Electric energy meter misalignment checking method and system based on electric energy pulse acquisition and medium

Abstract

The invention discloses an electric energy meter misalignment checking method and system based on electric energy pulse acquisition, and a medium. The method comprises the following steps: respectively acquiring a first pulse number corresponding to a general meter and second pulse numbers corresponding to a plurality of sub-meters in a meter box; respectively smoothing the first pulse number and each second pulse number to obtain a first average electric energy pulse number corresponding to a general meter and a second average electric energy pulse number corresponding to each sub-meter; inputting the first average electric energy pulse number and each second average electric energy pulse number into a preset misalignment analysis model for solving to respectively obtain a line loss rate, a fixed loss and a plurality of sub-meter errors; and determining a misalignment sub-meter needing to be calibrated according to the plurality of sub-meter errors, and checking the misalignment sub-meter based on the line loss rate and the fixed loss. According to the invention, the accuracy and efficiency of electric energy meter misalignment checking can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power metering, and in particular to a method, system and medium for correcting electric energy meter inaccuracy based on electric energy pulse acquisition. Background Art

[0002] Electricity metering plays a crucial role in power production, marketing, and the safe operation of power grids. Its accuracy directly impacts the fairness, impartiality, and accuracy of electricity trade settlements, and thus influences the trust between power companies and users. However, due to a variety of factors, such as aging components, quality issues with spare parts, environmental impact, and improper use, electricity meters are often prone to inaccurate measurements. Inaccurate meters not only cause economic losses to power companies and users, but can also lead to disputes and pose a potential threat to the safe and stable operation of the power grid. Therefore, timely and accurate calibration of electricity meters is crucial for ensuring the accuracy and fairness of electricity trade settlements, maintaining order in the power market, and ensuring the safe and reliable operation of the power grid.

[0003] In the existing technology, there are two main methods for calibrating electricity meter inaccuracies. The first is to manually carry calibration equipment to the site to calibrate smart meters. Although this method is highly accurate, it is time-consuming, inefficient, and has a long cycle, making it difficult to conduct a comprehensive survey of large-scale electricity meters. The second method is to conduct error assessment through data analysis based on substation data. According to the principle of energy conservation in the substation, the electricity consumption data collected by the metering automation system is used to evaluate the smart meters, and the error changes of the smart meters are tracked in real time based on the newly added data. However, this method requires sufficient substation electricity consumption data, and the accuracy of the error assessment results is relatively poor. Therefore, it is crucial to develop a method that can improve the accuracy and efficiency of electricity meter inaccuracy calibration. Summary of the Invention

[0004] The present application provides an electric energy meter misalignment calibration method, system and medium based on electric energy pulse acquisition to improve the accuracy and efficiency of electric energy meter misalignment calibration.

[0005] In a first aspect, the present application provides a method for calibrating an electric energy meter based on electric energy pulse acquisition, comprising:

[0006] respectively collecting the first pulse number corresponding to the main meter in the meter box and the second pulse number corresponding to several sub-meters;

[0007] Smoothing the first pulse number and each second pulse number respectively to obtain a first average electric energy pulse number corresponding to the total meter and a second average electric energy pulse number corresponding to each sub-meter;

[0008] Inputting the first average number of electric energy pulses and each of the second average numbers of electric energy pulses into a preset misalignment analysis model for solution to obtain a line loss rate, a fixed loss, and a plurality of sub-meter errors, wherein the misalignment analysis model is constructed based on a mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss, and the initial fixed loss;

[0009] An inaccurate sub-meter that needs to be calibrated is determined according to errors of a plurality of sub-meters, and the inaccurate sub-meter is checked based on the line loss rate and the fixed loss.

[0010] The embodiment of the present application provides a complete data basis for subsequent analysis by collecting the pulse numbers of the total meter and each sub-meter, thereby avoiding inaccurate analysis caused by missing data; through smoothing, the noise and interference in the pulse number can be reduced, the impact of data fluctuations on subsequent analysis can be reduced, and the data can be made more stable and reliable; by solving the inaccuracy analysis model, key parameters such as line loss rate, fixed loss and sub-meter error can be obtained respectively, thereby achieving quantitative analysis of the metering error of the electric energy meter and providing a specific basis for further calibration and adjustment; based on the comparison of the sub-meter error with the preset error threshold, the inaccurate sub-meter that needs to be calibrated can be accurately located, avoiding unnecessary calibration of all sub-meters, thereby improving the pertinence and efficiency of the calibration work; by calibrating the inaccurate sub-meter based on the line loss rate and fixed loss, the true metering value of the sub-meter can be determined more accurately, the metering error of the sub-meter can be corrected, and the accuracy of electric energy metering can be improved. Compared with the prior art, the present application can improve the accuracy and efficiency of the inaccuracy calibration of electric energy meters.

[0011] Furthermore, the expression of the misalignment analysis model is specifically:

[0012]

[0013] Where, is the second average energy pulse number of each sub-meter after multi-cycle weighted average processing, c j is the pulse constant of each sub-meter, where j = 1, 2, ..., p; N' is the first average electric energy pulse number of the total meter in the meter box after multi-cycle weighted averaging, and c' is the pulse constant of the total meter in the meter box; is the measurement value of the same electric energy meter in different time series; e j is the error of each sub-table; e y is the line loss; e0 is the fixed loss; n is the total number of equations.

[0014] Furthermore, the first average number of electric energy pulses and each second average number of electric energy pulses are input into a preset misalignment analysis model for solution, specifically:

[0015] Transform the misalignment analysis model to obtain a target misalignment analysis model, and determine the magnitude relationship between the data of n measurement periods and P + 2 unknowns, where P is the number of sub - meters in the meter box;

[0016] If n = P + 2, the target misalignment analysis model is a positive - definite equation, and the linear equation can be directly solved;

[0017] If n > P + 2, the target misalignment analysis model is an over - determined system of equations, and the least - squares method or other optimization methods are used for solution;

[0018] If n < P + 2, the target misalignment analysis model is an indefinite equation, and methods such as ridge regression are used for solution.

