A transformer area electric energy meter operation error evaluation method, device and medium

Abstract

The present application relates to the technical field of power system, specifically, a kind of transformer area electric energy meter operation error evaluation method, equipment and medium, comprising the following steps: S1, data acquisition and processing;S2, construct the power conservation equation set between total meter and each user meter in transformer area;S3, according to transformer area data volume, use different strategy least square method to solve, solve the error rate e of each user j , S4, correlation algorithm model is constructed;S5, the iterative error rate trend q j Is calculated;S6, review sub-process;S7, transformer area low-voltage user electric energy meter operation state evaluation.The present application is combined by the method of correlation, power conservation and review sub-process etc. multiple point composite model, and is combined dynamic optimization strategy, forms a comprehensive multiple point composite model, provides more comprehensive, accurate electric energy meter state evaluation method, more accurately assesses the actual state of electric energy meter, reduces misjudgment, improves the adaptability and flexibility of system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a method, device, and medium for evaluating the operation error of power meters in a power distribution area. Background Art

[0002] In the traditional evaluation and replacement of power meter status, the method of rotating at the due date is usually adopted. The existing regular inspection and after-sales maintenance mode is not only inefficient but also consumes a large amount of human and material resources. With the popularization of smart power meters and the progress of technology, the actual operation life of power meters generally exceeds the originally scheduled 8-year usage cycle. Therefore, the "one-size-fits-all" due-date rotation method no longer meets the current development needs. In addition, the retrieved power meters lack effective sampling detection and data collection, resulting in insufficient analysis of failure causes and evaluation of power meter status, causing waste of resources.

[0003] The above deficiencies need to be improved. Summary of the Invention

[0004] To solve or alleviate the problem of low accuracy of the power meter status evaluation method in the above-mentioned existing technology, the present invention provides a method, device, and medium for evaluating the operation error of power meters in a power distribution area.

[0005] The technical solution of the present invention is as follows:

[0006] A method for evaluating the operation error of power meters in a power distribution area includes the following steps:

[0007] S1. Data acquisition and processing;

[0008] S2. Construct an electric energy conservation equation set between the main meter and each user meter in the power distribution area;

[0009] S3. Solve using different strategies of the least squares method according to the amount of data in the power distribution area, and solve the error rate e of each user j ,

[0010] When T≥J, solve using the ordinary least squares method, estimate the regression coefficients by minimizing the sum of squared residuals, and obtain the error rate e j as a constant error rate,

[0011] When T<J, solve using the ridge regression algorithm improved by the least squares method, and obtain the error rate e j as an iterative error rate,

[0012] where T is the amount of power data and J is the total number of users in the power distribution area;

[0013] S4. Construct a correlation algorithm model, including:

[0014] Fixed-proportion correlation algorithm model: Set a fixed proportion to construct a fixed-proportion correlation coefficient r between user power consumption and transformer area line loss rate. j ,

[0015] Dynamic coefficient algorithm model: Set a rolling date number as a dynamic calculation window to construct a dynamic coefficient r between user power consumption and transformer area line loss rate. j ';

[0016] S5. Calculate the iterative error rate trend q j Using the data values ​​of T and T-1, substitute them into the energy conservation equations for calculation;

[0017] S6, Review sub-process, based on user power consumption P j and error rate e j Calculate the restored charge Θ j Calculate the dispersion C of the restored charge. j ;

[0018] S7. Evaluation of the operational status of low-voltage user electricity meters in the distribution area: based on a fixed proportional correlation coefficient r. j Dynamic coefficient r j 'Dispersion C' j Absolute value of error rate |e j | and iterative error rate trend q j The status of user j's electricity meter is determined by comparing it with the corresponding preset value.

[0019] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0020] At least one processor; and,

[0021] A memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores computer program instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for evaluating the operating error of the distribution area electricity meter.

