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

Abstract

This application discloses a method, system, and medium for energy meter misalignment verification based on energy pulse acquisition, comprising: acquiring the first pulse count corresponding to the main meter and the second pulse count corresponding to several sub-meters in the meter box; smoothing the first pulse count and each of the second pulse counts to obtain the first average energy pulse count corresponding to the main meter and the second average energy pulse count corresponding to each sub-meter; inputting the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution, obtaining the line loss rate, fixed loss, and several sub-meter errors; determining the misaligned sub-meters that need to be calibrated based on the several sub-meter errors, and verifying the misaligned sub-meters based on the line loss rate and the fixed loss. This application can improve the accuracy and efficiency of energy meter misalignment verification.

Description

Technical Field

[0001] This application relates to the field of power metering, and in particular to a method, system and medium for calibrating power meter inaccuracies based on power pulse acquisition. Background Technology

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

[0003] In existing technologies, two main methods are used for calibrating electricity meters to correct misalignment. The first method involves manually carrying calibration equipment to the site to verify the smart meters. While this method offers high accuracy, it is time-consuming, labor-intensive, inefficient, and time-consuming, making it difficult to conduct a comprehensive survey of a large number of meters. The second method involves error assessment based on data analysis of distribution area data. Based on the principle of energy conservation in distribution areas, this method uses electricity consumption data collected by the metering automation system to evaluate the smart meters and tracks changes in error in real time based on new data. However, this method requires sufficient distribution area electricity consumption data, and the accuracy of the error assessment results is relatively poor. Therefore, it is crucial to research a method that can improve the accuracy and efficiency of electricity meter misalignment calibration. Summary of the Invention

[0004] This application provides a method, system, and medium for calibrating energy meter misalignment based on energy pulse acquisition, in order to improve the accuracy and efficiency of energy meter misalignment calibration.

[0005] In a first aspect, this application provides a method for calibrating the inaccuracy of an energy meter based on energy pulse acquisition, including:

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

[0007] The first pulse count and each of the second pulse counts are smoothed to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table;

[0008] The first average number of electrical energy pulses and each of the second average number of electrical energy pulses are input into a preset misalignment analysis model for solution, and the line loss rate, fixed loss and several sub-meter errors are obtained respectively. The misalignment analysis model is constructed based on the mathematical relationship between the total meter power supply, the sub-meter power consumption, the initial line loss and the initial fixed loss.

[0009] The misaligned sub-meters that need to be calibrated are determined based on several sub-meter errors, and the misaligned sub-meters are checked based on the line loss rate and the fixed loss.

[0010] This application's embodiments collect pulse counts from the main meter and each sub-meter, providing a complete data foundation for subsequent analysis and avoiding inaccuracies caused by missing data. Smoothing processing reduces noise and interference in the pulse counts, mitigating the impact of data fluctuations on subsequent analysis and making the data more stable and reliable. Solving the misalignment analysis model yields key parameters such as line loss rate, fixed loss, and sub-meter error, enabling quantitative analysis of metering errors and providing a concrete basis for further verification and adjustment. By comparing sub-meter errors with preset error thresholds, misaligned sub-meters requiring verification can be accurately identified, avoiding unnecessary verification of all sub-meters and improving the targeting and efficiency of the verification work. Verifying misaligned sub-meters based on line loss rate and fixed loss more accurately determines the true metering value, correcting sub-meter metering errors and improving the accuracy of electricity metering. Compared with existing technologies, this application can improve the accuracy and efficiency of electricity meter misalignment verification.

[0011] Furthermore, the expression for the misalignment analysis model is as follows:

[0012]

[0013] In the formula, c is the second average number of energy pulses after multi-cycle weighted averaging of each sub-meter. j is the pulse constant of each sub-meter, where j = 1, 2, ..., p; N′ is the first average energy pulse number of the meter box after multi-cycle weighted averaging, and c′ is the pulse constant of the meter box. These are the meter readings from the same electricity meter at different time series; e j For each of the sub-table errors; e y e0 is the line loss; n is the fixed loss; and n is the total number of equations.

[0014] Furthermore, the step of inputting the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution is specifically as follows:

[0015] Transforming the misalignment analysis model to obtain a target misalignment analysis model, and comparing the number of n metering cycle data with P+2 unknowns, wherein 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 solved directly;

[0017] if n>P+2, the target misalignment analysis model is an overdetermined system of equations, which is solved by the least square method or other optimization methods;

[0018] if n<P+2, the target misalignment analysis model is an indeterminate equation, which is solved by methods such as ridge regression.