[0019] Furthermore, the misaligned sub - meters are calibrated based on the line loss rate and the fixed loss, specifically:

[0020] Obtain the first measurement value of the misaligned sub - meter, the second measurement values of other sub - meters, and the total power supply of the main meter respectively;

[0021] Based on the sub - meter error and the first measurement value, determine the true measurement value of the misaligned sub - meter;

[0022] Based on the true measurement value and the second measurement values, determine the total power consumption of all sub - meters in the meter box, and verify energy conservation based on the total power consumption, the total power supply of the main meter, the line loss rate, and the fixed loss;

[0023] If the verification is passed, the misaligned sub - meter is calibrated based on the true measurement value.

[0024] Calibrating the misaligned sub - meters based on the line loss rate and the fixed loss can more accurately determine the true measurement value of the sub - meters, realize the correction of the measurement error of the sub - meters, and improve the accuracy of electric energy measurement.​​​​​​​​​​​The first preprocessing result and each second preprocessing result are smoothed by a multi-cycle weighted average algorithm to obtain a first average number of electric energy pulses corresponding to the total meter and a second average number of electric energy pulses corresponding to each sub-meter.

[0029] In this way, through smoothing processing, the noise and interference in the pulse number can be reduced, the impact of data fluctuations on subsequent analysis can be reduced, and the data can be made more stable and reliable.

[0030] Furthermore, the calculation formula for smoothing the first preprocessing result and each second preprocessing result using a multi-period weighted average algorithm is specifically:

[0031]

[0032] In the formula, in the formula, is the first average number of electric energy pulses after the first pre-processing result of the i-th cycle total table is smoothed over multiple cycles, w k is the weight of the kth period, N′ i+k is the first preprocessing result corresponding to the total table of the i+kth period, M is the cumulative number of periods, is the second average number of electric energy pulses after the second pre-processing results of each sub-table in the i-th period are smoothed over multiple periods, is the second preprocessing result corresponding to each sub-table in the i+k-th period.

[0033] In a second aspect, the present application provides an electric energy meter misalignment calibration device based on electric energy pulse acquisition, comprising: an acquisition module, a processing module, a calculation module, and a calibration module;

[0034] The acquisition module is used to respectively acquire the first pulse number corresponding to the main meter and the second pulse numbers corresponding to the plurality of sub-meters in the meter box;

[0035] The processing module is used to perform smoothing processing on the first pulse number and each second pulse number respectively to obtain a first average electric energy pulse number corresponding to the total meter and a second average electric energy pulse number corresponding to each sub-meter;

[0036] The calculation module is configured to input the first average number of electric energy pulses and each second average number of electric energy pulses into a preset misalignment analysis model for solution to obtain a line loss rate, a fixed loss, and a plurality of sub-meter errors, wherein the misalignment analysis model is constructed based on a mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss, and the initial fixed loss;

[0037] The calibration module is used to determine the inaccurate sub-meter that needs to be calibrated according to the errors of the multiple sub-meters, and calibrate the inaccurate sub-meter based on the line loss rate and the fixed loss.

[0038] The embodiment of the present application provides a complete data basis for subsequent analysis by collecting the pulse numbers of the total meter and each sub-meter, thereby avoiding inaccurate analysis caused by missing data; through smoothing, the noise and interference in the pulse number can be reduced, the impact of data fluctuations on subsequent analysis can be reduced, and the data can be made more stable and reliable; by solving the inaccuracy analysis model, key parameters such as line loss rate, fixed loss and sub-meter error can be obtained respectively, thereby achieving quantitative analysis of the metering error of the electric energy meter and providing a specific basis for further calibration and adjustment; based on the comparison of the sub-meter error with the preset error threshold, the inaccurate sub-meter that needs to be calibrated can be accurately located, avoiding unnecessary calibration of all sub-meters, thereby improving the pertinence and efficiency of the calibration work; by calibrating the inaccurate sub-meter based on the line loss rate and fixed loss, the true metering value of the sub-meter can be determined more accurately, the metering error of the sub-meter can be corrected, and the accuracy of electric energy metering can be improved. Compared with the prior art, the present application can improve the accuracy and efficiency of the inaccuracy calibration of electric energy meters.

[0039] Furthermore, the verification module includes: an acquisition unit, a determination unit, a verification unit and a verification unit;

[0040] The acquisition unit is used to respectively acquire the first measurement value of the inaccurate sub-meter, the second measurement values of other sub-meters, and the power supply of the main meter;

[0041] The determining unit is configured to determine a true measurement value of the inaccurate sub-meter based on the sub-meter error and the first measurement value;

[0042] The verification unit is configured to determine the total power consumption of all sub-meters in the meter box based on the real measurement value and the second measurement value, and perform energy conservation verification based on the total power consumption, the total meter power supply, the line loss rate, and the fixed loss;

[0043] The calibration unit is configured to calibrate the inaccuracy meter based on the true measurement value if the verification is passed.

[0044] Furthermore, the processing module includes: a first processing unit, a second processing unit and a third processing unit;

[0045] The first processing unit is configured to perform time synchronization on the first pulse number and each of the second pulse numbers to obtain first pulse data corresponding to the total table and second pulse data corresponding to each sub-table;

[0046] The second processing unit is configured to perform data preprocessing on the first pulse data and each of the second pulse data to obtain a first preprocessing result corresponding to the total table and a second preprocessing result corresponding to each sub-table;

[0047] The third processing unit is used to smooth the first preprocessing result and each second preprocessing result through a multi-cycle weighted average algorithm to obtain a first average number of electric energy pulses corresponding to the total meter and a second average number of electric energy pulses corresponding to each sub-meter.