[0023] To address the aforementioned problems, the present invention also provides a computer-readable storage medium, comprising a data storage area and a program storage area, wherein the data storage area stores created data and the program storage area stores a computer program; wherein, when the computer program is executed by a processor, it implements the above-described method for evaluating the operating error of a transformer substation energy meter.

[0024] According to the above-described solution, the beneficial effects of this invention are as follows:

[0025] This invention employs a multi-point composite model combined with various data analysis techniques to formulate optimization strategies based on different transformer substation conditions, thereby improving the accuracy of electricity meter status evaluation. Simultaneously, it dynamically adjusts the replacement cycle of electricity meters based on intelligent optimization algorithms, avoiding a "one-size-fits-all" replacement approach and reducing resource waste caused by premature or delayed meter replacement, saving manpower and material costs. Furthermore, it leverages big data and artificial intelligence technologies to achieve online monitoring and evaluation of smart meter operating errors, comprehensively improving the management level and operational efficiency of the power system.

[0026] This invention combines a multi-point composite model, including correlation, energy conservation, and verification sub-processes, with a dynamic optimization strategy to form a comprehensive multi-point composite model. This provides a more comprehensive and accurate method for evaluating the state of electricity meters, more accurately assessing their actual condition, reducing misjudgments, and improving the system's adaptability and flexibility. Existing technologies typically rely on a single model or simple threshold detection; this invention, however, achieves multi-dimensional analysis of electricity meter status through the integration of multiple models.

[0027] This invention employs various optimization strategies to adapt to the characteristics of data from different transformer substations. Whether using a fixed-ratio model or a dynamic interval model, both can be optimized for the different characteristics of electricity meter data from various substations, providing a highly flexible and adaptable solution. Existing technologies typically rely on general substation data analysis and optimization, lacking consideration for situations such as small-scale substations. This invention, through adaptive optimization strategies, can better address various complex substation electricity meter status evaluation scenarios, improving the overall system's adaptability and intelligence level.

[0028] Traditional technologies typically rely on periodic inspections and post-incident maintenance, or use single models for direct assessment. These methods often suffer from significant errors in meter condition evaluation due to inaccurate predictions. This invention, by combining a multi-point composite model with various data analysis techniques, can fully leverage data characteristics to accurately assess meter condition, significantly improving the accuracy of condition evaluation.

[0029] Traditional fixed-mode methods are poorly adaptable to different types and states of electricity meters, while the multi-point composite model and intelligent optimization strategy of this invention enable the system to adapt to various types and states of electricity meters, improving the system's adaptability and flexibility. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating a method for evaluating the operational error of a power meter in a distribution area, provided in an embodiment of the present invention.

[0032] Figure 2 This is a schematic diagram of the internal structure of an electronic device for implementing a method for evaluating the operating error of a power meter in a distribution area, as provided in an embodiment of the present invention. Detailed Implementation

[0033] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0034] It should be noted that when a component is referred to as "fixed," "set," or "connected" to another component, it may be located directly or indirectly on that other component. The terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or position based on the accompanying drawings, and are for ease of description only, and should not be construed as limiting the technical solution. The terms "first," "second," etc., are used for ease of description only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features. "Many" means two or more, unless otherwise explicitly specified. "Several" means one or more, unless otherwise explicitly specified.

[0035] like Figure 1 As shown in the figure, a method for evaluating the operating error of a distribution area electricity meter according to one embodiment of the present invention includes the following steps:

[0036] S1. Data acquisition and processing;

[0037] Specifically, first, acquire the data by reading the electricity data T for more than M consecutive days from the regional master meter and each user's electricity meter, including archives and daily electricity data, and then calculate the regional statistical line loss rate P on day t. xsl :

[0038]

[0039] M≥200, P gr The total electricity supplied to the distribution area on day t is P. j This refers to the electricity consumption of user j within the distribution area on day t.

[0040] Next, the acquired data is preprocessed.