[0019] Further, the checking of the misaligned sub-meter based on the line loss rate and the fixed loss is specifically:

[0020] respectively obtaining a first metering value of the misaligned sub-meter, second metering values of other sub-meters, and the power supply amount of the main meter;

[0021] determining a true metering value of the misaligned sub-meter based on the sub-meter error and the first metering value;

[0022] determining a total power consumption of all sub-meters in the meter box based on the true metering value and the second metering values, and performing energy conservation verification based on the total power consumption, the power supply amount of the main meter, the line loss rate and the fixed loss;

[0023] if the verification is passed, the misaligned sub-meter is checked based on the true metering value.

[0024] In this way, checking the misaligned sub-meter based on the line loss rate and the fixed loss can more accurately determine the true metering value of the sub-meter, realize the correction of the metering error of the sub-meter, and improve the accuracy of electric energy metering.

[0025] Further, the smoothing processing of the first pulse number and each of the second pulse numbers respectively to obtain a first average electric energy pulse number corresponding to the main meter and a second average electric energy pulse number corresponding to each sub-meter is specifically:

[0026] performing time synchronization on the first pulse number and each of the second pulse numbers to respectively obtain first pulse data corresponding to the main meter and second pulse data corresponding to each sub-meter;

[0027] performing data preprocessing on the first pulse data and each of the second pulse data to respectively obtain a first preprocessing result corresponding to the main meter and a second preprocessing result corresponding to each sub-meter;

[0028] The first preprocessing result and each of the second preprocessing results are smoothed by a multi-cycle weighted average algorithm to obtain the first average number of power pulses corresponding to the total table and the second average number of power pulses corresponding to each sub-table.

[0029] This smoothing process reduces noise and interference in the pulse count, minimizes the impact of data fluctuations on subsequent analysis, and makes the data more stable and reliable.

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

[0031]

[0032] In the formula, in the formula, w represents the first average number of electrical energy pulses after multi-cycle smoothing of the first preprocessing result of the total table in the i-th period. k Let N′ be the weight for the k-th period. i+k This represents the first preprocessing result corresponding to the summary table of the (i+k)th cycle, where M is the cumulative number of cycles. The second average number of electrical energy pulses is the result of multi-cycle smoothing of the second preprocessing result of each sub-table in the i-th cycle. This represents the second preprocessing result corresponding to each sub-table in the (i+k)th cycle.

[0033] Secondly, this application provides a power meter misalignment verification device based on power pulse acquisition, comprising: an acquisition module, a processing module, a calculation module and a verification module;

[0034] The acquisition module is used to acquire the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters respectively.

[0035] The processing module is used to smooth the first pulse count and each of the second pulse counts respectively to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table;

[0036] The calculation module is used to input the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution, and obtain the line loss rate, fixed loss and several sub-meter errors respectively. The misalignment analysis model is constructed based on the 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-meters that need to be calibrated based on several sub-meter errors, and to calibrate the inaccurate sub-meters based on the line loss rate and the fixed loss.

[0038] This application's embodiments collect pulse counts from the main meter and each sub-meter, providing a complete data foundation for subsequent analysis and avoiding inaccuracies caused by missing data. Smoothing processing reduces noise and interference in the pulse counts, mitigating the impact of data fluctuations on subsequent analysis and making the data more stable and reliable. Solving the misalignment analysis model yields key parameters such as line loss rate, fixed loss, and sub-meter error, enabling quantitative analysis of metering errors and providing a concrete basis for further verification and adjustment. By comparing sub-meter errors with preset error thresholds, misaligned sub-meters requiring verification can be accurately identified, avoiding unnecessary verification of all sub-meters and improving the targeting and efficiency of the verification work. Verifying misaligned sub-meters based on line loss rate and fixed loss more accurately determines the true metering value, correcting sub-meter metering errors and improving the accuracy of electricity metering. Compared with existing technologies, this application can improve the accuracy and efficiency of electricity meter misalignment verification.

[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 acquire the first metering value of the inaccurate sub-meter, the second metering value of other sub-meters, and the total power supply of the meter, respectively.

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

[0042] The verification unit is used to determine the total power consumption of all sub-meters in the meter box based on the actual meter value and the second meter value, and to 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 verification unit is used to verify the inaccurate sub-table based on the actual measurement value if the verification passes.

[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 used to synchronize the first pulse count and each of the second pulse counts in time, and obtain the first pulse data corresponding to the master table and the second pulse data corresponding to each sub-table, respectively.