[0048] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for correcting the misalignment of an electric energy meter based on electric energy pulse acquisition as described in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is the topological structure diagram of the meter box area provided by this application;

[0050] Figure 2 This is a flow chart of an embodiment of a method for correcting inaccuracy of an electric energy meter based on electric energy pulse acquisition provided by the present application;

[0051] Figure 3 This application Figure 2 Flow diagram of step S102;

[0052] Figure 4 It is a structural diagram of an embodiment of an electric energy meter inaccuracy calibration system based on electric energy pulse acquisition provided by the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0055] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0057] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0058] Electricity metering is crucial for power production, marketing, and grid security. Its accuracy directly impacts the fairness and impartiality of electricity trade settlements, and thus undermines trust between power companies and users. However, due to factors such as component aging, quality issues, environmental impacts, and improper use, electricity meters may suffer from meter inaccuracies, resulting in economic losses and disputes for all parties, and even threatening grid security. Therefore, timely and accurate inaccuracy verification is crucial for ensuring the accuracy of trade settlements and maintaining order in the electricity market. Two main existing verification methods exist: manual on-site verification, while highly accurate, is inefficient, time-consuming, and difficult to fully survey. The other involves error assessment based on substation data, utilizing the principle of energy conservation and electricity consumption data for analysis. However, this requires large amounts of data and results with limited accuracy. Therefore, research on methods to improve the accuracy and efficiency of electricity meter inaccuracy verification is crucial.

[0059] Next, the nouns involved in this application are analyzed:

[0060] Inaccurate electricity meter: The electricity meter may measure inaccurately due to aging of its own components, quality problems of spare parts, the influence of the electricity meter's operating environment, improper use, etc.

[0061] Electricity meter Bluetooth: A Bluetooth communication module is added to the traditional smart electricity meter, which is mainly used for short-range wireless transmission, enabling mutual communication between several nodes with high communication efficiency.

[0062] Based on this, the embodiments of the present application provide a method, system and medium for calibrating the misalignment of an electric energy meter based on electric energy pulse acquisition, which can improve the accuracy and efficiency of the misalignment calibration of the electric energy meter.

[0063] The embodiments of the present application provide a method, system and medium for correcting the inaccuracy of an electric energy meter based on electric energy pulse acquisition, which are specifically illustrated through the following embodiments. First, the method for correcting the inaccuracy of an electric energy meter based on electric energy pulse acquisition in the embodiments of the present application is described.

[0064] The electric energy meter misalignment calibration method based on electric energy pulse acquisition provided in the embodiment of the present application relates to the field of electric power metering. The electric energy meter misalignment calibration method based on electric energy pulse acquisition provided in the embodiment of the present application can be applied in a terminal, can also be applied in a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the electric energy meter misalignment calibration method based on electric energy pulse acquisition, etc., but is not limited to the above forms.

[0065] The present application can also be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] Before describing the electric energy meter misalignment calibration method based on electric energy pulse acquisition in the embodiment of the present application, the meter box topology structure is first introduced to facilitate subsequent understanding. The meter box area topology structure diagram is as follows: Figure 1 As shown, the meter box topology consists of a total meter M 总 and several sub-tables (M1~M i ) is composed of a main meter box and user sub-meters, forming a simple tree topology. The energy transmission relationship between the meter boxes is that electricity is transmitted to each user via the main meter box, and user electricity consumption data is measured by each household's sub-meter. The meter box's electricity meter energy data is obtained by multiple upper-level devices equipped with Bluetooth modules through wireless connection, synchronously acquiring the energy pulses output by the energy meter. The energy pulses of each channel are counted to obtain the change in the energy meter's energy indication within a specific period. This meter box topology and power supply and consumption relationship are the physical basis for the subsequent misalignment analysis model.

[0067] Example 1

[0068] Please refer to Figure 2 , Figure 2 This is a flow chart of an embodiment of a method for calibrating an electric energy meter based on electric energy pulse acquisition provided by the present application, comprising steps S101 to S104;

[0069] Step S101, respectively collecting the first pulse number corresponding to the main meter and the second pulse numbers corresponding to the plurality of sub-meters in the meter box;

[0070] In some embodiments, the first pulse number corresponding to the master meter in the meter box and the second pulse number corresponding to several sub-meters are collected separately, specifically: first, the built-in Bluetooth module of the master meter in the meter box establishes wireless communication with each sub-meter, wherein the master meter in the meter box is connected as the Bluetooth communication master device, and each user's electric energy meter is connected as a Bluetooth communication slave device. The master meter in the meter box is responsible for allocating time slots, and each user's electric energy meter transmits data within the specified time slot. The communication between the master meter in the meter box and the user's electric energy meter is full-duplex, that is, the master meter in the meter box and the user's electric energy meter can send and receive data in different time slots. Secondly, the master device uses a high-precision clock as a synchronization clock source, and the slave device synchronizes its internal clock through communication with the master device. When the slave device joins the Bluetooth network, it obtains the clock information of the master device by receiving the synchronization packet (such as the FHS packet) sent by the master device, and adjusts its own clock to keep synchronized with the master device. This enables the master meter to collect its own pulse number (that is, the first pulse number, denoted as N′) in real time, and receive the second pulse number (denoted as N) output by each sub-meter in real time via Bluetooth. j , j=1,2,...,p).

[0071] It should be noted that the built-in Bluetooth module of the meter box master meter establishes wireless communication with each sub-meter. Specifically, before joining the Bluetooth network, the slave device will scan the available master device channel. Once the master device is found, the slave device sends a connection request. After receiving the connection request, the master device assigns a unique slave device address (BD_ADDR) and notifies the slave device of its position in the time slot and transmission plan. The slave device obtains the clock information and time slot allocation plan of the master device by receiving the synchronization packet sent by the master device to complete the initial synchronization.

