Electric energy meter corrects electric quantity calculation method and device, electronic equipment and storage medium

Abstract

The application provides a power meter correction electric quantity calculation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring metering history data of a target power meter; analyzing the metering history data by using a phase angle feature portrait algorithm, and identifying a wiring error type corresponding to the target power meter; determining a corresponding correction transformation matrix based on the wiring error type; and calculating the correction electric quantity of the target power meter based on the correction transformation matrix. The application provides a novel power meter correction electric quantity calculation scheme. By using an advanced algorithm and a mathematical model, the correction electric quantity of the power meter can be more accurately calculated, the interference of human factors is reduced, various complex wiring error conditions can be adapted to, and therefore the accuracy and reliability of the power system power meter correction electric quantity calculation method are improved.

Description

Technical Field

[0001] This application relates to the field of power calculation technology, and in particular to a method, apparatus, electronic device and storage medium for calculating corrected electricity consumption of an electricity meter. Background Technology

[0002] With the continuous expansion and intelligentization of power systems, the accuracy of electricity meters, as key devices for measuring the energy consumption of electrical equipment, is crucial for both power suppliers and consumers. However, in practical applications, incorrect wiring of electricity meters is a common problem, leading to inaccurate energy measurement and consequently affecting the fairness of electricity trading and the economic efficiency of the power system.

[0003] Currently, the widely used method by power supply companies for calculating the corrected electricity meter wiring is the "correction coefficient method." This method is based on an ideal electricity meter working model and calculates a correction coefficient to correct the metering deviation caused by wiring errors. The correction coefficient is obtained by comparing the correct power and the incorrect power, and then the amount of electricity to be supplemented or refunded is calculated using this coefficient.

[0004] However, existing methods for calculating correction quantities have some limitations: they are mainly applicable to balanced three-phase loads, and their correction effect is poor for unbalanced loads or special wiring configurations; furthermore, under certain types of wiring errors, such as mismatched current and voltage, the correction factor may not be able to be calculated, resulting in an inability to obtain a valid correction quantity. Additionally, since the calculation of the correction factor depends on the selection of the average power factor, different selections can lead to significant differences in the correction value, increasing the influence of human factors and reducing the reliability of the calculation results. Therefore, existing methods for calculating correction quantities have low accuracy and reliability, and poor applicability.

[0005] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0006] To address the aforementioned technical problems, this application provides a method, apparatus, electronic device, and storage medium for calculating corrected electricity meter readings, which solves the problems of low accuracy and low reliability in the prior art. It calculates the corrected electricity meter readings more accurately, reduces interference from human factors, and can adapt to various complex wiring errors, thereby improving the accuracy and reliability of calculating corrected electricity meter readings in the power system.

[0007] To address the aforementioned technical problems, this application provides a method for calculating corrected electricity consumption for electricity meters, comprising the following steps:

[0008] Obtain historical metering data from the target electricity meter;

[0009] The phase angle feature profiling algorithm is used to analyze the historical metering data to identify the wiring error type corresponding to the target energy meter;

[0010] Based on the type of wiring error, determine the corresponding correction transformation matrix;

[0011] Based on the correction transformation matrix, the corrected energy amount of the target energy meter is calculated.

[0012] Furthermore, in some embodiments, the phase angle feature profiling algorithm is constructed in the following ways:

[0013] Obtain operational data from multiple different types of electricity meters;

[0014] The running data is preprocessed to obtain preprocessed running data;

[0015] Extract the corresponding phase angle features and power features from the preprocessed operating data;

[0016] The phase angle features and power features are classified according to different wiring types to construct a typical phase angle feature learning set;

[0017] The preset machine learning algorithm is trained based on the typical phase angle feature learning set to obtain the phase angle feature profiling algorithm.

[0018] Furthermore, in some embodiments, the step of using a phase angle feature profiling algorithm to analyze the historical metering data and identify the wiring error type corresponding to the target energy meter includes:

[0019] The phase angle feature profiling algorithm is used to extract features from the historical metering data to obtain the phase angle features and power features of the target energy meter;

[0020] The phase angle feature and the power feature are integrated to obtain a comprehensive feature vector;

[0021] The comprehensive feature vector is matched with the typical phase angle features in the typical phase angle feature learning set to obtain several matching combinations with the highest matching degree;

[0022] Calculate the Euclidean distances corresponding to the several matching results respectively, and take the matching combination with the smallest Euclidean distance as the voltage wiring combination corresponding to the target energy meter to generate the matching result;

[0023] The wiring error type of the target energy meter is determined based on the matching results.

[0024] Furthermore, in some embodiments, determining the corresponding correction transformation matrix based on the wiring error type includes:

[0025] Based on the wiring error type, construct a correction model corresponding to the target energy meter;

[0026] The corresponding correction transformation matrix is ​​determined based on the correction model.

[0027] Furthermore, in some embodiments, constructing a correction model corresponding to the target energy meter based on the wiring error type includes:

[0028] Determine the standard phase angle of the target energy meter under correct wiring conditions;

[0029] Determine the actual phase angle of the target energy meter based on the type of wiring error;

[0030] Calculate the phase difference between the standard phase angle and the actual phase angle;

[0031] Based on the phase difference, a corresponding correction transformation matrix is ​​constructed as the correction model corresponding to the target energy meter.