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

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

[0048] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy meter misalignment verification method based on energy pulse acquisition as described in this application. Attached Figure Description

[0049] Figure 1 This is the topology diagram of the meter box area provided in this application;

[0050] Figure 2 This is a flowchart illustrating an embodiment of the energy meter misalignment verification method based on energy pulse acquisition provided in this application;

[0051] Figure 3 This application Figure 2 A flowchart illustrating step S102;

[0052] Figure 4 This is a schematic diagram of an embodiment of the energy meter misalignment verification system based on energy pulse acquisition provided in this application. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

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

[0055] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this 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 “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0057] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0058] Electricity metering is crucial for electricity production, marketing, and grid security. Its accuracy directly impacts the fairness and impartiality of electricity trade settlement, thereby affecting trust between power companies and users. However, electricity meters may experience metering inaccuracies due to factors such as component aging, quality issues, environmental impacts, and improper use, leading to economic losses, disputes, and even threatening grid security. Therefore, timely and accurate inaccuracy verification is essential for ensuring the accuracy of trade settlement and maintaining order in the electricity market. Existing verification methods mainly fall into two categories: manual on-site verification, which, while highly accurate, is inefficient, time-consuming, and difficult to conduct comprehensive surveys; and error assessment based on distribution area data, utilizing the principle of energy conservation and electricity consumption data analysis, but requiring large amounts of data and having limited accuracy. Therefore, researching methods to improve the accuracy and efficiency of electricity meter inaccuracy verification is particularly important.

[0059] Next, the terms used in this application will be explained:

[0060] Inaccurate electricity meter readings: Electricity meters may become inaccurate due to reasons such as aging of their own components, quality problems with spare parts, the influence of the electricity meter's operating environment, or improper use.

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

[0062] Based on this, the embodiments of this application provide a method, system and medium for verifying the inaccuracy of electricity meters based on power pulse acquisition, which can improve the accuracy and efficiency of electricity meter inaccuracy verification.

[0063] This application provides a method, system, and medium for verifying the inaccuracy of an energy meter based on energy pulse acquisition. The specific implementation details are provided in the following embodiments. First, the method for verifying the inaccuracy of an energy meter based on energy pulse acquisition in this application is described.

[0064] The energy meter misalignment verification method based on energy pulse acquisition provided in this application relates to the field of power metering. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the energy meter misalignment verification method based on energy pulse acquisition, etc., but is not limited to the above forms.

[0065] This application can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This 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, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0066] Before describing the energy meter misalignment verification method based on energy pulse acquisition in the embodiments of this application, let's first introduce the meter box topology to facilitate subsequent understanding. The meter box substation topology diagram is as follows: Figure 1 As shown, the table box topology consists of a master table M. 总 and several sub-tables (M1~M i The meter box consists of a main meter and individual user meters, forming a simple tree topology. Energy transfer between the meter boxes occurs through the main meter to each user, with individual user electricity consumption data measured by each user's individual meter. The energy data from the meter boxes is obtained by multiple upstream devices with Bluetooth modules wirelessly acquiring the energy pulses output by the meters and counting the pulses from each channel to obtain the change in the meter reading within a specific period. This meter box topology and power supply / consumption relationship form the physical basis for the subsequent misalignment analysis model.

[0067] Example 1

[0068] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the energy meter misalignment verification method based on energy pulse acquisition provided in this application, including steps S101 to S104.

[0069] Step S101: Collect the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters respectively;

[0070] In some embodiments, the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters are collected respectively. Specifically, the main meter in the meter box establishes wireless communication with each sub-meter, where the main meter in the meter box acts as the Bluetooth communication master device, and each user energy meter acts as the Bluetooth communication slave device. The main meter in the meter box is responsible for allocating time slots, and each user energy meter transmits data within the specified time slot. The communication between the main meter in the meter box and the user energy meters is full-duplex, meaning that the main meter in the meter box and the user energy meters can send and receive data in different time slots. Secondly, the master device uses a high-precision clock as a synchronization clock source. The slave devices synchronize their internal clocks through communication with the master device. When a slave device joins the Bluetooth network, it obtains the clock information of the master device by receiving synchronization packets (such as FHS packets) sent by the master device and adjusts its own clock to keep synchronized with the master device. This enables the main meter to collect its own pulse count (i.e., the first pulse count, denoted as N′) in real time and receive the second pulse count (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 main meter box establishes wireless communication with each sub-meter. Specifically, before joining the Bluetooth network, the slave device scans for available master device channels. Once a 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 the transmission plan. The slave device then obtains the master device's clock information and time slot allocation plan by receiving the synchronization packet sent by the master device, thus completing the initial synchronization.

[0072] It should be noted that the master device allocates transmit and receive time slots to each slave device according to a preset time slot scheduling algorithm. This scheduling algorithm can be fixed or dynamically adjusted. Within the allocated time slots, the master and slave devices transmit and receive data packets in a predetermined order. Each data packet contains a time slot number to identify the current time slot. After receiving a data packet from the master device, 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 ensure the transmission priority of critical data frames.