[0072] It should be noted that the master device allocates transmit and receive time slots to each slave device based on a preset time slot scheduling algorithm. This scheduling algorithm can be fixed or dynamically adjusted. Furthermore, within the allocated time slots, the master and slave devices send and receive data packets in a predetermined order. Each data packet includes a time slot number that identifies the current time slot. After receiving a data packet from the master, the slave device sends an acknowledgment packet in the next time slot to ensure reliable data transmission. Furthermore, the master device dynamically adjusts time slot allocation based on network load and communication requirements to prioritize the transmission of critical data frames.

[0073] It should be noted that the master device regularly sends synchronization update packets, and the slave device adjusts its own clock according to these update packets to ensure long-term synchronization. At the same time, the slave device monitors the deviation between its own clock and the master device clock and compensates for it to reduce the cumulative error. In addition, the master and slave devices maintain a connection through regular heartbeat packets (such as POLL and NULL data packets) to ensure that synchronization is not lost.

[0074] It should be noted that Bluetooth uses Adaptive Frequency Hopping (AFH) technology to avoid interference with other wireless devices. The master device dynamically adjusts the frequency to ensure stable communication. At the same time, the master and slave devices measure and compensate for transmission delays to ensure that data packets arrive at the correct time.

[0075] It should be noted that when a slave device leaves the network, the master device releases its allocated time slot resources. When the slave device reconnects to the network, it needs to re-synchronize to obtain a new time slot allocation.

[0076] Through the above mechanism, the Bluetooth time slot synchronization mechanism can ensure time coordination between the master and slave devices, achieving efficient and reliable data synchronization transmission. This mechanism is particularly important in multi-device communication scenarios, effectively avoiding data conflicts and loss, and ensuring the transmission of critical data frames.

[0077] It should be noted that since the pulse frequency is proportional to the change in electrical energy, the pulse count value directly reflects the increase or decrease in electrical energy.

[0078] It should be noted that the first pulse number N' is the pulse data of the total meter, which is used as the reference value of the total power supply of the meter box, and the second pulse number N j It is the pulse data of each sub-meter in the meter box, which can be used as a reference value for the electricity consumption of each sub-meter.

[0079] Step S102, smoothing the first pulse number and each second pulse number respectively to obtain a first average electric energy pulse number corresponding to the total meter and a second average electric energy pulse number corresponding to each sub-meter;

[0080] In some embodiments, please refer to Figure 3, step S102 may include but is not limited to steps S301 to S303:

[0081] Step S301, performing time synchronization on the first pulse number and each second pulse number to obtain first pulse data corresponding to the total table and second pulse data corresponding to each sub-table;

[0082] In some embodiments, the first pulse number N′ of the total meter and the second pulse number N′ of the sub-meter are collected by using the synchronization mechanism of Bluetooth communication. j Perform time synchronization to ensure data periodicity and consistency.

[0083] It should be noted that in Bluetooth communication, the master device (master meter) and the slave device (sub-meter) synchronize their clocks through synchronization packets (such as FHS packets), and the slave device adjusts its own clock to be consistent with the master device.

[0084] Step S302, performing data preprocessing on the first pulse data and each of the second pulse data to obtain a first preprocessing result corresponding to the total table and a second preprocessing result corresponding to each sub-table;

[0085] In some embodiments, the data preprocessing may include, but is not limited to, filtering, denoising, and calibration to improve the accuracy of the data.

[0086] In some embodiments, first, a digital filtering algorithm (such as a low-pass filter) is used to remove the first pulse data N′ and each second pulse data N j Then, a statistical method is used to identify and remove the first pulse data N' and each of the second pulse data N j The outliers in the data can be calculated by calculating the mean and standard deviation of the data, and the data points that exceed a certain range (such as the mean ± 3 times the standard deviation) can be regarded as noise and removed; finally, according to the known calibration data or standard signal source, the first pulse data N′ and each second pulse data N j Adjustments are made to eliminate system errors, thereby obtaining a first preprocessing result corresponding to the total table and a second preprocessing result corresponding to each sub-table.

[0087] Step S303 , smoothing the first preprocessing result and each second preprocessing result by a multi-cycle weighted average algorithm to obtain a first average number of electric energy pulses corresponding to the total meter and a second average number of electric energy pulses corresponding to each sub-meter.

[0088] In some embodiments, a time window is set to perform weighted averaging on the first preprocessing result and each of the second preprocessing results, wherein, in the weighted averaging process, a weight strategy needs to be set, wherein the weight can be equal weight or exponential decay weight, so as to obtain the first average electric energy pulse number corresponding to the total table after weighted averaging. The second average energy pulse number corresponding to each sub-meter For example, if the current time series is 1s and the number of electric energy pulses fluctuates greatly, resulting in unstable error model solution, a 10S time window (M=10) can be used for smoothing and weighted calculation of 1 pulse data.

[0089] In some embodiments, the calculation formula for smoothing the first preprocessing result and each second preprocessing result using a multi-period weighted average algorithm is specifically:

[0090]

[0091] In the formula, in the formula, is the first average number of electric energy pulses after the first pre-processing result of the i-th cycle total table is smoothed over multiple cycles, w k is the weight of the kth period, N′ i+k is the first preprocessing result corresponding to the total table of the i+kth period, M is the cumulative number of periods, is the second average number of electric energy pulses after the second pre-processing results of each sub-table in the i-th period are smoothed over multiple periods, is the second preprocessing result corresponding to each sub-table in the i+k-th period.

[0092] In this way, through smoothing processing, the noise and interference in the pulse number can be reduced, the impact of data fluctuations on subsequent analysis can be reduced, and the data can be made more stable and reliable.

[0093] Step S103: Inputting the first average number of electric energy pulses and each of the second average numbers of electric energy pulses into a preset misalignment analysis model for solution to obtain a line loss rate, a fixed loss, and a plurality of sub-meter errors, wherein the misalignment analysis model is constructed based on a mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss, and the initial fixed loss;

[0094] In some embodiments, since the meter box topology and the power supply and consumption relationship are the physical basis of the smart meter measurement error evaluation model, based on the topology and the principle of conservation of energy, a mathematical relationship between the meter box total meter measurement data, meter box line loss, meter box fixed loss, sub-meter metering data and meter error can be constructed, that is, an inaccuracy analysis model.