[0032] Furthermore, in some embodiments, determining the corresponding correction transformation matrix based on the correction model includes:

[0033] Calculate the theoretical power of the target energy meter under correct wiring conditions;

[0034] The correction transformation matrix of the target energy meter is determined based on the type of wiring error.

[0035] The actual power is corrected based on the correction transformation matrix to obtain the corrected actual measured power.

[0036] Furthermore, in some embodiments, calculating the corrected electricity amount of the target energy meter based on the corrected transformation matrix includes:

[0037] Determine the time interval for recording data from the target electricity meter;

[0038] Collect current and voltage data from the target energy meter in each time interval;

[0039] Based on the current and voltage data collected from the target energy meter in each time interval, the metered power data for each time interval is corrected by a correction transformation matrix to obtain the corrected actual measured power.

[0040] The amount of electricity generated by the target energy meter in each time interval is calculated based on the actual measured power, and the corrected amount of electricity generated in each time interval is obtained.

[0041] The total corrected electricity consumption for the entire billing cycle is obtained by summing up the electricity consumption for each corrected time interval.

[0042] Accordingly, this application also provides a device for calculating corrected electricity consumption for an electricity meter, characterized in that it includes:

[0043] The acquisition module is used to acquire the historical metering data of the target electricity meter;

[0044] The analysis module is used to analyze the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter;

[0045] The determination module is used to determine the corresponding correction transformation matrix based on the wiring error type;

[0046] The calculation module is used to calculate the corrected energy amount of the target energy meter based on the correction transformation matrix.

[0047] This application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of the electricity meter correction calculation method as described above.

[0048] This application also provides a storage medium storing a computer program that can be loaded by a processor and executed as described above for the method of calculating corrected electricity consumption in an energy meter.

[0049] Implementing the embodiments of this application has the following beneficial effects:

[0050] As described above, this application provides a method, apparatus, electronic device, and storage medium for calculating corrected electricity meter charge. The method includes: acquiring historical metering data of the target electricity meter; analyzing the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target electricity meter; determining the corresponding correction transformation matrix based on the wiring error type; and calculating the corrected electricity meter charge based on the correction transformation matrix. This application's electricity meter correction charge calculation scheme, by employing advanced algorithms and mathematical models, can more accurately calculate the corrected electricity meter charge, reduce interference from human factors, thereby improving the accuracy and reliability of calculating electricity meter correction charges in power systems. Furthermore, it is applicable to various complex wiring error situations, effectively improving the applicability of the electricity meter correction charge calculation method in power systems. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0052] Figure 1 This is a flowchart illustrating the method for calculating corrected electricity consumption in an energy meter according to an embodiment of this application.

[0053] Figure 2 This is another flowchart illustrating the method for correcting electricity consumption calculation in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of a typical phase angle feature learning set provided in an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of the phase angle combination matching process provided in the embodiments of this application;

[0056] Figure 5 This is a schematic diagram of the voltage wiring combination matching process provided in the embodiments of this application;

[0057] Figure 6 This is a schematic diagram showing the correct phase angle provided in the embodiments of this application;

[0058] Figure 7 This is a schematic diagram showing the phase angle A phase polarity reversal provided in the embodiments of this application;

[0059] Figure 8 This is a schematic diagram of the minimum interval accumulation method provided in the embodiments of this application;

[0060] Figure 9 This is a schematic diagram of the structure of the energy meter correction calculation device provided in the embodiments of this application;

[0061] Figure 10 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application.

[0062] The realization of the objectives, functional features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0064] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0065] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0066] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0067] Due to various objective and subjective factors, wiring errors in metering devices occur frequently. These errors take many forms and can cause the meter to run fast or slow, or even stop. When a wiring error occurs, the incorrect wiring should first be corrected, followed by calculation of the erroneous charge amount to provide timely information to the power company and customers. Currently, power companies generally use the "correction coefficient method" to calculate the erroneous charge amount. Assuming the erroneous charge is Wc, the correction coefficient K is derived from the power or power expression, and the charge amount to be compensated, Wb, can be calculated. The calculation process is as follows: Correction coefficient K = Correct power / Erroneous power; Charge amount to be compensated Wb = (|K|-1)Wc. As can be seen from its calculation principle, the existing technology has the following defects: (1) The correction coefficient method is only applicable to the case of three-phase load balance; (2) Some error types (such as the correction coefficient being infinite when the current and voltage do not correspond) cannot be calculated; (3) The average power factor during a certain period of normal operation is generally used as the basis for calculating the correction coefficient, but the results of the calculation differ by a factor of 10 when the average power factor is selected as 0.7 or 0.9.

[0068] To address the aforementioned technical problems, this application provides a method, apparatus, electronic device, and storage medium for calculating corrected electricity consumption in an electricity meter. By employing advanced algorithms and mathematical models, it can more accurately calculate the corrected electricity consumption of the electricity meter, reduce interference from human factors, and adapt to various complex wiring errors, thereby improving the accuracy and reliability of electricity calculation.

[0069] This application provides a method, apparatus, electronic device, and storage medium for calculating corrected electricity consumption in an electricity meter.

