Transformer area electric energy meter operation error evaluation method, equipment and medium

Abstract

The invention relates to the technical field of electric power systems, in particular to a transformer area electric energy meter operation error evaluation method and device and a medium, and the method comprises the following steps: S1, data acquisition and processing; s2, constructing an electric energy conservation equation set between the general table in the transformer area and each user table; s3, solving the error rate ej of each user by using different strategy least square methods according to the data volume of the transformer area; S4, constructing a correlation algorithm model; s5, calculating an iteration error rate trend qj; s6, rechecking the sub-process; and S7, evaluating the running state of the electric energy meter of the transformer area low-voltage user. According to the method, a comprehensive multi-point composite model is formed through a multi-point composite model combination method of correlation, electric energy conservation, sub-process re-checking and the like in combination with a dynamic optimization strategy, so that a more comprehensive and accurate electric energy meter state evaluation method is provided, the actual state of the electric energy meter is evaluated more accurately, misjudgment is reduced, and the reliability of the electric energy meter is improved. And the adaptability and the flexibility of the system are improved.

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 transformer substation area. Background Art

[0002] In the traditional evaluation and replacement work of power meters, the method of rotating them at the expiration date is usually adopted. The existing regular inspection and after-sales maintenance modes are not only inefficient but also consume 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" expiration rotation method no longer meets the current development needs. In addition, the power meters retrieved lack effective sample 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 transformer substation 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 transformer substation 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 transformer substation area;

[0009] S3. Solve using different strategies of the least squares method according to the amount of data in the transformer substation area, and solve the error rate e 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 the 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 the iterative error rate,

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

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

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

[0015] Dynamic coefficient algorithm model: set the rolling date number as the dynamic calculation window to construct the dynamic coefficient r of user power consumption and area line loss rate j ';

[0016] S5. Calculate the iteration error rate trend q j , use the data of T and T-1, substitute into the energy conservation equations to calculate;

[0017] S6, review sub-process, according to the user's power P j and error rate e j , calculate the restored power Θ j , calculate the dispersion C of the restored charge j ;

[0018] S7. Evaluation of the operating status of low-voltage user electric energy meters in the substation area: Based on the fixed proportion correlation coefficient r j , dynamic coefficient r j ', Discreteness C j 、Absolute value of error rate|e j | and the iterative error rate trend q j Compare with the corresponding preset value to determine the status of the electric energy meter of user j.

[0019] In order to solve the above problem, the present invention further 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, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for evaluating the operation error of the substation electric energy meter.

[0023] In order to solve the above problems, the present invention also provides a computer-readable storage medium, including a data storage area and a program storage area, 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, the above-mentioned method for evaluating the operating errors of electric energy meters in a substation is implemented.

[0024] The present invention according to the above scheme has the following beneficial effects:

[0025] The present invention adopts a multi-point composite model combined with a variety of data analysis technologies to formulate optimization strategies according to the conditions of different substations to improve the accuracy of the status evaluation of the electric energy meter. At the same time, based on the intelligent optimization algorithm, the replacement cycle of the electric energy meter is dynamically adjusted to avoid the "one-size-fits-all" expiration rotation mode, reduce the waste of resources caused by replacing the electric energy meter too early or too late, save manpower and material costs, and use big data and artificial intelligence technology to realize the online monitoring and evaluation of the operation error of the intelligent electric energy meter, comprehensively improve the management level and operation efficiency of the power system.

[0026] The present invention forms a comprehensive multi-point composite model by combining multiple point composite models such as correlation, energy conservation and verification sub-process, and combines dynamic optimization strategy, providing a more comprehensive and accurate method for evaluating the status of the electric energy meter, more accurately evaluating the actual status of the electric energy meter, reducing misjudgment, and improving the adaptability and flexibility of the system. The prior art usually relies on a single model or simple threshold detection, while the present invention realizes multi-dimensional analysis of the status of the electric energy meter through the integration of multiple models.

[0027] The present invention can adapt according to the characteristics of different area data through different optimization strategies. Whether it is a fixed ratio model or a dynamic interval model, it can optimize the data characteristics of different area electric energy meters, providing a highly flexible and adaptable solution. The existing technology usually relies on general area data analysis and optimization, and lacks consideration of small-scale areas and other situations. The present invention can better cope with various complex area electric energy meter status evaluation scenarios through adaptive optimization strategies, thereby improving the adaptability and intelligence level of the overall system.