[0041] S101. Eliminate uncalculated transformer areas: When the total number of days T of transformer area data meets the first preset condition, mark the transformer area as an uncalculated transformer area, and mark the electricity meters of each user under the transformer area as observation tables, which will not participate in the subsequent electricity meter status evaluation.

[0042] S102. Remove noise data and data that does not conform to the actual power situation: When the line loss rate P of the transformer area is calculated... xsl When the second preset condition is met, the data for that day is marked as noise data, and the data for that day is removed, and the result is used as the total number of days T after data preprocessing.

[0043] The first preset condition is T<30, and the second preset condition is P. xsl >30% or P xsl <-8% or P xsl =0. In practical applications, the first and second preset conditions can be adjusted according to the actual situation of the transformer area. A weighted average of daily electricity consumption is calculated monthly to smooth data fluctuations and ensure the accuracy of the analysis.

[0044] In a preferred embodiment, in S1, the preprocessed data is subjected to a weighted average process to calculate the weighted average power consumption. The calculation period is Q.

[0045] Daily electricity consumption data is P i If the calculation period is Q = 7, then the weighted average electricity consumption is... The calculation formula is:

[0046]

[0047] Among them, w i The weighting coefficient for each day can be adaptively set according to electricity consumption fluctuations or user characteristics. Based on experience, it is uniformly set to 1. Through this weighted average strategy, short-term fluctuations in electricity consumption can be smoothed out, resulting in more stable electricity data.

[0048] S2. Construct a set of energy conservation equations between the main meter and each user meter within the transformer area;

[0049] Specifically, the power supply sequence of the transformer area is obtained by weighted averaging of the T-day data. Electricity consumption sequence of users in the distribution area in and Let represent the power supplied to the transformer area and the weighted average power consumption of user j in the transformer area during the T / Q period, respectively. Then, the energy conservation equations are as follows:

[0050]

[0051] Where e jLet e0 be the error rate of the electricity meter for user j, and e0 be the variable and fixed losses of the distribution area, which can be estimated based on the theoretical line loss formula of the power grid:

[0052]

[0053] Among them, A xl The theoretical line loss is N, the grid structure constant at the low-voltage outlet of the distribution transformer (3 for three-phase three-wire, 3.5 for three-phase four-wire, and 2 for single-phase two-wire), K is the characteristic coefficient of the line load curve, and I... av R is the average operating current of the line. dz The equivalent resistance of the line;

[0054] I av Average operating current of the line:

[0055]

[0056] or

[0057]

[0058] Among them, U av Average operating voltage of the line, U av ≈U e =0.38, and The active power supply and reactive power supply are collected at the beginning of the line, and the load power factor is collected for low-voltage lines.

[0059] Line load curve characteristic coefficient K:

[0060]

[0061] Among them, I i To collect hourly current;

[0062] R dz The equivalent resistance of the line is as follows:

[0063]

[0064] Where R is the resistance of the transformer conductor. A set of energy conservation equations is constructed and iteratively calculated daily to ensure the stability and reliability of the energy calculation.

[0065] S3. Solve the error rate e for each user using the least squares method with different strategies based on the amount of data in the distribution area. j ,

[0066] When T≥J, the ordinary least squares method is used to solve the problem. The regression coefficients are estimated by minimizing the sum of squared residuals, and the error rate e is obtained. jAs the constant error rate, the calculation process of the constant error rate is as follows:

[0067] y = Xβ + ∈

[0068] where y is a T×1 vector of observed values; X is a T×J design matrix, where each column represents a predictor variable; β is a J×1 vector of regression coefficients; ∈ is a T×1 vector of deviations;

[0069] The objective function of ordinary least squares is to minimize the sum of squared residuals, that is:

[0070] RSS = ||y - Xβ|| 2

[0071] By minimizing the objective function, the estimated value of the regression coefficient is obtained:

[0072]

[0073] where X T X is the inner product of the design matrix, then (X T X) -1 is the inverse matrix of this matrix; X T y is the inner product accumulation of the design matrix and the observed values;

[0074] When T < J, the ridge regression algorithm improved by the least squares method is used to solve, and the error rate e is obtained j As the iterative error rate, the objective function of the iterative error rate is:

[0075] RSS = ||y - Xβ|| 2 + λ||β|| 2

[0076] The estimated value of the regression coefficient is obtained:

[0077]

[0078] where λ is the regularization parameter, where T is the amount of power data and J is the total number of users in the transformer area.