[0070] The energy meter correction calculation device can be deployed in a terminal or a server. The server can include a standalone server or a distributed server, or a server cluster consisting of multiple servers. The terminal can include a mobile phone, tablet computer or personal computer (PC).

[0071] The following sections provide detailed descriptions of each example. It should be noted that the order in which the embodiments are described is not intended to limit the priority of the embodiments.

[0072] This application provides a method for calculating the correction amount of an electricity meter, comprising: acquiring the metering history data of the target electricity meter; analyzing the metering history data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target electricity meter; determining the corresponding correction transformation matrix based on the wiring error type; and calculating the correction amount of the target electricity meter based on the correction transformation matrix.

[0073] Please see Figure 1 , Figure 1 This is a flowchart illustrating the method for calculating corrected electricity consumption for an electricity meter according to an embodiment of this application. The method for calculating corrected electricity consumption for an electricity meter provided in this embodiment can be executed in a power supply system, and specifically includes the following steps:

[0074] S1. Obtain the historical metering data of the target electricity meter;

[0075] Specifically, for step S1, this step mainly involves collecting historical metering data such as electricity consumption, current, voltage, and power factor stored in the electricity meter register. This data is usually automatically recorded by the electricity meter and can be read in real time or periodically through communication interfaces (such as RS485, Ethernet, wireless communication, etc.).

[0076] S2. The phase angle feature profiling algorithm is used to analyze the historical metering data and identify the wiring error type corresponding to the target energy meter;

[0077] Specifically, for step S2, the self-developed phase angle feature profiling algorithm is used to analyze the collected historical data to identify the wiring error types of the electricity meter. This algorithm can be a machine learning-based model that identifies different wiring error features by training a learning set, which includes electricity meter data with normal wiring and various incorrect wiring, as well as typical phase angle feature sets.

[0078] S3. Determine the corresponding correction transformation matrix based on the wiring error type;

[0079] Specifically, for step S3, by analyzing the wiring error type, the corresponding model of the algorithm is used to determine the correction transformation matrix. This step requires constructing a correction model, which builds the correction transformation matrix based on the phase difference between the standard phase angle and the actual phase angle. This matrix can correct the power calculation based on the actual wiring error.

[0080] S4. Based on the correction transformation matrix, calculate the corrected energy amount of the target energy meter;

[0081] Specifically, for step S4, the corrected power is calculated for the current and voltage data in each time interval using the corrected transformation matrix determined in step 3. This process may involve calculating and correcting the power consumption for each 15-minute interval (or a smaller time interval), and then accumulating these corrected power consumptions to obtain the total corrected power consumption for the entire billing cycle.

[0082] As can be seen, the electricity meter correction calculation method provided in this embodiment, by using a more advanced algorithm and correction model, can more accurately calculate the electricity deviation caused by wiring errors; the automated calculation method reduces the interference of human factors and improves the objectivity and fairness of electricity calculation; it provides a more scientific and reliable electricity correction method, which helps to increase customers' trust in the accuracy of the electricity correction calculation by the power supply department; accurate correction electricity calculation provides strong technical support for the power supply department in the process of claiming compensation for electricity, and enhances legal effect; the automated method reduces the workload and time of manual calculation and improves work efficiency; this method is not limited to a specific type of electricity meter, but can be widely applied to various types of electricity meters, and has strong adaptability and versatility.

[0083] Furthermore, in some embodiments, the phase angle feature profiling algorithm in this embodiment is constructed in the following ways:

[0084] Obtain operational data from multiple different types of electricity meters;

[0085] Perform data preprocessing on the running data to obtain preprocessed running data;

[0086] Extract the corresponding phase angle features and power features from the preprocessed operating data;

[0087] The phase angle features and power features are classified according to different wiring types, and a typical phase angle feature learning set is constructed.

[0088] The phase angle feature profiling algorithm is obtained by training a preset machine learning algorithm based on a typical phase angle feature learning set.

[0089] Specifically, this embodiment also provides a method for constructing a phase angle feature profiling algorithm, with the following steps: First, acquire operational data from multiple different types of electricity meters. This operational data, including voltage, current, power factor, and power, can come from electricity meters of different manufacturers and models to ensure the algorithm's generalization ability and adaptability. Next, perform data preprocessing on the operational data. Clean and format the collected raw data for subsequent analysis. Preprocessing may include denoising, outlier handling, data standardization, and missing value handling. Then, extract phase angle and power features from the preprocessed data. Key features, such as phase angle and power, can be extracted. Feature extraction can use methods such as Fourier transform and wavelet transform to identify periodic changes and important frequency components. Finally, classify and construct a typical phase angle feature learning set. Based on different wiring types of the electricity meters, classify the extracted features and construct a learning set. The learning set includes data on normal wiring and various known incorrect wiring types, used to train the machine learning model. Finally, the constructed learning set is used to train a pre-defined machine learning algorithm to identify different types of wiring errors. The algorithms used include Support Vector Machines (SVM), Random Forests, and Neural Networks, which are capable of learning from large amounts of data and recognizing complex patterns.

