example, if the user's electricity meter numbered 2 is judged to be abnormal for 9 days out of 10 days, its abnormality suspicion coefficient is 0.9; if the user's electricity meter numbered 8 is judged to be abnormal for 5 days out of 10 days, its abnormality suspicion coefficient is 0.5. The staff can set the urgency of handling the abnormality of the user's electricity meter according to the size of the abnormality suspicion coefficient.

[0101] It should be understood that although Figure 4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Figure 4 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0102] The above-mentioned embodiment disclosed in the present application describes in detail a method for determining abnormality of an electric energy meter in a distribution station area. The above-mentioned method disclosed in the present application can be implemented using equipment in various forms. Therefore, the present application also discloses a distribution station area electric energy meter abnormality determination device corresponding to the above-mentioned method. A specific embodiment is given below for detailed description.

[0103] Second, as attached Figure 6 As shown, the present invention provides a device for determining abnormality of electric energy meters in a distribution station area, comprising:

[0104] The power parameter acquisition module is used to obtain the fixed loss in the distribution area, as well as the total power meter parameters, user power meter parameters and line losses in several time periods in the distribution area;

[0105] An error model building module is used to build an electric energy meter error analysis linear regression model based on the total electric energy meter parameters, user electric energy meter parameters, line loss and fixed loss;

[0106] The error evaluation value acquisition module is used to determine the relative error evaluation values ​​of the user's electric energy meters in several time periods according to the electric energy meter error analysis linear regression model;

[0107] An error evaluation value boundary acquisition module is used to obtain a relative error evaluation value confidence interval envelope surface of the user's electric energy meter according to the relative error evaluation value, and determine the upper boundary and the lower boundary of the relative error evaluation value according to the relative error evaluation value confidence interval envelope surface;

[0108] A visualization establishment module is used to draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter in the coordinate system;

[0109] The electric energy meter abnormality judgment module is used to judge whether there is an intersection between the area formed by the upper boundary and the lower boundary of the relative error evaluation value and the area formed by the upper limit plane and the lower limit plane of the relative error of the user's electric energy meter. If there is no intersection, it is determined that there is an abnormality in the user's electric energy meter during this period.

[0110] In this embodiment, the visualization establishment module is also used to draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter using a three-dimensional coordinate system. Specifically, in order to improve the readability of the drawn coordinate system content and improve the efficiency of the staff in analyzing the abnormality of the user electric energy meter, this embodiment also uses color parameters to distinguish the upper boundary and lower boundary of different relative error evaluation values. The specific drawing method is as follows:

[0111] First, plot the upper and lower bounds of the relative error estimate in the coordinate system:

[0112] Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the first interval. If so, set the relative error curve of the user's electric energy meter in this time period to green;

[0113] Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the second interval. If so, set the relative error curve of the user's electric energy meter in this time period to orange;

[0114] It is determined whether the relative error evaluation value of the user's electric energy meter in each time period falls into the third interval. If so, the relative error curve of the user's electric energy meter in this time period is set to red.

[0115] Thus, the staff can intuitively and quickly judge the severity of the error of the user's electric energy meter according to the color of the relative error curve. The first interval, the second interval and the third interval can be set according to the relative error evaluation value standard required for analysis, for example, the first interval is set to [-2%, 2%], the second interval is [-10%, -2%] U [2%, 10%], and the third interval is [-∞, -10] U [10%, +∞]. It should be noted that the above-mentioned intervals are only reference values ​​and can be specifically set according to the requirements for relative error evaluation values ​​in actual applications.

[0116] Then, draw the upper limit plane and lower limit plane of the relative error of the user's electric energy meter:

[0117] The upper limit plane and lower limit plane of the relative error of the user sub-electric energy meter are determined according to the accuracy level of the residential electric energy meter. For example, if the standard level of the residential electric energy meter is set to level 2.0, the upper limit plane of the relative error of the user sub-electric energy meter is Z=2%, and the lower limit plane of the relative error of the user sub-electric energy meter is Z=-2%.

[0118] For the specific definitions of other modules of the distribution station area electric energy meter abnormality determination device, please refer to the above definition of the method, which will not be repeated here. Each module in the above device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the terminal device in the form of hardware, or can be stored in the memory of the terminal device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0119] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining an abnormality of an electric energy meter in a distribution station area according to the first aspect.

[0120] The computer-readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM (erasable programmable read-only memory), a hard disk or a ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program codes for executing any of the method steps in the above method. These program codes may be read from or written into one or more computer program products, and the program codes may be compressed in an appropriate form.

[0121] In a fourth aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the method for determining an abnormality of an electric energy meter in a distribution station area according to the first aspect when executing the computer program.

[0122] The computer device includes a memory, a processor and one or more computer programs, wherein the one or more computer programs can be stored in the memory and configured to be executed by one or more processors, and the one or more application programs are configured to execute the above-mentioned distribution station area electric energy meter abnormality determination method.

[0123] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire computer device, and executes various functions of the computer device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or more combinations of a central processing unit (CPU), a reporting verifier (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a communication chip.