[0079] S4. Construct a correlation algorithm model, including:

[0080] Fixed - ratio correlation algorithm model: Set a fixed ratio to construct the fixed - ratio correlation coefficient r between the user's electricity consumption and the transformer area line loss rate j , the fixed - ratio Pearson correlation coefficient between the user's electricity consumption and the transformer area line loss rate, and the calculation formula is as follows:

[0081]

[0082] where r j is the fixed - ratio correlation coefficient of user j; Pj,l For user j, the amount of electricity consumed on day l P represents the average battery consumption of user j over L days; xsl,l For the line loss rate of the transformer area on day l, The average line loss rate of the transformer substation over L days;

[0083] Dynamic coefficient algorithm model: Set a rolling date number as a dynamic calculation window to construct a dynamic coefficient r between user power consumption and transformer area line loss rate. j ';

[0084] Let the dynamic calculation window start at i, then the window range is [i, i+0-1]. The dynamic coefficient r of user power consumption and transformer area line loss rate in a single window is... j The calculation formula is as follows:

[0085]

[0086] Where, r j ' is the dynamic coefficient of user j, P j,l For user j, the battery level on day l within the window, P represents the average battery level of user j within the window. xsl,l The line loss rate of the transformer substation on day l within the window, T-O+1 represents the average line loss rate of the transformer area within the window; T-O+1 is the total number of windows.

[0087] S5. Calculate the iterative error rate trend q j The formula is as follows:

[0088] q j =e jT -e jT-1

[0089] Using the data amounts T and T-1, the iteration error rate e is calculated by substituting them into the energy conservation equations. jT e jT-1 .

[0090] S6, Review sub-process, based on user power consumption P j and error rate e j Calculate the restored charge Θ j Calculate the dispersion C of the restored charge. j ;

[0091] Calculate the user's restored electricity Θ j :

[0092]

[0093] Then the amount of electricity restored by user j on day T is Θ. j,T ={Θ j,1 ,Θj,2 ,...,Θ j,T};

[0094] Calculate the dispersion C of the restored charge. j :

[0095]

[0096] in, Let be the average amount of electricity restored by user j over T days. Output a comprehensive suspicion level based on the iterative error rate, constant error rate, and the dispersion of restored electricity.

[0097] S7. Evaluation of the operational status of low-voltage user electricity meters in the distribution area: based on a fixed proportional correlation coefficient r. j Dynamic coefficient r j 'Dispersion C' j Absolute value of error rate |e j | and iterative error rate trend q j The status of user j's electricity meter is determined by comparing it with the corresponding preset value.

[0098] Specifically, when user j satisfies: a fixed correlation coefficient r j >0.9, dynamic coefficient r j >0.85, dispersion C j <0.3, absolute value of error rate |e j When |>10%, iterative error rate trend q j If the value is less than 0, the user j's electricity meter is considered to be in an abnormal state; otherwise, the user j's electricity meter is considered to be in a normal state. In practical applications, the preset values ​​for each evaluation parameter can be determined based on actual needs and experience.

[0099] This invention combines a multi-point composite model, including correlation, energy conservation, and verification sub-processes, with a dynamic optimization strategy to form a comprehensive multi-point composite model. This provides a more comprehensive and accurate method for evaluating the state of electricity meters, more accurately assessing their actual condition, reducing misjudgments, and improving the system's adaptability and flexibility. Existing technologies typically rely on a single model or simple threshold detection; this invention, however, achieves multi-dimensional analysis of electricity meter status through the integration of multiple models.