[0090] This embodiment uses machine learning algorithms to more accurately identify wiring error types in electricity meters; by collecting and analyzing data from various types of electricity meters, the algorithm can adapt to different electricity meters and environmental conditions; automated feature extraction and classification reduce human intervention, improving the efficiency and consistency of data processing; it reduces the impact of human factors in data preprocessing and feature extraction, improving data quality; automated preprocessing and feature extraction significantly improve the speed and accuracy of data processing; and by training with data from various types of electricity meters, the algorithm can better adapt to new or unknown electricity meter types.

[0091] In a specific embodiment, the typical phase angle feature learning set is the core technology of this embodiment. The typical phase angle feature learning set is constructed based on the relevant principles of voltage, current, and power phase angles in power systems, the relevant principles of electricity meter metering, and business rules. The generated data fully reflects the correspondence between the power values ​​of each component of three-phase three-wire and three-phase four-wire meters under theoretical conditions, as well as the voltage combination relationships and current combination relationships. The concept of the learning set is as follows: Figure 3 As shown, the learning set contains subsets of 6 voltage combinations based on their respective active power, reactive power, component angles, and power factor angles of phase voltage and phase current, forming a corresponding relationship with the metering data of active power and reactive power.

[0092] Furthermore, in some embodiments, step S2, "analyzing historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter," may specifically include:

[0093] S21. The phase angle feature profiling algorithm is used to extract features from the historical metering data to obtain the phase angle features and power features of the target energy meter;

[0094] S22. Integrate the phase angle features and power features to obtain a comprehensive feature vector;

[0095] S23. Match the comprehensive feature vector with the typical phase angle features in the typical phase angle feature learning set to obtain several matching combinations with the highest matching degree;

[0096] S24. Calculate the Euclidean distances corresponding to several matching results respectively, and take the matching combination with the smallest Euclidean distance as the voltage wiring combination corresponding to the target energy meter to generate the matching result;

[0097] S25. Determine the wiring error type of the target energy meter based on the matching results.

[0098] Specifically, for step S2, firstly, a phase angle feature profiling algorithm is used to extract phase angle features and power features from historical metering data. Feature extraction includes calculating the phase difference between voltage and current waveforms and analyzing power data to identify load characteristics. Then, the extracted phase angle and power features are integrated into a comprehensive feature vector. This integration involves normalization to ensure comparability between different features. Next, the comprehensive feature vector is matched against features in a typical phase angle feature learning set. The matching process can use pattern recognition techniques, such as nearest neighbor algorithms, to find the most similar feature combinations. Euclidean distance is calculated on the matching results to evaluate the similarity between the feature vector and features in the learning set. The calculation of Euclidean distance may involve distance measurements in multi-dimensional space to determine the closest match. Finally, the wiring error type of the electricity meter is determined based on the matching results.

[0099] This embodiment can accurately identify the wiring error type of the electricity meter through feature extraction and matching; the automated analysis process reduces manual intervention and improves processing speed and consistency; data-based feature matching and distance calculation provide a more objective basis for decision-making; the use of comprehensive feature vectors reduces the misjudgment that may be caused by a single feature; the fast feature extraction and matching algorithm improves the efficiency of the overall processing flow; the algorithm can adapt to different electricity meters and metering conditions and has strong versatility.

[0100] In specific embodiments, such as Figure 4As shown, the collected voltage, current, and power values ​​from the electricity meter are converted into an array [B1]. This array is then trained against a typical phasor graph library of six voltage reference combinations, resulting in six training results corresponding to the six phase angle combinations with the highest matching degree. Figure 5 As shown, the six learning and training results are judged using Euclidean distance values, and the voltage wiring combination of the energy meter corresponding to array [B1] is determined by the minimum Euclidean distance value.

[0101] Furthermore, in some embodiments, step S3, "determining the corresponding correction transformation matrix based on the wiring error type," may specifically include:

[0102] S31. Based on the wiring error type, construct the correction model corresponding to the target energy meter;

[0103] S32. Determine the corresponding correction transformation matrix based on the correction model.

[0104] Specifically, for step S3, a correction model is constructed based on the type of wiring error in the electricity meter. This model describes the impact of incorrect wiring on electricity meter measurement. The correction model is based on physical laws, circuit theory, and the working principle of the electricity meter, considering factors such as the phase relationship of voltage and current, and the power factor. A correction transformation matrix is ​​used to construct the correction model, which is used to correct the metering data of the electricity meter to compensate for the metering deviation caused by the wiring error. The process of determining the correction transformation matrix includes algorithmic calculations such as matrix operations and optimization algorithms. Constructing the correction model requires determining the standard phase angle of the electricity meter under correct wiring conditions and calculating the phase difference with the actual phase angle. This phase difference is used to construct the wiring transformation matrix, which is the core part of the correction model. The calculation of the correction transformation matrix includes calculating the difference between theoretical power and actual power, and using the wiring transformation matrix to correct the actual power, thereby obtaining a more accurate measurement of electricity consumption.

[0105] This embodiment, by constructing a correction model and calculating the correction transformation matrix, can more accurately calculate the electrical quantity error caused by wiring errors. The correction model provides a scientific theoretical basis, making the correction process more reasonable and reliable. The automated calculation of the correction transformation matrix reduces the interference of human factors and improves the objectivity and fairness of electrical quantity calculation. This method can adapt to different types of wiring errors and has good versatility and flexibility. The automated calculation process of the correction transformation matrix improves the efficiency of electrical quantity correction and reduces the workload of manual calculation.