[0124] The memory may include a random access memory (RAM) or a read-only memory (ROM). The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the terminal device during use, etc.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining abnormality of electric energy meters in a distribution station area, characterized in that: include: Obtain fixed losses within the distribution area, as well as total energy meter parameters, user energy meter parameters and line losses for several time periods within the distribution area; Establishing a linear regression model for error analysis of electric energy meters according to the total electric energy meter parameters, user electric energy meter parameters, line losses and fixed losses; Determine the relative error evaluation value of the user's electric energy meter in several time periods according to the linear regression model of electric energy meter error analysis; Obtaining a confidence interval envelope surface of the relative error evaluation value of the user's electric energy meter according to the relative error evaluation value, and determining an upper boundary and a lower boundary of the relative error evaluation value according to the confidence interval envelope surface of the relative error evaluation value; Draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter in the coordinate system; Determine whether the area formed by the upper and lower boundaries of the relative error evaluation value intersects with the area formed by the upper and lower limit planes of the relative error of the user's sub-electric energy meter. If no intersection exists, it is determined that there is an abnormality in the user's sub-electric energy meter during this period.

2. A method for determining abnormality of electric energy meters in a distribution station area according to claim 1, characterized in that: The total electric energy meter parameters include the indication increment and relative error of the total electric energy meter; The user sub-electric energy meter parameters include the indication increment of the user sub-electric energy meter.

3. A method for determining abnormality of electric energy meters in a distribution station area according to claim 1, characterized in that: Determining the relative error evaluation values ​​of the user's electric energy meters for several time periods according to the electric energy meter error analysis linear regression model includes: Determining the optimal solution of the electric energy meter error analysis linear regression model; The relative error evaluation value of the user's electric energy meter in the distribution station area is determined according to the optimal solution.

4. A method for determining abnormality of electric energy meters in a distribution station area as claimed in claim 3, characterized in that: Determining the upper boundary and the lower boundary of the relative error evaluation value according to the relative error evaluation value confidence interval envelope surface includes: determining a confidence interval for the optimal solution; The upper and lower boundaries of the relative error evaluation value are determined according to the confidence interval of the optimal solution.

5. The method for determining abnormality of electric energy meters in a distribution station area according to claim 1, characterized in that: The upper limit plane and the lower limit plane of the relative error of the user electric energy meter are determined according to the accuracy level of the residential electric energy meter.

6. A method for determining abnormality of electric energy meters in a distribution station area according to claim 1, characterized in that: The step of drawing the upper boundary and the lower boundary of the relative error evaluation value in the coordinate system further includes: Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the first interval. If so, set the relative error curve of the user's electric energy meter in this time period to green; Determine whether the relative error evaluation value of the user's electric energy meter in each time period falls into the second interval. If so, set the relative error curve of the user's electric energy meter in this time period to orange; It is determined whether the relative error evaluation value of the user's electric energy meter in each time period falls into the third interval. If so, the relative error curve of the user's electric energy meter in this time period is set to red.

7. A device for determining abnormality of electric energy meters in a distribution station area, characterized in that: include: The power parameter acquisition module is used to obtain the fixed loss in the distribution area, as well as the total power meter parameters, user power meter parameters and line losses in several time periods in the distribution area; An error model building module is used to build an electric energy meter error analysis linear regression model based on the total electric energy meter parameters, user electric energy meter parameters, line loss and fixed loss; The error evaluation value acquisition module is used to determine the relative error evaluation values ​​of the user's electric energy meters in several time periods according to the electric energy meter error analysis linear regression model; An error evaluation value boundary acquisition module is used to obtain a relative error evaluation value confidence interval envelope surface of the user's electric energy meter according to the relative error evaluation value, and determine the upper boundary and the lower boundary of the relative error evaluation value according to the relative error evaluation value confidence interval envelope surface; A visualization establishment module is used to draw the upper boundary and lower boundary of the relative error evaluation value and the upper limit plane and lower limit plane of the relative error of the preset user electric energy meter in the coordinate system; The electric energy meter abnormality judgment module is used to judge whether there is an intersection between the area formed by the upper boundary and the lower boundary of the relative error evaluation value and the area formed by the upper limit plane and the lower limit plane of the relative error of the user's electric energy meter. If there is no intersection, it is determined that there is an abnormality in the user's electric energy meter during this period.

8. A distribution station area electric energy meter abnormality determination system, characterized in that: include: A total electric energy meter, a plurality of user electric energy meters, and a control terminal with a display device, wherein the total electric energy meter and the user electric energy meters are located in the same distribution station area; The total electric energy meter is used to upload total electric energy meter parameters; The user sub-electric energy meter is used to upload the user sub-electric energy meter parameters; The control terminal with a display device is used to execute the method for determining abnormality of an electric energy meter in a distribution station area as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining abnormality of an electric energy meter in a distribution station area according to any one of claims 1 to 6 are implemented.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the method for determining abnormality of an electric energy meter in a distribution station area according to any one of claims 1 to 6 is performed.

Images