[0095] In some embodiments, the specific structure of the misalignment analysis model is as follows: total meter box electricity consumption = sum of each user's electricity consumption + line loss + meter box fixed loss, where the total meter box electricity consumption and each user's electricity consumption are information measured by the smart meter, line loss is the consumption on the transmission line during power transmission, mainly caused by line impedance, and fixed loss is the energy loss of the communication equipment, data collection terminal, and smart meter in the meter box circuit. The power of the equipment is relatively stable and is therefore considered fixed loss in the model. Therefore, the specific process of constructing the misalignment analysis model is as follows: first, the total meter box electricity consumption and each user's electricity consumption are represented by the smart meter measurement data and its error: Where P is the total number of sub-meters in the meter box, y(i) is the total power supply of the power supply meter in metering period i, φ j (i) is the measurement value of meter j in measurement period i, e j is the estimated relative error of table j, e y is the line loss rate of the meter box, e0 is the fixed loss of the meter box; and the meter box master meter is used as the calibration device to calibrate each sub-meter in the meter box. The measurement value y′(i) of the meter box master meter is used to approximate the total power supply y(i) of the meter box, and we can get: Subsequently, based on the original equation, the model introduces the concept of multi-period weighted averaging for sub-meter errors, line loss rates, and fixed loss rates. The so-called weights are the corresponding meter values of the user meter or the total meter. When establishing the equation, it is assumed that the fluctuation range of the weighted average value in a certain period is very limited or even remains unchanged. After taking the weighted average of the error, line loss rate, and fixed loss, the adverse effects of these quantities caused by noise, time difference, etc. will be reduced. At the same time, for residential users, since their electricity usage habits remain basically stable within a certain period, the weighted average value of their electricity meter errors can be considered to remain unchanged. Specifically, taking M periods for weighted averaging and then transforming it can obtain the relationship:

[0096]

[0097] Where, It represents the average value of the total power supply of M metering periods starting from metering period i, represents the average value of the measurement value of the metering point j in the M measurement cycles starting from the measurement cycle i, ε0 represents the average fixed loss of the meter box in M measurement cycles, ε y represents the weighted average line loss rate of the meter box M metering cycles, ε j It represents the weighted average of the estimated relative errors of M measurement cycles at the metering point j. Therefore, when the meter box accumulates data for n cycles, a system of equations consisting of n equations can be obtained, specifically:

[0098]

[0099] In this system of equations, y′(n) is the measurement value of different time series in the total table and is a known quantity, φ p (1)—φ p (n) is the measurement value of the same sub-meter in different time series, which is a known quantity. When the number of measurement cycle data n is greater than or equal to P+2, the unknown error e of each user's electricity meter can be solved. j , line loss rate e y and fixed loss e0, thereby obtaining the operating error value of each user's electric energy meter in the meter box. Then, the cumulative change in the number of pulses obtained by the master meter through the Bluetooth channel to each user's electric energy meter is used to approximately represent the change in the electric energy of each user's electric energy meter. At the same time, the change in the number of pulses of the master meter in the meter box is used to approximately represent the change in the electric energy of the master meter in the meter box. Combining the pulse constants of the master meter in the meter box and each user's sub-meter, the above equations can be rewritten to obtain: Where, is the measurement value of the same electric energy meter in different time series, c j 、N j , c′, N′ are known quantities, c j is the pulse constant of each submeter, N j is the number of electric energy pulses of each sub-meter, where j = 1, 2, ..., p, c' is the pulse constant of the total meter in the meter box, and N' is the number of electric energy pulses of the total meter in the meter box.

[0100] It should be noted that the above formula includes n=N equations. When the number is greater than or equal to p+2, the unknown error e of each user's electricity meter can be solved. j , line loss rate e y and fixed loss e0, thereby obtaining the error values of the meter box.

[0101] It should be noted that the pulse constant represents the number of pulses output by the energy meter for every kilowatt-hour (kWh) of electricity. The pulse constant of each energy meter is fixed, and the unit is pulses / kWh. For example, K=3200 pulses / kWh means that for every kilowatt-hour of electricity consumed, the energy meter outputs a total of 3200 pulses. The number of energy pulses (that is, the first pulse number and the second pulse number mentioned) are the total number of pulses actually measured, without units, and directly reflect the cumulative number of pulses output by the energy meter within a certain period of time. The meter box measurement value is the consumed energy. For example, φ in the above equation j (i), which is the measurement value of meter j in metering period i. The relationship among the pulse constant, the number of energy pulses, and the meter box measurement value is: the number of energy pulses (i.e. the first pulse number and the second pulse number) = pulse constant × consumed energy.

[0102] It should be noted that according to multiple accumulated periodic pulse data, the above equations can be transformed into an expression of the misalignment analysis model. By substituting the first average power pulse number corresponding to the master meter and the second average power pulse number corresponding to each sub-meter into the misalignment analysis model together, the error e j of the sub-meter, the line loss rate e y and the fixed loss e0 can be obtained by solving.

[0103] In some embodiments, the expression of the misalignment analysis model is specifically:

[0104]

[0105] In the formula, is the second average power pulse number of each sub-meter after multi-period weighted average processing, c j is the pulse constant of each sub-meter, where j = 1, 2,..., p; N' is the first average power pulse number of the master meter in the meter box after multi-period weighted average processing, and c' is the pulse constant of the master meter in the meter box; are the measurement values of the same watt-hour meter at different time series; e j is the error of each sub-meter, where j = 1, 2,..., p; e y is the line loss; e0 is the fixed loss; n is the total number of equations.