[0073] It should be noted that the master device periodically 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's clock and compensates for it to reduce accumulated errors. In addition, the master device and the slave device maintain their connection through periodic 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 communication stability. 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 the time slot resources it allocated. When the slave device reconnects to the network, it needs to resynchronize to obtain new time slot allocations.

[0076] Through the above mechanisms, the Bluetooth timeslot synchronization mechanism ensures time coordination between the master and slave devices, enabling efficient and reliable data synchronization and 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 directly 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 count N′ is the pulse data of the main meter, serving as a reference value for the total power supply of the meter box, while the second pulse count N... j The pulse data for each sub-meter in the meter box can be used as a reference value for the power consumption of each sub-meter.

[0079] Step S102: Smooth the first pulse count and each of the second pulse counts respectively to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table;

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

[0081] Step S301: Synchronize the first pulse count and each of the second pulse counts in time to obtain the first pulse data corresponding to the master table and the second pulse data corresponding to each sub-table;

[0082] In some embodiments, the synchronization mechanism of Bluetooth communication is used to synchronize the first pulse count N′ of the total table and the second pulse count N′ of the sub-table. j Time synchronization is performed to ensure the periodicity and consistency of the data.

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

[0084] Step S302: Perform data preprocessing on the first pulse data and each of the second pulse data to obtain the first preprocessing result corresponding to the general table and the 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, firstly, a digital filtering algorithm (such as a low-pass filter) is used to remove the first pulse data N′ and each of the second pulse data N. j High-frequency noise in the data; then, statistical methods are used to identify and remove the first pulse data N′ and each of the second pulse data N. j Outliers in the data can be identified by calculating the mean and standard deviation of the data, and data points exceeding a certain range (e.g., mean ± 3 times standard deviation) can be considered noise and removed. Finally, based on known calibration data or a standard signal source, the first pulse data N′ and each of the second pulse data N′ are processed. j Adjustments are made to eliminate systematic errors, thereby obtaining the first preprocessing result corresponding to the master table and the second preprocessing result corresponding to each sub-table.

[0087] Step S303: The first preprocessing result and each of the second preprocessing results are smoothed by a multi-cycle weighted average algorithm to obtain the first average number of power pulses corresponding to the total table and the second average number of power pulses corresponding to each sub-table.

[0088] In some embodiments, a time window is set to perform a weighted average of the first preprocessing result and each of the second preprocessing results. During the weighted averaging process, a weighting strategy needs to be set, where the weights can be equal or exponentially decaying, to obtain the first average number of energy pulses corresponding to the total table after weighted averaging. and the second average number of electrical energy pulses corresponding to each sub-table For example, if the current time series is 1 second and the number of energy pulses fluctuates greatly, causing the error model solution to be unstable, a 10-second time window (M=10) can be used for smoothing, and the data of one pulse can be weighted for calculation.

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

[0090]

[0091] In the formula, in the formula, w represents the first average number of electrical energy pulses after multi-cycle smoothing of the first preprocessing result of the total table in the i-th period. k Let N′ be the weight for the k-th period. i+k This represents the first preprocessing result corresponding to the summary table of the (i+k)th cycle, where M is the cumulative number of cycles. The second average number of electrical energy pulses is the result of multi-cycle smoothing of the second preprocessing result of each sub-table in the i-th cycle. This represents the second preprocessing result corresponding to each sub-table in the (i+k)th cycle.

[0092] This smoothing process reduces noise and interference in the pulse count, minimizes the impact of data fluctuations on subsequent analysis, and makes the data more stable and reliable.

[0093] Step S103: Input the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution, and obtain the line loss rate, fixed loss and several sub-meter errors respectively. The misalignment analysis model is constructed based on the 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 topology of the meter box and the power supply relationship are the physical basis of the smart meter metering error assessment model, a mathematical relationship can be constructed between the total metering data of the meter box, the line loss of the meter box, the fixed loss of the meter box, the metering data of the sub-meters and the meter error, based on the topology and the principle of energy conservation. This relationship is known as the misalignment analysis model.