[0106] Furthermore, in some embodiments, step S31, "constructing a correction model corresponding to the target energy meter based on the wiring error type," may specifically include:

[0107] S311. Determine the standard phase angle of the target energy meter under correct wiring conditions;

[0108] S312. Determine the actual phase angle of the target energy meter based on the type of wiring error;

[0109] S313. Calculate the phase difference between the standard phase angle and the actual phase angle;

[0110] S314. Construct the corresponding wiring transformation matrix based on the phase difference, as the correction model corresponding to the target energy meter.

[0111] Specifically, for step S31, firstly, the standard phase angle of the electricity meter under correct wiring conditions is determined. This typically includes the correct ideal or expected phase relationship of the voltage and current waveforms measured by the meter's metering elements, which can be referenced from the electricity meter's technical specifications or standard data provided by the manufacturer. Then, based on the type of wiring error, the actual phase angle of the electricity meter during operation is determined. Determining the actual phase angle involves on-site measurement or inference through analysis of historical data using machine learning algorithms. The difference between the standard phase angle and the actual phase angle is calculated to obtain the phase difference. The calculation process for the phase difference includes trigonometric functions and vector analysis to determine the exact phase relationship between the voltage and current waveforms. Finally, a correction transformation matrix is ​​constructed using the calculated phase difference. This matrix is ​​used to correct the metering data of the electricity meter. The correction transformation matrix is ​​a mathematical tool used to describe and correct deviations in electricity calculations caused by wiring errors.

[0112] This embodiment can accurately correct erroneous metering data caused by wiring errors by precisely calculating the phase difference and constructing a correction transformation matrix. Using scientific methods to determine the phase angle and construct the correction model improves the accuracy and reliability of the correction process. The automated calculation process reduces manual intervention, improving processing speed and consistency. Precise phase difference calculation reduces metering errors caused by wiring errors. Rapid phase difference calculation and construction of the wiring transformation matrix improve the efficiency of the overall processing flow. This method is adaptable to different types of electricity meters and wiring errors, demonstrating strong versatility.

[0113] Furthermore, in some embodiments, step S32, "determining the corresponding correction transformation matrix based on the correction model," may specifically include:

[0114] S321. Calculate the theoretical power of the target energy meter under correct wiring conditions;

[0115] S322. Determine the correction transformation matrix of the target energy meter based on the wiring error type;

[0116] S323. Correct the actual power based on the correction transformation matrix to obtain the corrected actual measured power.

[0117] Specifically, for step S32, the theoretical power is calculated using a preset formula based on the standard phase angle of the energy meter under correct wiring conditions and the known voltage and current values. The calculation of theoretical power considers the phase relationship and power factor in a three-phase power system. The actual correct power is calculated by correcting the calculation based on the actual wiring error type of the energy meter and the measured voltage and current values. The calculation of the actual correct power needs to consider the phase angle change caused by the wiring error. A correction transformation matrix is ​​used to correct the actual power to simulate the power measurement value if the energy meter is correctly wired. The correction process involves matrix operations, mapping the power vector calculated from the actual measured current and voltage to the corrected power vector. The correction transformation matrix can be expressed as a matrix relationship:

[0118]

[0119] The correction transformation matrix reflects the transformation relationship between the actual measured value and the theoretical value, and is used to correct the calculated power value of the electricity meter.

[0120] This embodiment corrects metering deviations caused by incorrect meter wiring by adjusting the actual power. The method is adaptable to various types of wiring errors, providing customized correction transformation matrices. The automated calculation process improves the efficiency of power correction and reduces the workload of manual calculations. It offers a more scientific and reliable method for power correction, helping to increase customer trust in the accuracy of power supply department calculations. Accurate correction transformation matrix calculations provide strong technical support for power supply departments in providing evidence for claiming additional power consumption.

[0121] In a specific embodiment, the principle of the wiring transformation matrix is ​​as follows: Figure 6 As shown, under normal wiring conditions for the electricity meter, the phase diagram is as follows: Figure 6 As shown in the diagram with correct phase angle, the phase angle between U1 and I1 is... The phase angle between U2 and I2 is The phase angle between U3 and I3

[0122] When the electricity meter is wired incorrectly, the polarity of phase A is reversed, such as... Figure 7 (Phase angle A is reversed) indicates that the phase angle between U1 and I1 becomes... In other words, if the electricity meter is wired incorrectly with phase A reversed, from a phase angle perspective, the phase angle between U1 and I1 will change from the original... Increased by 180° (i.e.) The wiring of U2 and I3 is correct, and the phase angle remains unchanged. Based on comparison and multiple verifications, a wiring transformation matrix is ​​compiled.