[0106] In some embodiments, the step of inputting the first average power pulse number and each second average power pulse number into a preset misalignment analysis model for solution includes: transforming the misalignment analysis model to obtain a target misalignment analysis model, and judging the magnitude relationship between n measurement period data and P + 2 unknowns, where P is the number of sub-meters in the meter box; if n = P + 2, the target misalignment analysis model is a positive definite equation and the linear equation can be directly solved; if n > P + 2, the target misalignment analysis model is an overdetermined system of equations and the least squares method or other optimization methods are used for solution; if n < P + 2, the target misalignment analysis model is an indefinite equation and methods such as ridge regression are used for solution. Specifically, first, transform the expression of the misalignment analysis model and write it in matrix form to obtain the target misalignment analysis model. The expression of the target misalignment analysis model is:

[0107]

[0108] In the formula,

[0109] ​In the formula, A is the electricity consumption matrix, x is the error vector, and b is the metering line loss power. In the target misalignment analysis model, the metering value of the total meter in the area (i.e., the first average power pulse number ) and the metering value of the user sub-meter (i.e., the second average power pulse number ) are known quantities. The error e of each user electricity meter j , the line loss rate e y and the fixed loss e0 are the unknowns of the equation. Assuming that the metering errors of each electricity meter remain relatively stable during the calculation period, comparing the accumulated data of n metering periods with P + 2 unknowns, the solution results are as follows: If n = P + 2, the target misalignment analysis model is a positive definite equation, and the linear equation can be directly solved; if n > P + 2, the target misalignment analysis model is an overdetermined system of equations, and the least squares method or other optimization methods are used to solve it; if n < P + 2, the target misalignment analysis model is an indefinite equation, and methods such as ridge regression are used to solve it.

[0110] It should be noted that in practice, the amount of collected data is generally greater than the number of user meters, that is, n > P + 2. At the same time, in order to reduce the large error generated by the unique solution obtained from n = P + 2 equations, the period n is increased so that n > P + 2, and then the least squares method or other methods are used to minimize the sum of the squared errors of the n-period equations to obtain the result. For the case of n < P + 2, since the number of unknowns is greater than the number of knowns, the obtained results are not unique, so it does not belong to our actual application scope;

[0111] In some embodiments, when n > P + 2, the system of equations is an overdetermined system of equations (the number of equations is more than the number of unknowns), and the least squares method needs to be used to fit the optimal solution. The specific process is as follows: First, for the overdetermined system of equations, the goal of solving the optimal solution is to minimize the residual sum, that is, the goal is to find the error vector x such that the difference between the model prediction value Ax and the actual observed value b is as small as possible. Here, it is to make the residual between them as small as possible. Among them, ‖.‖ represents the Euclidean norm of the vector (that is, taking the square root after summing the squares of each vector respectively), and the sum of squared residuals ‖Ax - b‖ 2 is a measure of the goodness of model fitting. The smaller it is, the better the fitting. For example: ‖Ax - b‖ 2 = 0 2 + 0 2 + 0 2 + (-1) 2 = 1; Then, expand the objective function and take the derivative: At the same time, in order to make the objective function minimum, the partial derivative of the objective function is made. Because the partial derivative is 0 when it is the minimum value, take the derivative with respect to x and set it to zero. Arrange the normal equation: 2A T Ax-2A T b=0,A T Ax=A T b, where A T is the transpose of matrix A, and we can get: x=(A T A) -1 A T b. This solution is the least square method, which is the only solution that minimizes the sum of squared residuals. It can be used to solve the relative error, line loss rate and fixed loss rate of each sub-meter.

[0112] It should be noted that when the deviation is large, the objective function will become larger and larger, so the minimum value is obtained when the partial derivative is 0.

[0113] It should be noted that the specific method of solving the misalignment analysis model can also be solved by existing technologies, and this application does not impose any limitation.

[0114] Step S104 , determining an inaccurate sub-meter that needs to be calibrated according to the errors of the multiple sub-meters, and calibrating the inaccurate sub-meter based on the line loss rate and the fixed loss.

[0115] In some embodiments, the inaccurate sub-meter that needs to be calibrated is determined based on the errors of several sub-meters. Specifically, the calculated sub-meter errors e j Compare with the allowable error limit (such as ±2%). If the error exceeds the allowable range, it means that the sub-meter is inaccurate and the actual measurement value needs to be corrected.

[0116] In some embodiments, the calibration of the inaccurate sub-meter based on the line loss rate and the fixed loss is specifically as follows: respectively obtaining the first metering value of the inaccurate sub-meter, the second metering values of other sub-meters, and the total meter power supply; determining the true metering value of the inaccurate sub-meter based on the sub-meter error and the first metering value; determining the total power consumption of all sub-meters in the meter box based on the true metering value and the second metering value, and performing energy conservation verification based on the total power consumption, the total meter power supply, the line loss rate, and the fixed loss; if the verification is passed, calibrating the inaccurate sub-meter based on the true metering value. Specifically, first, determine the first metering value E in combination with the pulse number and the pulse constant. err , the second measurement value E of other sub-meters j And the total meter power supply y'; then, based on the solved sub-meter error, correct the first meter value E err , E′ err =E err (1-e j ), and obtain the true measurement value E' of the inaccuracy meter err Then, based on the true measurement value E'err and the second measurement value E j , summarize the total electricity consumption Y, total electricity consumption Y = E' err +∑ j≠err E j , then substitute the total power consumption Y into the energy conservation equation to verify whether y′≈Y+e y y′+e0, if the deviation is within the allowable range (such as ±0.5%), the verification is passed; finally, when the verification is passed, the error of the misalignment meter is confirmed to be valid, and its measurement value is updated to E′ err , and trigger the electricity replenishment of the inaccurate meter. If the verification fails, the model parameters (e y is the meter box line loss rate, e0 is the meter box fixed loss) or check whether there are hidden errors in other sub-meters.