[0095] In some embodiments, the specific structure of the misalignment analysis model is as follows: Total power consumption of the meter box = Sum of power consumption of each user + Line loss + Fixed loss of the meter box. Here, the total power consumption of the meter box and the power consumption of each user are the information measured by the smart meter; line loss is the energy loss during power transmission on the transmission line, mainly caused by line impedance; and fixed loss is the energy consumption loss of communication equipment, data acquisition terminals, and smart meters in the meter box circuit. Since the power of these devices is relatively stable, they are considered fixed losses in the model. Therefore, the specific construction process of the misalignment analysis model is as follows: First, the total power consumption of the meter box and the power consumption of each user are represented by the smart meter measurement data and its error. In the formula, P represents the total number of sub-meters in the meter box, y(i) represents the total power supply of the meter in metering cycle i, and φ j (i) represents the metering value of submeter j within metering period i, e j e represents the relative error of the estimation of sub-table j. y Let e0 be the meter box line loss rate, and e0 be the meter box fixed loss. Using the main meter box as the calibration device to calibrate each sub-meter in the meter box, the total power supply y(i) of the meter box can be approximated by the meter value y′(i) of the main meter box. Thus: Subsequently, the model, based on the original equations, introduces the concept of multi-period weighted averaging for sub-meter errors, line loss rates, and fixed loss rates. The weights refer to the corresponding user meter or main meter readings. When establishing the equations, it is assumed that the fluctuation range of the weighted average over a certain period is very limited or even remains constant. After weighting and averaging the errors, line loss rates, and fixed losses, the adverse effects of noise, time differences, etc., on these quantities will be reduced. Simultaneously, for residential users, since their electricity consumption habits remain relatively stable within a certain period, the weighted average of their electricity meter errors can be considered constant. Specifically, taking M periods for weighted averaging and transforming it yields the following relationship:

[0096]

[0097] In the formula, This represents the average total power consumption of the meter over the M metering cycles starting from metering cycle i. εj represents the average value of the measured values ​​at metering point j over the first M metering cycles of metering period i, and ε0 represents the average fixed loss of the meter box over the M metering cycles. y ε represents the weighted average line loss rate over M metering cycles of the meter box. j Let represent the weighted average of the estimated relative errors over M measurement periods for measurement point j. Therefore, after the meter box accumulates data for n periods, a system of n equations can be obtained, specifically:

[0098]

[0099] In this system of equations, y′(n) represents known quantities, which are the measured values ​​of different time series in the summary table, and φ p (1)—φ p (n) represents known quantities, which are the metered values ​​of different time series within the same sub-meter. When the number of data points n in the metering period 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 with the fixed loss e0, the operating error values ​​of each user's energy meter in the meter box are obtained. Then, the cumulative change in the number of pulses of each user's energy meter obtained by the main meter through the Bluetooth channel is used to approximate the change in electrical energy of each user's energy meter. At the same time, the change in the number of pulses of the main meter in the meter box is used to approximate the change in electrical energy of the main meter in the meter box. Combining the pulse constants of the main meter in the meter box and each user's sub-meter, the above equations can be rewritten as follows: In the formula, c represents the meter readings at different time series from the same electricity meter. j N j c, c′, and N′ are known quantities, where c j N is the pulse constant of each sub-table. j is the number of energy pulses of each sub-meter, where j = 1, 2, ..., p, c′ is the pulse constant of the meter box, and N′ is the number of energy pulses of 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 the fixed loss e0, thus 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 electricity meter for every kilowatt-hour (kWh) of electrical energy consumed. The pulse constant is fixed for each electricity meter, and the unit is pulses / kWh. For example, K = 3200 pulses / kWh means that the electricity meter outputs a total of 3200 pulses for every 1 kWh of electricity consumed. The number of electrical energy pulses (i.e., the first and second pulse counts mentioned above) are the total number of pulses actually measured, without units, and directly reflect the cumulative number of pulses output by the electricity meter over a certain period of time. The meter reading is the consumed electrical energy, such as φ in the above equation. j (i), which is the metering value of meter j in metering period i. Among them, the relationship between pulse constant, number of energy pulses, and meter box metering value is: 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, based on the accumulated data of multiple periodic pulses, the above equations can be transformed into expressions for a misalignment analysis model, by using the first average energy pulse count corresponding to the total table. Second average electric energy pulse numbers corresponding to each sub-meter are substituted into the inaccuracy analysis model together, and then the sub-meter error e j , line loss rate e y and fixed loss e0 can be obtained by solving.

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

[0104]

[0105] In the formula, is the second average electric 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 average processing, and c' is the pulse constant of the total meter in the meter box; is the metering value of the same electric energy meter in different time sequences; e j is the error of each said sub-meter, where j=1,2,...,p; e y is the said line loss; e0 is the said fixed loss; n is the total number of equations.