[0100] This invention employs a unique optimization strategy to adapt to the characteristics of data from different transformer substations. Whether using a fixed-ratio model or a dynamic interval model, it can optimize for the different characteristics of electricity meter data from various substations, providing a highly flexible and adaptable solution. Existing technologies typically rely on general substation data analysis and optimization, lacking consideration for situations such as small-scale substations. This invention, through an adaptive optimization strategy, can better address various complex substation electricity meter status evaluation scenarios, improving the overall system's adaptability and intelligence.

[0101] Traditional technologies typically rely on periodic inspections and post-incident maintenance, or use single models for direct assessment. These methods often suffer from significant errors in meter condition evaluation due to inaccurate predictions. This invention, by combining a multi-point composite model with various data analysis techniques, can fully leverage data characteristics to accurately assess meter condition, significantly improving the accuracy of condition evaluation.

[0102] Traditional fixed-mode methods are poorly adaptable to different types and states of electricity meters, while the multi-point composite model and intelligent optimization strategy of this invention enable the system to adapt to various types and states of electricity meters, improving the system's adaptability and flexibility.

[0103] like Figure 2 The diagram shown is a structural schematic of the electronic device used in this invention to implement the method for evaluating the operating error of electricity meters in a distribution area.

[0104] The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a transformer area energy meter operation error evaluation program.

[0105] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a program to evaluate the operating error of a distribution area energy meter) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0106] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed in the electronic device, such as the code of a transformer substation energy meter operation error evaluation program, but also to temporarily store data that has been output or will be output.

[0107] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0108] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0109] Figure 2 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 2The structures shown do not constitute a limitation on the electronic device, which may include fewer or more components than those shown, or combine certain components, or have different component arrangements.

[0110] For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to at least one processor 10 through a power management device, so as to implement functions such as charging management, discharging management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0111] It should be understood that the embodiments are for illustrative purposes only and are not limited by this structure in the scope of the patent application.

[0112] The operation error evaluation program of the area power meter stored in the memory 11 of the electronic device is a combination of multiple computer programs. When running in the processor 10, it can implement:

[0113] S1. Data acquisition and processing;

[0114] S2. Construct an electric energy conservation equation system between the main meter and each user meter in the area;

[0115] S3. Use different strategies of the least squares method to solve according to the area data volume, and solve the error rate e of each user j ,

[0116] When T≥J, the ordinary least squares method is used to solve, and the regression coefficients are estimated by minimizing the sum of squared residuals, and the error rate e j is obtained as a constant error rate,

[0117] When T<J, the ridge regression algorithm improved by the least squares method is used to solve, and the error rate e j is obtained as an iterative error rate,

[0118] where T is the electric energy data volume and J is the total number of users in the area;

[0119] S4. Construct a correlation algorithm model, including:

[0120] Fixed ratio correlation algorithm model: Set a fixed ratio to construct a fixed ratio correlation coefficient r between the user electricity consumption and the area line loss rate j ,

[0121] Dynamic coefficient algorithm model: Set the number of rolling dates as a dynamic calculation window to construct a dynamic coefficient r between the user electricity consumption and the area line loss rate j ';

[0122] S5. Calculate the iterative error rate trend q j Using the data values ​​of T and T-1, substitute them into the energy conservation equations for calculation;

[0123] S6, Review sub-process, based on user power consumption P j and error rate e j Calculate the restored charge Θ j Calculate the dispersion C of the restored charge. j ;

[0124] S7. Evaluation of the operational status of low-voltage user electricity meters in the distribution area: based on a fixed proportional correlation coefficient r. j Dynamic coefficient r j 'Dispersion C' j Absolute value of error rate |e j | and iterative error rate trend q j The status of user j's electricity meter is determined by comparing it with the corresponding preset value.