[0028] Traditional technologies usually rely on regular inspections and post-maintenance at fixed intervals, or use a single model for direct judgment. This method often leads to large errors in the evaluation of the status of the electric energy meter due to inaccurate predictions. The present invention uses a multi-point composite model that combines multiple data analysis techniques to fully mine data features, accurately evaluate the status of the electric energy meter, and greatly improve the accuracy of status evaluation.

[0029] The fixed mode of the traditional method has poor adaptability to electric energy meters of different types and states, while the multi-point composite model and intelligent optimization strategy of the present invention enable the system to adapt to electric energy meters of various types and states, thereby improving the adaptability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a flow chart of a method for evaluating an operation error of an electric energy meter in an area provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of the internal structure of an electronic device for implementing a method for evaluating the operation error of an area electric energy meter provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 used to limit the present invention.

[0034] It should be noted that when a component is referred to as being "fixed" or "set" or "connected" to another component, it may be located directly or indirectly on the other component. The directions or positions indicated by the terms "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. are based on the directions or positions shown in the accompanying drawings and are only for the convenience of description and should not be construed as limitations on the present technical solution. The terms "first", "second", etc. are only used for the convenience of description and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features. "Multiple" means two or more, unless otherwise clearly and specifically defined. "Several" means one or more, unless otherwise clearly and specifically defined.

[0035] like Figure 1 As shown, a method for evaluating the operation error of an electric energy meter in a substation according to an embodiment of the present invention comprises the following steps:

[0036] S1, data acquisition and processing;

[0037] Specifically, first obtain data, read the amount of power data T for more than M consecutive days from the total meter of the area and the power meter of each user, including archive and daily power data, and calculate the statistical line loss rate P of the area on the tth day xsl :

[0038]

[0039] M ≥ 200, P gr The total power supply to the area on the tth day is P j is the electricity consumption of user j in the substation on the tth day.

[0040] Next, the acquired data is preprocessed.

[0041] S101, eliminating uncountable areas: when the total number of days T of area data meets the first preset condition, the area is marked as an uncountable area, and the electric energy meters of each user in the area are marked as observation meters, and do not participate in the subsequent electric energy meter status evaluation;

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

[0043] The first preset condition is T<30, and the second preset condition P xsl >30% or P xsl <-8% or P xsl =0. In actual application, the first preset condition and the second preset condition can be adjusted according to the actual situation of the substation. The daily electricity consumption is weighted averaged on a monthly basis to smooth data fluctuations and ensure the accuracy of the analysis.

[0044] In a preferred embodiment, in S1, the pre-processed data is subjected to weighted average processing, and the weighted average power is calculated. The calculation period is Q.

[0045] The daily power data is P i , taking the calculation period Q = 7, the weighted average power The calculation formula is:

[0046]

[0047] Among them, w i is the weight coefficient for each day, which can be set adaptively according to the fluctuation of electricity consumption or user characteristics. It is uniformly set to 1 based on experience. Through this weighted average strategy, short-term fluctuations in electricity consumption can be smoothed and more stable electricity data can be obtained.

[0048] S2, constructing a group of electric energy conservation equations between the total meter and each user meter in the substation area;

[0049] Specifically, the T-day data of the substation is weighted averaged to obtain the substation power supply sequence Electricity consumption sequence of users in the area in and Represent the power supply to the substation and the weighted average power of user j in the T / Q period, respectively. Then the power conservation equations are:

[0050]

[0051] where e jis the error rate of the electric energy meter of user j, and e0 is the variable loss and fixed loss of the substation, which can be estimated according to the theoretical line loss formula of the power grid:

[0052]

[0053] Among them, A xl is the theoretical line loss of the line, N is the low-voltage outlet grid structure constant of the distribution transformer, 3 for three-phase three-wire, 3.5 for three-phase four-wire, 2 for single-phase two-wire, K is the characteristic coefficient of the line load curve, I av is the average operating current of the line, R dz is 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 They are respectively used to collect active power supply and reactive power supply at the head end of the line, and load power factor for the cosθ low-voltage line;

[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 conductor in the substation. The energy conservation equations are constructed and iterative calculations are performed on a daily basis to ensure the stability and reliability of the energy calculation.