[0106] In some embodiments, said inputting the first average electric energy pulse number and each said second average electric energy pulse number into the preset inaccuracy analysis model for solving comprises: transforming the said inaccuracy analysis model to obtain a target inaccuracy analysis model, and comparing the quantity of n metering cycle data with P+2 unknowns, wherein P is the number of sub-meters in the meter box; if n=P+2, the target inaccuracy analysis model is a positive definite equation, which can directly solve the linear equation; if n>P+2, the target inaccuracy analysis model is an overdetermined system of equations, which is solved by the least square method or other optimization methods; if n<P+2, the target inaccuracy analysis model is an indeterminate equation, which is solved by ridge regression or other methods. Specifically, firstly, the expression of the inaccuracy analysis model is transformed and written in matrix form to obtain the target inaccuracy analysis model, wherein the expression of the target inaccuracy analysis model is:

[0107]

[0108] In the formula, For the inaccuracy analysis model in the form of linear equations, the general matrix expression is

[0109] Ax=b, in the formula, A is an electricity consumption matrix, x is an error vector, b is metering line loss electric quantity, and in the target inaccuracy analysis model, the metering value of the district total meter (that is, the first average electric energy pulse number ) and the metering value of the user sub-meter (that is, the second average electric energy pulse number are known quantities, and the error e of each user's electric energy meter j and line loss rate e y and fixed loss e0 are unknown quantities of the equation. It is assumed that the metering error of each electric meter remains relatively stable within the calculation period. Comparing the accumulated data of n metering cycles with P+2 unknown quantities, 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 solved directly; if n>P+2, the target misalignment analysis model is an overdetermined system of equations, which is solved by the least square method or other optimization methods; if n<P+2, the target misalignment analysis model is an indeterminate equation, which is solved by methods such as ridge regression.

[0110] It should be noted that in practice, the amount of collected data is generally larger than the number of user meters, that is, n>P+2. Meanwhile, in order to reduce the large error of the unique solution solved by n=P+2 systems of equations, the number of cycles n is increased to make n>P+2, and then the sum of squared errors of the equations of n cycles is minimized by methods such as the least square method to solve the result. For the case where n<P+2, since the number of unknowns is greater than the number of knowns, the solved results are not unique, so it does not belong to the scope of our practical application;

[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 square method is required to fit the optimal solution. The specific process is as follows: first, the goal of solving the optimal solution for the overdetermined system of equations is to minimize the sum of residuals, that is, the goal is to find an error vector x such that the difference between the model predicted value Ax and the actual observed value b is as small as possible, which means minimizing the residual between them, wherein ‖.‖ is the Euclidean norm representing a vector (that is, taking the square root after calculating the sum of squares for each vector respectively), and the sum of squared residuals ‖Ax-b‖ 2 is a measure of the goodness of model fitting, and a smaller value indicates better fitting. For example: ‖Ax-b‖ 2 =0 2 +0 2 +0 2 +(-1) 2 =1; then, expand the objective function and take the derivative: Meanwhile, in order to minimize the objective function, the partial derivative of the objective function is used, since the minimum value is obtained when the partial derivative is 0. Take the derivative with respect to x and set it to zero, arrange to obtain the normal equation: 2A T Ax-2A T b=0, A T Ax=A T b, wherein A TGiven the transpose of matrix A, we can solve for x: x = (A T A) -1 A T b. This solution is the least squares method, which is the only solution that minimizes the sum of squared residuals. It can be used to solve for the relative error, line loss rate and fixed loss rate of each sub-table.

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

[0113] It should be noted that the specific solution to the inaccuracy analysis model can also be achieved using existing technologies, and this application does not impose any restrictions.

[0114] Step S104: Determine the misaligned sub-meters that need to be calibrated based on several sub-meter errors, and verify the misaligned sub-meters based on the line loss rate and the fixed loss.

[0115] In some embodiments, the misaligned sub-tables that need to be calibrated are determined based on several sub-table errors. Specifically, the calculated sub-table error e is... j Compare with the allowable error limit (e.g., ±2%). If the error exceeds the allowable range, it indicates that the sub-meter is inaccurate and the actual measurement value needs to be corrected.

[0116] In some embodiments, the step of verifying the inaccurate sub-meter based on the line loss rate and the fixed loss specifically involves: obtaining the first metering value of the inaccurate sub-meter, the second metering values ​​of other sub-meters, and the total power supply of the meter; 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 power supply of the meter, the line loss rate, and the fixed loss; if the verification passes, then verifying the inaccurate sub-meter based on the true metering value. Specifically, firstly, the first metering value E is determined by combining the pulse number and the pulse constant. err The second measurement value E of other sub-tables j And the total power supply y′; then, based on the already solved sub-meter error, correct the first meter value E. err E′ err =E err (1-e j The true measurement value E′ of the misaligned sub-meter is obtained. err Then, based on the actual measurement value E′ err and the second measurement value E j Sum the total electricity consumption Y, total electricity consumption Y = E′ err +∑ j≠err E jThen, the total electricity consumption Y is substituted into the energy conservation equation to verify whether y′≈Y+e is satisfied. y If the deviation is within the allowable range (e.g., ±0.5%), the verification is successful. Finally, when the verification is successful, the error of the misaligned sub-meter is confirmed to be valid, and its measurement value is updated to E′. err This triggers the replenishment of electricity from misaligned meters. If the verification fails, the model parameters (e) are recalibrated. y (e0 is the meter box line loss rate, and e0 is the meter box fixed loss) or check whether there are hidden errors in other sub-meters.