[0123] Under normal circumstances, when the voltage combination of a three-phase three-wire energy meter is ABC and the current combination is Ia Ic, the correction transformation matrix is ​​as follows:

[0124]

[0125] When the voltage combination is ABC and the current combination is -Ia, Ic, the correction transformation matrix is ​​as follows:

[0126]

[0127] Furthermore, in some embodiments, step S4, "calculating the corrected amount of the target energy meter based on the correction transformation matrix," may specifically include:

[0128] S41. Determine the time interval for recording data from the target electricity meter;

[0129] S42. Collect current and voltage data of the target energy meter in each time interval;

[0130] S43. Based on the current and voltage data of the target energy meter in each time interval, the metered power data of each time interval is corrected by a correction transformation matrix to obtain the corrected actual measured power;

[0131] S44. Calculate the electricity consumption of the target energy meter in each time interval based on the actual measured power, and obtain the corrected electricity consumption for each time interval;

[0132] S45. Sum up the electricity consumption of each corrected time interval to obtain the total corrected electricity consumption for the entire billing cycle.

[0133] Specifically, for step S4, the time interval for recording electricity meter data is determined. This is usually a fixed interval set by the electricity meter or related metering system, such as recording data every 15 minutes. Determining the time interval is fundamental to electricity consumption calculation and must be matched to the electricity meter's data recording frequency. Within each time interval, metering data is collected, including voltage, current, and power factor. The collected data needs to be sufficiently accurate and complete for subsequent electricity consumption calculations. The electricity consumption for each time interval is calculated using the collected metering data, based on the electricity meter's operating principle. Electricity consumption calculation involves complex energy conversion formulas and must consider meter measurement errors and environmental factors. A correction transformation matrix is ​​applied to correct the electricity consumption for each time interval to compensate for metering deviations caused by wiring errors or other factors. The correction process requires ensuring the accurate application of the correction transformation matrix and necessitates multiple iterative calculations to ensure the accuracy of the results. The corrected electricity consumption for all time intervals is accumulated to obtain the total corrected electricity consumption for the entire billing cycle. The accumulation process must ensure the accuracy and completeness of the data to avoid deviations in the total electricity consumption calculation.

[0134] This embodiment, by applying a correction transformation matrix, can more accurately calculate the corrected electricity consumption of the electricity meter, correcting metering deviations caused by the meter wiring. The automated electricity consumption calculation and correction process improves processing speed and consistency, reducing manual intervention. Calculations based on actual metering data provide a more objective basis for decision-making, enhancing the reliability of the corrected electricity consumption calculation. The automated calculation process reduces the impact of human factors in the corrected electricity consumption calculation process. Rapid corrected electricity consumption calculation improves the efficiency of the overall processing flow and reduces operating costs. It provides a more scientific and reliable electricity consumption correction method, helping to increase customer trust in the accuracy of the power supply department's corrected electricity consumption calculations.

[0135] In a specific embodiment, this embodiment uses the minimum interval accumulation method to calculate the total corrected electricity consumption for the entire billing cycle. The minimum interval accumulation method is a calculation method derived from the principles of calculus in mathematics, combined with the data granularity collected by the metering system (one record per user every 15 minutes). For example... Figure 8 As shown, P represents the component power, which is calculated by multiplying the voltage value collected by the metering automation system by the current value, and then multiplying by the angle between the voltage and current, according to the following formula:

[0136]

[0137] In the formula: The value is the inverse cosine of the collected power factor, where P is the component power, U is the voltage value, and I is the current value.

[0138] The total energy consumption W calculated using the minimum interval accumulation method is equal to the energy consumption over the time interval from t0 to tn. The energy consumption values ​​are accumulated at 15-minute intervals, with n 15-minute intervals. The calculation is performed using the following formula:

[0139]

[0140] In the formula, U is the collected voltage value, I is the collected current value, P is the active power of the energy meter, W is the energy value calculated within the specified time period, and Δt=tk-tk-1 represents the time interval, which is the minimum interval for data collection by the metering system, 15 minutes, and the current minimum time interval for data storage by the energy meter, 15 minutes.

[0141] In summary, the method for calculating corrected electricity meter charge in this embodiment includes: acquiring historical metering data such as current, voltage, power, and meter readings of the target electricity meter; analyzing the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target electricity meter; determining the corresponding correction transformation matrix based on the wiring error type; and calculating the corrected electricity meter charge based on the correction transformation matrix. It is evident that the electricity meter correction charge calculation scheme provided in this embodiment, by employing advanced algorithms and mathematical models, can more accurately calculate the corrected electricity meter charge, reduce interference from human factors, thereby improving the accuracy and reliability of electricity meter correction charge calculation in power systems. Furthermore, it is applicable to various complex wiring error situations, effectively improving the applicability of the electricity meter correction charge calculation method in power systems.

[0142] To facilitate better implementation of the electricity meter correction calculation method of this application embodiment, this application embodiment also provides an electricity meter correction calculation device. The meanings of the terms used are the same as in the above-described electricity meter correction calculation method, and specific implementation details can be found in the description of the method embodiment.

[0143] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of the electricity meter correction power calculation device provided in an embodiment of this application. Specifically, the electricity meter correction power calculation device may include an acquisition module 201, an analysis module 202, a determination module 203, and a calculation module 204, as follows:

[0144] The acquisition module 201 is used to acquire the historical metering data of the target electricity meter;

[0145] Analysis module 202 is used to analyze historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter;

[0146] The determination module 203 is used to determine the corresponding correction transformation matrix based on the wiring error type;

[0147] The calculation module 204 is used to calculate the corrected energy amount of the target energy meter based on the correction transformation matrix.