[0065] S3. Use the least square method with different strategies to solve the error rate e of each user according to the amount of data in the station area. j ,

[0066] When T ≥ J, the ordinary least squares method is used to estimate the regression coefficient by minimizing the residual sum of squares to obtain the error rate e 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 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 substation area.

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

[0080] Fixed-ratio correlation algorithm model: Set a fixed ratio to construct a fixed-ratio correlation coefficient r between the user electricity consumption and the substation area line loss rate j , the fixed-ratio Pearson correlation coefficient between the user electricity consumption and the substation 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 is the power consumption of user j on day l, is the average power consumption of user j in L days; P xsl,l is the line loss rate of the station area on the first day, is the average line loss rate of the substation area within L days;

[0083] Dynamic coefficient algorithm model: set the rolling date number as the dynamic calculation window to construct the dynamic coefficient r of user power consumption and area line loss rate j ';

[0084] Assume that the dynamic calculation window starts at i, then the window range is [i,i+O-1], and the dynamic coefficient r of the user power consumption and the line loss rate of the substation in a single window is j 'The calculation formula is as follows:

[0085]

[0086] Among them, r j ' is the dynamic coefficient of user j, P j,l is the power consumption of user j on day l in the window, is the average power consumption of user j in the window; P xsl,l is the line loss rate of the area on the first day within the window, is the average area line loss rate within the window; T-O+1 is the total number of windows.

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

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

[0089] Use the data of T and T-1, substitute into the energy conservation equations, and calculate the iteration error rate e jT 、e jT-1 .

[0090] S6, review sub-process, according to the user's power P j and error rate e j , calculate the restored power Θ j , calculate the dispersion C of the restored charge j ;

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

[0092]

[0093] Then the restored power of user j in T days 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, is the average value of restored electricity of user j in T days. The comprehensive suspicion is output by iterating the error rate, constant error rate, and the dispersion of restored electricity.

[0097] S7. Evaluation of the operating status of low-voltage user electric energy meters in the substation area: Based on the fixed proportion correlation coefficient r j , dynamic coefficient r j ', Discreteness C j 、Absolute value of error rate|e j | and the iterative error rate trend q j Compare with the corresponding preset value to determine the status of the electric energy meter of user j.

[0098] Specifically, when user j satisfies: fixed ratio correlation coefficient r j >0.9, dynamic coefficient r j '>0.85, Discreteness C j <0.3, absolute value of error rate|e j When |>10%, the iteration error rate trend q j <0, it is judged that the state of the electric energy meter of user j is abnormal; if it is not satisfied, it is judged that the state of the electric energy meter of user j is normal. In actual application, the preset values ​​corresponding to each evaluation parameter can be determined according to actual needs and experience.

[0099] The present invention forms a comprehensive multi-point composite model by combining multiple point composite models such as correlation, energy conservation and verification sub-process, and combines dynamic optimization strategy, providing a more comprehensive and accurate method for evaluating the status of the electric energy meter, more accurately evaluating the actual status of the electric energy meter, reducing misjudgment, and improving the adaptability and flexibility of the system. The prior art usually relies on a single model or simple threshold detection, while the present invention realizes multi-dimensional analysis of the status of the electric energy meter through the integration of multiple models.

[0100] The present invention can adapt according to the characteristics of different area data through the same optimization strategy. Whether it is a fixed ratio model or a dynamic interval model, it can optimize the data characteristics of different area electric energy meters, providing a highly flexible and adaptable solution. The existing technology usually relies on general area data analysis and optimization, and lacks consideration of small-scale areas and other situations. The present invention can better cope with various complex area electric energy meter status evaluation scenarios through an adaptive optimization strategy, thereby improving the adaptability and intelligence level of the overall system.

[0101] Traditional technologies usually rely on regular inspections and post-maintenance at fixed intervals, or use a single model for direct judgment. This method often leads to large errors in the evaluation of the status of the electric energy meter due to inaccurate predictions. The present invention uses a multi-point composite model that combines multiple data analysis techniques to fully mine data features, accurately evaluate the status of the electric energy meter, and greatly improve the accuracy of status evaluation.

[0102] The fixed mode of the traditional method has poor adaptability to electric energy meters of different types and states, while the multi-point composite model and intelligent optimization strategy of the present invention enable the system to adapt to electric energy meters of various types and states, thereby improving the adaptability and flexibility of the system.