[0117] By calibrating inaccurate sub-meters based on line loss rate and fixed loss, the true metering value of the sub-meters can be determined more accurately, the metering error of the sub-meters can be corrected, and the accuracy of electricity metering can be improved.

[0118] This application's embodiments collect pulse counts from the main meter and each sub-meter, providing a complete data foundation for subsequent analysis and avoiding inaccuracies caused by missing data. Smoothing processing reduces noise and interference in the pulse counts, mitigating the impact of data fluctuations on subsequent analysis and making the data more stable and reliable. Solving the misalignment analysis model yields key parameters such as line loss rate, fixed loss, and sub-meter error, enabling quantitative analysis of metering errors and providing a concrete basis for further verification and adjustment. By comparing sub-meter errors with preset error thresholds, misaligned sub-meters requiring verification can be accurately identified, avoiding unnecessary verification of all sub-meters and improving the targeting and efficiency of the verification work. Verifying misaligned sub-meters based on line loss rate and fixed loss more accurately determines the true metering value, correcting sub-meter metering errors and improving the accuracy of electricity metering. Compared with existing technologies, this application can improve the accuracy and efficiency of electricity meter misalignment verification.

[0119] Example 2

[0120] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of an embodiment of the energy meter misalignment verification system based on energy pulse acquisition provided in this application, including: acquisition module 100, processing module 200, calculation module 300 and verification module 400;

[0121] The acquisition module 100 is used to acquire the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters respectively.

[0122] The processing module 200 is used to smooth the first pulse count and each of the second pulse counts respectively to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table;

[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 synchronize the first pulse count and each of the second pulse counts in time to obtain the first pulse data corresponding to the master table and the second pulse data corresponding to each sub-table; the second processing unit is used to perform data preprocessing on the first pulse data and each of the second pulse data to obtain the first preprocessing result corresponding to the master table and the second preprocessing result corresponding to each sub-table; the third processing unit is used to smooth the first preprocessing result and each of the second preprocessing results using a multi-cycle weighted average algorithm to obtain the first average energy pulse count corresponding to the master table and the second average energy pulse count corresponding to each sub-table.

[0124] The calculation module 300 is used to input the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution, and obtain the line loss rate, fixed loss and several sub-meter errors respectively. The misalignment analysis model is constructed based on the 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 used to determine the inaccurate sub-meters that need to be calibrated based on several sub-meter errors, and to calibrate the inaccurate sub-meters based on the line loss rate and the fixed loss.

[0126] In some embodiments, the verification module 400 includes: an acquisition unit, a determination unit, a verification unit, and a verification unit; the acquisition unit is used to acquire the first metering value of the inaccurate sub-meter, the second metering values ​​of other sub-meters, and the total power supply of the 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 to perform energy conservation verification based on the total power consumption, the total power supply of the meter, the line loss rate, and the fixed loss; the verification unit is used to verify the inaccurate sub-meter based on the true metering value if the verification passes.

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

[0128] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the method in this embodiment, depending on actual needs.

[0129] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the method for verifying the inaccuracy of an energy meter based on energy pulse acquisition as described in Embodiment 1 above.

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

[0131] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application.

[0132] In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for checking the accuracy of an electric energy meter based on electric energy pulse collection, characterized in that, include: Collect the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters respectively; The first pulse count and each of the second pulse counts are smoothed to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table; The first average energy pulse count and each of the second average energy pulse counts are input into a preset misalignment analysis model for solution, yielding the line loss rate, fixed loss, and several sub-meter errors. The misalignment analysis model is constructed based on the mathematical relationship between the total meter power supply, sub-meter power consumption, initial line loss, and initial fixed loss. The expression for the misalignment analysis model is as follows: In the formula, It is the second average number of energy pulses after multi-cycle weighted averaging of each sub-meter. These are the pulse constants of each sub-table, where j = 1, 2, ..., p; It is the first average energy pulse count of the total meter reading after multi-cycle weighted averaging. It is the pulse constant of the main meter in the meter box; These are the meter readings from the same electricity meter at different time series. The error of each of the sub-tables is mentioned; The line loss; The fixed loss; The total number of equations; The misaligned sub-meters that need to be calibrated are determined based on several sub-meter errors, and the misaligned sub-meters are checked based on the line loss rate and the fixed loss. Specifically, the step of verifying the inaccurate sub-meter based on the line loss rate and the fixed loss involves: obtaining the first metering value of the inaccurate sub-meter, the second metering values ​​of other sub-meters, and the total power supply of the meter; 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 power supply of the meter, the line loss rate, and the fixed loss; if the verification passes, then verifying the inaccurate sub-meter based on the true metering value.