[0117] In this way, the inaccurate sub-meter is calibrated based on the line loss rate and fixed loss, which can more accurately determine the true measurement value of the sub-meter, correct the measurement error of the sub-meter, and improve the accuracy of electricity metering.

[0118] The embodiment of the present application provides a complete data basis for subsequent analysis by collecting the pulse numbers of the total meter and each sub-meter, thereby avoiding inaccurate analysis caused by missing data; through smoothing, the noise and interference in the pulse number can be reduced, the impact of data fluctuations on subsequent analysis can be reduced, and the data can be made more stable and reliable; by solving the inaccuracy analysis model, key parameters such as line loss rate, fixed loss and sub-meter error can be obtained respectively, thereby achieving quantitative analysis of the metering error of the electric energy meter and providing a specific basis for further calibration and adjustment; based on the comparison of the sub-meter error with the preset error threshold, the inaccurate sub-meter that needs to be calibrated can be accurately located, avoiding unnecessary calibration of all sub-meters, thereby improving the pertinence and efficiency of the calibration work; by calibrating the inaccurate sub-meter based on the line loss rate and fixed loss, the true metering value of the sub-meter can be determined more accurately, the metering error of the sub-meter can be corrected, and the accuracy of electric energy metering can be improved. Compared with the prior art, the present application can improve the accuracy and efficiency of the inaccuracy calibration of electric energy meters.

[0119] Example 2

[0120] Please refer to Figure 2 , Figure 2 This is a structural diagram of an embodiment of an electric energy meter misalignment calibration system based on electric energy pulse acquisition provided by the present application, comprising: an acquisition module 100, a processing module 200, a calculation module 300 and a calibration module 400;

[0121] The acquisition module 100 is used to respectively acquire the first pulse number corresponding to the master meter and the second pulse numbers corresponding to the plurality of sub-meters in the meter box;

[0122] The processing module 200 is used to smooth the first pulse number and each second pulse number respectively to obtain a first average electric energy pulse number corresponding to the total meter and a second average electric energy pulse number corresponding to each sub-meter;

[0123] In some embodiments, the processing module 200 includes: a first processing unit, a second processing unit and a third processing unit; the first processing unit is used to perform time synchronization on the first pulse number and each second pulse number, and obtain the first pulse data corresponding to the total table and the second pulse data corresponding to each sub-table respectively; the second processing unit is used to perform data preprocessing on the first pulse data and each second pulse data, and obtain the first preprocessing result corresponding to the total table and the second preprocessing result corresponding to each sub-table respectively; the third processing unit is used to smooth the first preprocessing result and each second preprocessing result through a multi-cycle weighted average algorithm, and obtain the first average electric energy pulse number corresponding to the total table and the second average electric energy pulse number corresponding to each sub-table.

[0124] The calculation module 300 is configured to input the first average number of electric energy pulses and each second average number of electric energy pulses into a preset misalignment analysis model for solution to obtain a line loss rate, a fixed loss, and a plurality of sub-meter errors, wherein the misalignment analysis model is constructed based on a mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss, and the initial fixed loss;

[0125] The calibration module 400 is configured to determine an inaccurate sub-meter that needs to be calibrated based on a number of sub-meter errors, and calibrate the inaccurate sub-meter based on the line loss rate and the fixed loss.

[0126] In some embodiments, the calibration module 400 includes: an acquisition unit, a determination unit, a verification unit and a calibration unit; the acquisition unit is used to respectively obtain the first metering value of the inaccurate sub-meter, the second metering values of other sub-meters and the power supply of the total meter; the determination unit is used to determine the true metering value of the inaccurate sub-meter based on the sub-meter error and the first metering value; the verification unit is used to determine the total power consumption of all sub-meters in the meter box based on the true metering value and the second metering value, and perform energy conservation verification based on the total power consumption, the total meter power supply, the line loss rate and the fixed loss; the calibration unit is used to calibrate the inaccurate sub-meter based on the true metering value if the verification is passed.

[0127] The information interaction, execution process, etc. between the modules in the above-mentioned electric energy meter misalignment calibration system based on electric energy pulse acquisition are based on the same concept as the embodiment of the electric energy meter misalignment calibration method based on electric energy pulse acquisition in the first aspect of the present invention, and the technical effects achieved are basically the same. For specific contents, please refer to the description in the first embodiment of the method of the present invention, and will not be repeated here.

[0128] The apparatus embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these elements may be selected based on actual needs to achieve the objectives of the methods of this embodiment.

[0129] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for correcting the misalignment of an electric energy meter based on electric energy pulse acquisition as described in the first embodiment above is implemented.

[0130] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-monitorable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0131] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0132] It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A method for correcting the inaccuracy of an electric energy meter based on electric energy pulse acquisition, characterized in that: including: collecting the first pulse number corresponding to the main meter and the second pulse numbers corresponding to several sub - meters in the meter box respectively; performing smoothing processing on the first pulse number and each of the second pulse numbers respectively to obtain the first average power pulse number corresponding to the main meter and the second average power pulse numbers corresponding to each sub - meter; inputting the first average power pulse number and each of the second average power pulse numbers into a preset misalignment analysis model for solution, and respectively obtaining the line loss rate, the fixed loss, and several sub - meter errors, wherein the misalignment analysis model is constructed according to the mathematical relationship of the main meter power supply, sub - meter power consumption, initial line loss, and initial fixed loss; determining the misaligned sub - meters that need to be calibrated according to several sub - meter errors, and checking the misaligned sub - meters based on the line loss rate and the fixed loss.

2. The method for calibrating an electric energy meter based on electric energy pulse acquisition according to claim 1, characterized in that: The expression of the misalignment analysis model is specifically: Where, is the second average energy pulse number of each sub-meter after multi-cycle weighted average processing, c j is the pulse constant of each sub-meter, where j = 1, 2, ..., p; N' is the first average electric energy pulse number of the total meter in the meter box after multi-cycle weighted averaging, and c' is the pulse constant of the total meter in the meter box; is the measurement value of the same electric energy meter in different time series; e j is the error of each sub-table; e y is the line loss; e0 is the fixed loss; n is the total number of equations.