[0103] like Figure 2 The figure is a schematic diagram of the structure of an electronic device for implementing the method for evaluating the operation error of the electric energy meter in the substation according to the present invention.

[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 executable on the processor 10, such as an area electric energy meter operation error evaluation program.

[0105] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing the error evaluation program of the electric energy meter in the substation area, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0106] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the error evaluation program of the power meter operation in the substation, but also be used to temporarily store data that has been output or is to be output.

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

[0108] The communication interface 13 is used for communication between the above-mentioned 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, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0109] Figure 2 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 2The structures shown do not constitute a limitation on the electronic device, and it 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 powering 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 evaluation program for the operation error of the power meter in the district 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 district;

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

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

[0117] When T<J, the ridge regression algorithm improved by the least squares method is used for solving to obtain the error rate e j as the iterative error rate,

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

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

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

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

[0122] S5. Calculate the iteration error rate trend q j , use the data of T and T-1, substitute into the energy conservation equations to calculate;

[0123] S6, review sub-process, according to the user's power P j and error rate e j , calculate the restored power Θ j , calculate the dispersion C of the restored charge j ;

[0124] S7. Evaluation of the operating status of low-voltage user electric energy meters in the substation area: Based on the fixed proportion correlation coefficient r j , dynamic coefficient r j ', Discreteness C j 、Absolute value of error rate|e j | and the iterative error rate trend q j Compare with the corresponding preset value to determine the status of the electric energy meter of user j.

[0125] Specifically, the specific implementation method of the processor 10 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0126] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium. Computer-readable storage media can be volatile or non-volatile. For example, computer-readable media can include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0127] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can achieve:

[0128] S1, data acquisition and processing;

[0129] S2, constructing a group of electric energy conservation equations between the total meter and each user meter in the substation area;

[0130] S3. Use the least square method with different strategies to solve the error rate e of each user according to the amount of data in the station area. j ,

[0131] When T ≥ J, the ordinary least squares method is used to estimate the regression coefficient by minimizing the residual sum of squares to obtain the error rate e jAs the 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 the iterative error rate,

[0133] where T is the amount of power data and J is the total number of users in the substation 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 power consumption and the line loss rate of the substation 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 power consumption and the line loss rate of the substation area j ';

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

[0138] S6. Recheck the sub - process. According to the user 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 substation 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 may 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 may 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] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0143] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, 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, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0145] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0146] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0147] In addition, 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 stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the operation error of electric energy meters in a substation, characterized in that: The following steps are involved: S1, data acquisition and processing; S2, constructing a group of electric energy conservation equations between the total meter and each user meter in the substation area; S3. Use the least square method with different strategies to solve the error rate e of each user according to the amount of data in the station area. j , When T ≥ J, the ordinary least squares method is used to estimate the regression coefficient by minimizing the residual sum of squares to obtain the error rate e j As a constant error rate, 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, Where T is the amount of electric energy data, and J is the total number of users in the area; S4. Construct a correlation algorithm model, including: Fixed ratio correlation algorithm model: Set a fixed ratio to construct a fixed ratio correlation coefficient r between user power consumption and area line loss rate j , Dynamic coefficient algorithm model: set the rolling date number as the dynamic calculation window to construct the dynamic coefficient r of user power consumption and area line loss rate j '; S5. Calculate the iteration error rate trend q j , use the data of T and T-1, substitute into the energy conservation equations to calculate; S6, review sub-process, according to the user's power P j and error rate e j , calculate the restored power Θ j , calculate the dispersion C of the restored charge j ; S7. Evaluation of the operating status of low-voltage user electric energy meters in the substation area: Based on the fixed proportion correlation coefficient r j , dynamic coefficient r j ', Discreteness C j 、Absolute value of error rate|e j | and the iterative error rate trend q j Compare with the corresponding preset value to determine the status of the electric energy meter of user j.

2. According to the method for evaluating the operation error of electric energy meters in a substation area as described in claim 1, it is characterized in that: In S1, read the amount of power data T for more than M consecutive days from the total meter of the area and the power meter of each user, including archive and daily power data, and calculate the statistical line loss rate P of the area on the tth day xsl : P gr The total power supply to the area on the tth day is P j is the electricity consumption of user j in the substation on the tth day.