[0148] Furthermore, in some embodiments, the phase angle feature profiling algorithm is constructed in the following ways:

[0149] Obtain operational data from multiple different types of electricity meters;

[0150] Perform data preprocessing on the running data to obtain preprocessed running data;

[0151] Extract the corresponding phase angle features and power features from the preprocessed operating data;

[0152] The phase angle features and power features are classified according to different wiring types, and a typical phase angle feature learning set is constructed.

[0153] The phase angle feature profiling algorithm is obtained by training a preset machine learning algorithm based on a typical phase angle feature learning set.

[0154] Furthermore, in some embodiments, the analysis module 202 is specifically used for:

[0155] A phase angle feature profiling algorithm is used to extract features from historical metering data to obtain the phase angle features and power features of the target energy meter;

[0156] By integrating the phase angle features and power features, a comprehensive feature vector is obtained;

[0157] The comprehensive feature vector is matched with the typical phase angle features in the typical phase angle feature learning set to obtain several matching combinations with the highest matching degree.

[0158] Calculate the Euclidean distances corresponding to several matching results, and take the matching combination with the smallest Euclidean distance as the voltage wiring combination corresponding to the target energy meter to generate the matching result;

[0159] The type of wiring error in the target energy meter is determined by the matching results.

[0160] Furthermore, in some embodiments, the determining module 203 is specifically used for:

[0161] Based on the type of wiring error, construct the correction model corresponding to the target energy meter;

[0162] The corresponding correction transformation matrix is ​​determined based on the correction model.

[0163] Furthermore, in some embodiments, the determining module 203 is specifically used for:

[0164] Determine the standard phase angle of the target energy meter under correct wiring conditions;

[0165] Determine the actual phase angle of the target energy meter based on the type of wiring error;

[0166] Calculate the phase difference between the standard phase angle and the actual phase angle;

[0167] The corresponding correction transformation matrix is ​​constructed based on the phase difference, which serves as the correction model for the target energy meter.

[0168] Furthermore, in some embodiments, the determining module 203 is specifically used for:

[0169] Calculate the theoretical power of the target energy meter under correct wiring conditions;

[0170] Based on the wiring error type and the voltage and current values ​​measured by the target energy meter, a correction transformation matrix is ​​used to correct the actual power to obtain the actual power of the target energy meter.

[0171] Furthermore, in some embodiments, the calculation module 204 is specifically used for:

[0172] Determine the time interval for recording data from the target electricity meter;

[0173] Collect the current, voltage, and data of the target energy meter in each time interval;

[0174] Based on the current, voltage and data of the target energy meter in each time interval, the metered power data of each time interval is corrected by a correction transformation matrix to obtain the corrected actual measured power.

[0175] Based on the corrected actual measured power data, the power consumption of the target energy meter in each time interval is calculated to obtain the corrected power consumption for each time interval;

[0176] The total corrected electricity consumption for the entire billing cycle is obtained by summing up the electricity consumption for each corrected time interval.

[0177] In summary, the electricity meter correction calculation device provided in this embodiment acquires the historical metering data of the target electricity meter through the acquisition module 201; analyzes the historical metering data using a phase angle feature profiling algorithm through the analysis module 202 to identify the wiring error type corresponding to the target electricity meter; determines the corresponding correction transformation matrix based on the wiring error type through the determination module 203; and calculates the correction amount of the target electricity meter based on the correction transformation matrix through the calculation module 204. It is evident that this embodiment, by employing advanced algorithms and mathematical models, can more accurately calculate the correction amount of the electricity meter, reduce interference from human factors, thereby improving the accuracy and reliability of power system correction amount calculation, and is applicable to various complex wiring error situations, effectively improving the applicability of the power system electricity meter correction amount calculation method.

[0178] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 10 The diagram illustrates the structure of an electronic device according to an embodiment of this application. Specifically, the electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 10 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0179] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0180] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and electricity meter correction calculation methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0181] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0182] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0183] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows:

[0184] Obtain historical metering data of the target energy meter; analyze the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter; determine the corresponding correction transformation matrix based on the wiring error type; and calculate the correction amount of the target energy meter based on the correction transformation matrix.

[0185] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0186] This application's embodiments, by employing advanced algorithms and mathematical models, can more accurately calculate the corrected amount of electricity meter readings, reduce interference from human factors, thereby improving the accuracy and reliability of calculating electricity meter corrected amounts in power systems. Furthermore, it is applicable to various complex wiring error situations, effectively enhancing the applicability of the method for calculating electricity meter corrected amounts in power systems.

[0187] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0188] Therefore, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the electricity meter correction calculation methods provided in this application. For example, the instructions can execute the following steps:

[0189] Obtain historical metering data of the target energy meter; analyze the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter; determine the corresponding correction transformation matrix based on the wiring error type; and calculate the correction amount of the target energy meter based on the correction transformation matrix.