3. The method for correcting the inaccuracy of an electric energy meter based on electric energy pulse acquisition according to claim 2, characterized in that: The process of inputting the first average power pulse number and each of the second average power pulse numbers into a preset misalignment analysis model for solution is specifically: transforming the misalignment analysis model to obtain a target misalignment analysis model, and judging the size relationship between the data of n measurement cycles and P + 2 unknowns, where P is the number of sub - meters in the meter box; If n = P + 2, the target misalignment analysis model is a positive definite equation, and the linear equation can be directly solved; If n > P + 2, the target misalignment analysis model is an over - determined system of equations, and the least - squares method or other optimization methods are used for solution; If n < P + 2, the target misalignment analysis model is an indefinite equation, and methods such as ridge regression are used for solution.

4. The method for calibrating an electric energy meter based on electric energy pulse acquisition according to claim 1, characterized in that: The process of checking the misaligned sub - meters based on the line loss rate and the fixed loss is specifically: respectively obtaining the first measurement value of the misaligned sub - meter, the second measurement values of other sub - meters, and the main meter power supply; determining the true measurement value of the misaligned sub - meter based on the sub - meter error and the first measurement value; determining the total power consumption of all sub - meters in the meter box based on the true measurement value and the second measurement values, and performing energy conservation verification based on the total power consumption, the main meter power supply, the line loss rate, and the fixed loss; if the verification passes, the misaligned sub - meter is checked based on the true measurement value.

5. The method for calibrating an electric energy meter based on electric energy pulse acquisition according to claim 1, characterized in that: The process of performing smoothing processing on the first pulse number and each of the second pulse numbers respectively to obtain the first average power pulse number corresponding to the main meter and the second average power pulse numbers corresponding to each sub - meter is specifically: performing time synchronization on the first pulse number and each of the second pulse numbers to respectively obtain the first pulse data corresponding to the main meter and the second pulse data corresponding to each sub - meter; performing data pre - processing on the first pulse data and each of the second pulse data to respectively obtain the first pre - processing result corresponding to the main meter and the second pre - processing results corresponding to each sub - meter; performing smoothing processing on the first pre - processing result and each of the second pre - processing results through a multi - cycle weighted average algorithm to obtain the first average power pulse number corresponding to the main meter and the second average power pulse numbers corresponding to each sub - meter.

6. The method for calibrating an electric energy meter based on electric energy pulse acquisition according to claim 5, characterized in that: The calculation formula for performing smoothing processing on the first pre - processing result and each of the second pre - processing results through a multi - cycle weighted average algorithm is specifically: In the formula, in the formula, is the first average number of electric energy pulses after the first pre-processing result of the i-th cycle total table is smoothed over multiple cycles, w k is the weight of the kth period, N′ i+k is the first preprocessing result corresponding to the total table of the i+kth period, M is the cumulative number of periods, is the second average number of electric energy pulses after the second pre-processing results of each sub-table in the i-th period are smoothed over multiple periods, is the second preprocessing result corresponding to each sub-table in the i+k-th period.

7. An electric energy meter inaccuracy calibration system based on electric energy pulse acquisition, characterized in that: including: Acquisition module, processing module, calculation module and verification module; The acquisition module is used to respectively acquire the first pulse number corresponding to the main meter and the second pulse numbers corresponding to the plurality of sub-meters in the meter box; The processing module is used to perform smoothing processing on the first pulse number and each second pulse number respectively to obtain a first average electric energy pulse number corresponding to the total meter and a second average electric energy pulse number corresponding to each sub-meter; The calculation module is configured to input the first average number of electric energy pulses and each second average number of electric energy pulses into a preset misalignment analysis model for solution to obtain a line loss rate, a fixed loss, and a plurality of sub-meter errors, wherein the misalignment analysis model is constructed based on a mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss, and the initial fixed loss; The calibration module is used to determine the inaccurate sub-meter that needs to be calibrated according to the errors of the multiple sub-meters, and calibrate the inaccurate sub-meter based on the line loss rate and the fixed loss.

8. The electric energy meter inaccuracy calibration system based on electric energy pulse acquisition according to claim 7 is characterized in that: The verification module includes: an acquisition unit, a determination unit, a verification unit and a verification unit; The acquisition unit is used to respectively acquire the first measurement value of the inaccurate sub-meter, the second measurement values of other sub-meters, and the power supply of the main meter; The determining unit is configured to determine a true measurement value of the inaccurate sub-meter based on the sub-meter error and the first measurement value; The verification unit is configured to determine the total power consumption of all sub-meters in the meter box based on the real measurement value and the second measurement value, and perform energy conservation verification based on the total power consumption, the total meter power supply, the line loss rate, and the fixed loss; The calibration unit is configured to calibrate the inaccuracy meter based on the true measurement value if the verification is passed.

9. The electric energy meter inaccuracy calibration system based on electric energy pulse acquisition according to claim 7, characterized in that: The processing module includes: a first processing unit, a second processing unit and a third processing unit; The first processing unit is configured to perform time synchronization on the first pulse number and each of the second pulse numbers to obtain first pulse data corresponding to the total table and second pulse data corresponding to each sub-table; The second processing unit is configured to perform data preprocessing on the first pulse data and each of the second pulse data to obtain a first preprocessing result corresponding to the total table and a second preprocessing result corresponding to each sub-table; The third processing unit is used to smooth the first preprocessing result and each second preprocessing result through a multi-cycle weighted average algorithm to obtain a first average number of electric energy pulses corresponding to the total meter and a second average number of electric energy pulses corresponding to each sub-meter.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for correcting the misalignment of an electric energy meter based on electric energy pulse acquisition as described in any one of claims 1 to 6 is implemented.

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