3. According to the method for evaluating the operation error of electric energy meters in a substation area as described in claim 2, it is characterized in that: In S1, the acquired data is preprocessed. S101, eliminating uncountable areas: when the total number of days T of area data meets the first preset condition, the area is marked as an uncountable area, and the electric energy meters of each user in the area are marked as observation meters, and do not participate in the subsequent electric energy meter status evaluation; S102, remove noise data: when the statistical line loss rate P of the substation area xsl When the second preset condition is met, the data of the day is marked as noise data, and the data of the day is removed as the total number of days T after data preprocessing.

4. According to the method for evaluating the operation error of electric energy meters in a substation area as described in claim 3, it is characterized in that: In S1, the pre-processed data is weighted averaged to calculate the weighted average power The calculation period is Q.

5. According to the method for evaluating the operation error of electric energy meters in a substation area as described in claim 4, it is characterized in that: In S2, the data of the area for T days is weighted averaged to obtain the area supply power sequence Electricity consumption sequence of users in the area in and Represent the power supply to the substation and the weighted average power of user j in the T / Q period, respectively. Then the power conservation equations are: where e j is the error rate of the electric energy meter of user j, and e0 is the variable loss and fixed loss of the substation, which can be estimated according to the theoretical line loss formula of the power grid: Among them, A xl is the theoretical line loss of the line, N is the low-voltage outlet grid structure constant of the distribution transformer, K is the characteristic coefficient of the line load curve, I av is the average operating current of the line, R dz is the equivalent resistance of the line; I av Average operating current of the line: or Among them, U av Average operating voltage of the line, U av ≈U e =0.38, A Pg and A Qg They are respectively used to collect active power supply and reactive power supply at the head end of the line, and load power factor for the cosθ low-voltage line; Line load curve characteristic coefficient K: Among them, I i To collect hourly current; R dz The equivalent resistance of the line is as follows: Among them, R is the conductor resistance in the station area.

6. According to the method for evaluating the operation error of electric energy meters in a substation area as described in claim 5, it is characterized in that: In S3, the constant error rate calculation process is as follows: y=Xβ+∈ Where y is the T×1 observation vector; X is the T×J design matrix, where each column represents a predictor variable; β is the J×1 regression coefficient vector; ∈ is the T×1 deviation vector; The objective function of the ordinary least squares method is to minimize the residual sum of squares, that is: RSS=||y-Xβ|| 2 By minimizing the objective function, we can get the estimated value of the regression coefficient: Among them, X T X is the inner product of the design matrix, then (X T X) -1 is the inverse matrix of the matrix; X T y is the internal accumulation of the design matrix and observations; The iterative error rate objective function is: RSS=||y-Xβ|| 2 +λ||β|| 2 Get estimates of the regression coefficients: Among them, λ is the regularization parameter.

7. A method for evaluating the operation error of an electric energy meter in a substation according to claim 6, characterized in that: In S4, the fixed ratio Pearson correlation coefficient between user power consumption and area line loss rate is calculated as follows: Among them, r j is the fixed ratio correlation coefficient of user j; P j,l is the power consumption of user j on day l, is the average power consumption of user j in L days; P xsl,l is the line loss rate of the station area on the first day, is the average line loss rate of the substation area within L days; The dynamic coefficient of user power consumption and area line loss rate, assuming that the dynamic calculation window starts at i, then the window range is [i,i+O-1], and the calculation formula for a single window is as follows: Among them, r j ' is the dynamic coefficient of user j, P j,l is the power consumption of user j on day l in the window, is the average power consumption of user j in the window; P xsl,l is the line loss rate of the area on the first day within the window, is the average area line loss rate within the window; T-O+1 is the total number of windows.

8. A method for evaluating the operation error of an electric energy meter in a substation according to claim 7, characterized in that: In S6, Calculate the user's restored power Θ j : Then the restored power of user j in T days is Θ j,T ={Θ j,1 ,Θ j,2 ,...,Θ j,T }; Calculate the dispersion C of the restored charge j : in, is the average amount of restored electricity consumed by user j in T days.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the substation electric energy meter operation error evaluation method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: It includes a data storage area and a program storage area, 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, the method for evaluating the operation error of an electric energy meter in an area as described in any one of claims 1 to 8 is implemented.

Images