[0190] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0191] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk, or optical disk, etc. Since the instructions stored in the storage medium can execute the steps in any of the electricity meter correction calculation methods provided in the embodiments of this application, the beneficial effects achievable by any of the electricity meter correction calculation methods provided in the embodiments of this application can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0192] The foregoing has provided a detailed description of the method, apparatus, electronic device, and storage medium for calculating corrected electricity consumption in an energy meter, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for calculating corrected electricity consumption in an electricity meter, characterized in that, Includes the following steps: Obtain historical metering data from the target electricity meter; A phase angle feature profiling algorithm is used to analyze the historical metering data to identify the wiring error type corresponding to the target energy meter. The construction method of the phase angle feature profiling algorithm includes: acquiring operating data from multiple different types of energy meters; preprocessing the operating data to obtain preprocessed operating data; extracting corresponding phase angle features and power features from the preprocessed operating data; classifying the phase angle features and power features according to different wiring types to construct a typical phase angle feature learning set; and training a preset machine learning algorithm based on the typical phase angle feature learning set to obtain the phase angle feature profiling algorithm. Based on the type of wiring error, determine the corresponding correction transformation matrix; The calculation of the corrected electricity consumption of the target energy meter based on the correction transformation matrix includes: determining the time interval for data recording of the target energy meter; collecting current and voltage data of the target energy meter in each time interval; correcting the metered power data of each time interval using the correction transformation matrix based on the collected current and voltage data of the target energy meter in each time interval to obtain the corrected actual measured power; calculating the electricity consumption of the target energy meter in each time interval based on the actual measured power to obtain the corrected electricity consumption of each time interval; and summing up the electricity consumption of all corrected time intervals to obtain the total corrected electricity consumption for the entire billing cycle.

2. The method for calculating corrected electricity consumption for an electricity meter according to claim 1, characterized in that, The phase angle feature profiling algorithm is used to analyze the historical metering data and identify the wiring error type corresponding to the target electricity meter, including: The phase angle feature profiling algorithm is used to extract features from the historical metering data to obtain the phase angle features and power features of the target energy meter; The phase angle feature and the power feature are integrated to obtain a comprehensive feature vector; The comprehensive feature vector is matched with the typical phase angle features in the typical phase angle feature learning set to obtain several matching combinations with the highest matching degree; Calculate the Euclidean distances corresponding to the several matching combinations, and take the matching combination with the smallest Euclidean distance as the voltage wiring combination corresponding to the target energy meter to generate the matching result; The wiring error type of the target energy meter is determined based on the matching results.

3. The method for calculating corrected electricity consumption for an electricity meter according to claim 1, characterized in that, The step of determining the corresponding correction transformation matrix based on the wiring error type includes: Based on the wiring error type, construct a correction model corresponding to the target energy meter; The corresponding correction transformation matrix is ​​determined based on the correction model.

4. The method for calculating corrected electricity consumption for an electricity meter according to claim 3, characterized in that, The step of constructing a correction model corresponding to the target electricity meter based on the wiring error type includes: Determine the standard phase angle of the target energy meter under correct wiring conditions; Determine the actual phase angle of the target energy meter based on the type of wiring error; Calculate the phase difference between the standard phase angle and the actual phase angle; Based on the phase difference, a corresponding correction transformation matrix is ​​constructed as the correction model corresponding to the target energy meter.

5. The method for calculating corrected electricity consumption for an electricity meter according to claim 4, characterized in that, Determining the corresponding correction transformation matrix based on the correction model includes: Calculate the theoretical power of the target energy meter under correct wiring conditions; The correction transformation matrix of the target energy meter is determined based on the type of wiring error. The actual measured power is corrected based on the correction transformation matrix to obtain the corrected actual measured power.

6. A device for calculating corrected electricity consumption in an electricity meter, characterized in that, include: The acquisition module is used to acquire the historical metering data of the target electricity meter; The analysis module is used to analyze the historical metering data using a phase angle feature profiling algorithm to identify the wiring error type corresponding to the target energy meter. This includes: acquiring operational data from multiple different types of energy meters; preprocessing the operational data to obtain preprocessed operational data; extracting corresponding phase angle features and power features from the preprocessed operational data; classifying the phase angle features and power features according to different wiring types to construct a typical phase angle feature learning set; and training a preset machine learning algorithm based on the typical phase angle feature learning set to obtain the phase angle feature profiling algorithm. The determination module is used to determine the corresponding correction transformation matrix based on the wiring error type; The calculation module is used to calculate the corrected electricity consumption of the target energy meter based on the correction transformation matrix, including: determining the time interval for data recording of the target energy meter; collecting current and voltage data of the target energy meter in each time interval; correcting the metered power data of each time interval using the correction transformation matrix based on the collected current and voltage data of the target energy meter in each time interval to obtain the corrected actual measured power; calculating the electricity consumption of the target energy meter in each time interval based on the actual measured power to obtain the corrected electricity consumption of each time interval; and summing up the electricity consumption of all corrected time intervals to obtain the total corrected electricity consumption for the entire billing cycle.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program as the steps of the electricity meter correction calculation method as described in any one of claims 1-5.

8. A storage medium, characterized in that, The device stores a computer program that can be loaded by a processor and executed as described in any one of claims 1-5 for correcting electricity meter calculations.

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