[0125] Specifically, the specific implementation method of the above computer program by the processor 10 can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0126] Furthermore, if the modules / units integrated into an electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be either volatile or non-volatile. For example, a computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0127] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0128] S1. Data acquisition and processing;

[0129] S2. Construct a set of energy conservation equations between the main meter and each user meter within the transformer area;

[0130] S3. Solve the error rate e for each user using the least squares method with different strategies based on the amount of data in the distribution area. j ,

[0131] When T≥J, the ordinary least squares method is used to solve the problem. The regression coefficients are estimated by minimizing the sum of squared residuals, and the error rate e is obtained. jAs a constant error rate,

[0132] When T < J, the ridge regression algorithm improved by the least squares method is used to solve, and the error rate e is obtained j As an iterative error rate,

[0133] where T is the amount of power data and J is the total number of users in the transformer area;

[0134] S4. Construct a correlation algorithm model, including:

[0135] Fixed ratio correlation algorithm model: Set a fixed ratio and construct a fixed ratio correlation coefficient r between the user's power consumption and the line loss rate of the transformer area j ,

[0136] Dynamic coefficient algorithm model: Set the number of rolling dates as a dynamic calculation window and construct a dynamic coefficient r between the user's power consumption and the line loss rate of the transformer area j ';

[0137] S5. Calculate the trend q of the iterative error rate j , and use the data volumes of T and T - 1 to substitute into the power conservation equation set for calculation;

[0138] S6. Recheck the sub - process. According to the user's power consumption P j and the error rate e j , calculate the restored power Θ j , and calculate the dispersion C of the restored power j ;

[0139] S7. Evaluation of the operating status of the low - voltage user electricity meters in the transformer area: According to the fixed ratio correlation coefficient r j , the dynamic coefficient r j ', the dispersion C j , the absolute value of the error rate |e j | and the trend q of the iterative error rate j are compared with the corresponding preset values to judge the status of the electricity meter of user j.

[0140] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation.

[0141] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0143] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0144] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0145] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0146] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0147] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The term "second class" is used to indicate names and does not indicate any specific order.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating operation error of a power meter in a transformer area, characterized in that, Comprising the following steps: S1, data acquisition and processing; S2, constructing the power conservation equation group between the total table and each user table in the transformer area; S3, according to the transformer area data volume using different strategies least square method, solving the error rate of each user , When T ≥ J, the ordinary least squares method is used to estimate the regression coefficients by minimizing the sum of squared residuals, and the error rate is obtained As a constant error rate, When T < J, the least square improved ridge regression algorithm is used to solve, and the error rate is obtained As an iterative error rate, Where T is the amount of power data, J is the total number of users in the transformer area; S4, constructing a correlation algorithm model, comprising: Fixed proportion correlation algorithm model: set a fixed proportion to build a fixed proportion correlation coefficient between user electricity consumption and transformer area line loss rate , Dynamic coefficient algorithm model: set the rolling date number as the dynamic calculation window to construct the dynamic coefficient of user power and feeder loss rate ; S5, calculate the iteration error rate trend Using the data amount of T and T-1, substitute into the power conservation equation set S6, review sub-process, calculate reduced power according to user power and error rate , calculate reduced power , calculate dispersion of reduced power ; S7, the running state evaluation of the low-voltage user electric energy meter in the transformer area: according to the fixed proportion correlation coefficient , dynamic coefficient , dispersion , error rate absolute value and iterative error rate trend , compared with the corresponding preset value, the state of the electric energy meter of user j is judged: when user j satisfies: the fixed proportion correlation coefficient , dynamic coefficient , dispersion , error rate absolute value , iterative error rate trend , it is judged that the state of the electric energy meter of user j is abnormal; if not, it is judged that the state of the electric energy meter of user j is normal.

2. The method for evaluating the operation error of a power meter in a transformer area according to claim 1, characterized in that, In S1, the electric energy data T of more than M days in succession is read from the total table of the transformer area and the electric energy meter of each user, including the archive and daily electric energy data, and the statistical line loss rate of the transformer area on the tth day is calculated : ； is the total amount of power supplied to the transformer area on day t, and is the amount of power consumed by user j in the transformer area on day t.