2. The method for verifying the inaccuracy of an energy meter based on energy pulse acquisition according to claim 1, characterized in that, The step of inputting the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution is as follows: The misalignment analysis model is transformed to obtain the target misalignment analysis model, and the magnitude between the data of n measurement cycles and P+2 unknowns is determined, where P is the number of sub-meters in the meter box; like The target misalignment analysis model is a positive definite equation, which can be directly solved as a linear equation; like The target misalignment analysis model is an overdetermined system of equations, which is solved using the least squares method or other optimization methods; like The target misalignment analysis model is an indeterminate equation, which is solved using the ridge regression method.

3. The method for verifying the inaccuracy of an energy meter based on energy pulse acquisition according to claim 1, characterized in that, The first pulse count and each of the second pulse counts are smoothed to obtain the first average power pulse count corresponding to the total table and the second average power pulse count corresponding to each sub-table. Specifically: The first pulse count and each of the second pulse counts are synchronized in time to obtain the first pulse data corresponding to the master table and the second pulse data corresponding to each sub-table; The first pulse data and each of the second pulse data are preprocessed to obtain the first preprocessing result corresponding to the general table and the second preprocessing result corresponding to each sub-table. The first preprocessing result and each of the second preprocessing results are smoothed by a multi-cycle weighted average algorithm to obtain the first average number of power pulses corresponding to the total table and the second average number of power pulses corresponding to each sub-table.

4. The method for verifying the inaccuracy of an energy meter based on energy pulse acquisition according to claim 3, characterized in that, The calculation formula for smoothing the first preprocessing result and each of the second preprocessing results using a multi-period weighted average algorithm is as follows: ； ； In the formula, The first average number of electrical energy pulses is the first preprocessing result of the total table in the i-th period after multi-period smoothing. The weight for the k-th period, This represents the first preprocessing result corresponding to the summary table of the (i+k)th cycle, where M is the cumulative number of cycles. The second average number of electrical energy pulses is the result of multi-cycle smoothing of the second preprocessing result of each sub-table in the i-th cycle. This represents the second preprocessing result corresponding to each sub-table in the (i+k)th cycle.

5. A system for verifying the inaccuracy of an energy meter based on energy pulse acquisition, characterized in that, include: The module comprises an acquisition module, a processing module, a calculation module, and a verification module. The acquisition module is used to acquire the first pulse count corresponding to the main meter in the meter box and the second pulse count corresponding to several sub-meters respectively. The processing module is used to smooth the first pulse count and each of the second pulse counts respectively to obtain the first average energy pulse count corresponding to the total table and the second average energy pulse count corresponding to each sub-table; The calculation module is used to input the first average energy pulse count and each of the second average energy pulse counts into a preset misalignment analysis model for solution, thereby obtaining the line loss rate, fixed loss, and several sub-meter errors. The misalignment analysis model is constructed based on the mathematical relationship between the total meter power supply, sub-meter power consumption, initial line loss, and initial fixed loss. Specifically, the expression of the misalignment analysis model is as follows: In the formula, It is the second average number of energy pulses after multi-cycle weighted averaging of each sub-meter. These are the pulse constants of each sub-table, where j = 1, 2, ..., p; It is the first average energy pulse count of the total meter reading after multi-cycle weighted averaging. It is the pulse constant of the main meter in the meter box; These are the meter readings from the same electricity meter at different time series. The error of each of the sub-tables is mentioned; The line loss; The fixed loss; The total number of equations; The verification module is used to determine the inaccurate sub-meters that need to be calibrated based on several sub-meter errors, and to verify the inaccurate sub-meters based on the line loss rate and the fixed loss. Specifically, the step of verifying the inaccurate sub-meter based on the line loss rate and the fixed loss involves: obtaining the first metering value of the inaccurate sub-meter, the second metering values ​​of other sub-meters, and the total power supply of the meter; 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 power supply of the meter, the line loss rate, and the fixed loss; if the verification passes, then verifying the inaccurate sub-meter based on the true metering value.

6. The energy meter misalignment verification system based on energy pulse acquisition according to claim 5, 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 used to synchronize the first pulse count and each of the second pulse counts in time, and obtain the first pulse data corresponding to the master 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 of the second pulse data to obtain the first preprocessing result corresponding to the general table and the second preprocessing result corresponding to each sub-table; The third processing unit is used to smooth the first preprocessing result and each of the second preprocessing results using a multi-cycle weighted average algorithm to obtain the first average number of power pulses corresponding to the total table and the second average number of power pulses corresponding to each sub-table.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for verifying the inaccuracy of an energy meter based on energy pulse acquisition as described in any one of claims 1-4.

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