3. The method for evaluating the operation error of a power meter in a transformer area according to claim 2, characterized in that, In S1, the acquired data is preprocessed, S101, eliminate unaccountable transformer area: when the total number of days T of transformer area data meets the first preset condition, mark the transformer area as unaccountable, and mark each user power meter under the transformer area as an observation table, which does not participate in the subsequent power meter state evaluation; S102, eliminate noise data: when the statistical line loss rate of the area When the second preset condition is met, mark the data of the day as noise data, eliminate the data of the day, and take the total number of days T after data preprocessing.

4. The method for evaluating the operation error of a power meter in a transformer area according to claim 3, characterized in that, In S1, the pre-processed data is weighted and averaged to calculate the weighted and averaged electric quantity , and the calculation period is Q.

5. The method for evaluating the operation error of a power meter in a transformer area according to claim 4, characterized in that, In S2, the substation T-day data is weighted and averaged to obtain the substation power supply sequence }, the substation user power consumption sequence }, wherein and respectively represent the substation power supply and the weighted average power of the substation user j in the T / Q period, and the power conservation equation set is: ； wherein the error rate of the electricity meter for user j, the variable and fixed losses of the transformer station, which can be estimated according to the theoretical line loss formula of the power grid line: ； wherein, is the line theory loss power, is the distribution transformer low voltage outlet power grid structure constant, is the line load curve characteristic coefficient, is the line average operating current, is the line equivalent resistance; Average operating current of the line: ； Or ； wherein, line average operating voltage, , and are the active power supply and the reactive power supply collected at the head of the line, respectively, the load power factor collected at the low voltage line. Line load curve characteristic coefficient : ； wherein, is the hourly current collected; The line equivalent resistance is as follows: ； wherein, R is the conductor resistance of the transformer.

6. The method for evaluating the operation error of a power meter in a transformer area according to claim 5, characterized in that, In S3, the constant error rate calculation process is as follows: ； wherein is an observation vector of ; is a design matrix of , wherein each column represents a predictor variable; is a regression coefficient vector of ; is a bias vector of ; The objective function of the ordinary least squares method is to minimize the sum of squares of residuals, that is: ； By minimizing the objective function, the estimated value of the regression coefficient is obtained: ； where is the inner product of the design matrix, then is the inverse of the matrix; is the inner product of the design matrix and the observations; The iterative error rate objective function is: ； The estimated value of the regression coefficient is obtained: ； wherein is a regularization parameter.

7. The method for evaluating the operation error of a power meter in a transformer area according to claim 6, characterized in that, In S4, the fixed proportion Pearson correlation coefficient of user power and transformer line loss rate is calculated as follows: ； wherein, is the fixed proportional correlation coefficient for user j; is the power of user j on day l, is the average power of user j over L days; is the transformer line loss rate on day l, is the average transformer line loss rate over L days; The dynamic coefficient of user power and transformer line loss rate is set to i as the starting of dynamic calculation window, and the window range is [i, i+0-1]. The single window calculation formula is as follows: ； wherein, is the dynamic coefficient of user j, is the power of user j on day l within the window, is the average power of user j within the window; is the feeder line loss rate on day l within the window, is the average feeder line loss rate within the window; is the total number of windows.

8. The method for evaluating the operation error of a power meter in a transformer area according to claim 7, characterized in that, In S6, Computing user restoring power : ； Then the restored power of user j in T days is ; Computing dispersion of state of charge : ； wherein, is the average of the restored energy of user j over T days.

9. An electronic device, comprising: The electronic device comprises: At least one processor; and The memory is in communication connection with the at least one processor; wherein The memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to execute the transformer area power meter running error evaluation method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The memory comprises a storage data area and a storage program area, the storage data area stores created data, and the storage program area stores a computer program; wherein the computer program is executed by the processor to realize the transformer area power meter running error evaluation method according to any one of claims 1 to 8.

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