Method for accurately measuring voltage, current and phase angles of sectional intelligent electric energy meter

Abstract

The invention discloses a method for accurately measuring voltage, current and phase angles of a sectional intelligent electric energy meter, which relates to the technical field of electric energy meter metering and comprises the following steps of: S1, constructing a sectional load-mutual inductor angle difference correlation model, and dividing a plurality of load intervals according to an application scene of the sectional intelligent electric energy meter and working characteristics of a mutual inductor, acquiring current transformer angular difference data and voltage transformer impedance drift related data in different load intervals, and establishing and storing an association relationship between each load interval and a corresponding transformer angular difference compensation coefficient in combination with inherent parameters of the transformer; according to the invention, a segmented load-mutual inductor angle difference correlation model is constructed in advance, load voltage and current amplitude data are collected in real time to accurately determine a current load interval, and a corresponding mutual inductor angle difference compensation coefficient is automatically matched to carry out dynamic adaptive compensation on an initial phase angle. The problem of phase deviation caused by angular difference nonlinear change of a current transformer in different load intervals and impedance drift of a voltage transformer in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, specifically a method for accurately measuring the voltage, current, and phase angle of a segmented smart electricity meter. Background Technology

[0002] Smart meters are core devices in the power grid metering system, capable of accurately collecting, storing, and transmitting electricity consumption data. They serve as the fundamental support for realizing electricity market transactions, energy conservation and emission reduction monitoring, and electricity consumption behavior analysis. Their operational stability and metering accuracy directly affect the legitimate rights and interests of power supply companies and electricity users, and are of great significance for ensuring the safe and economical operation of the power grid and promoting the optimal allocation of power resources. Voltage and current phase angle measurement is one of the key core functions of smart meters. The phase angle reflects the phase correspondence between voltage and current signals in the power grid. Its measurement accuracy not only directly determines the reliability of electricity metering results, but is also an important basis for judging load electricity consumption characteristics, monitoring the power grid operating status, and diagnosing line faults. It plays an irreplaceable role in optimizing power grid dispatching schemes, improving power supply quality, and reducing power grid losses.

[0003] However, existing phase angle measurement technologies for segmented smart meters still have certain shortcomings. Current phase error compensation schemes are mostly designed for fixed load ranges, neglecting the non-linear changes in phase angle difference of current transformers across different load current ranges. Furthermore, voltage transformers are prone to impedance drift during long-term operation, leading to additional phase shifts. This results in inconsistent transformer phase angle differences when segmented smart meters switch between different operating conditions such as light load, heavy load, and no load, significantly increasing phase angle measurement errors and severely impacting the accuracy of energy metering. This fails to meet the high-precision energy metering requirements of smart grids. Therefore, developing accurate phase angle measurement methods for segmented smart meters is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a precise method for measuring the voltage and current phase angles of segmented smart energy meters. This method can accurately determine the current load range by pre-constructing a segmented load-transformer phase difference correlation model and collecting real-time load voltage and current amplitude data. It automatically matches the corresponding transformer phase difference compensation coefficient to dynamically and adaptively compensate the initial phase angle, solving the problems of nonlinear changes in current transformer phase difference across different load ranges and phase shift caused by voltage transformer impedance drift in existing technologies. It also considers the measurement needs under different operating conditions such as light load, heavy load, and no load, avoiding phase measurement errors caused by inconsistent transformer phase differences under different loads. This improves the accuracy of voltage and current phase angle measurements in segmented smart energy meters and provides a reliable guarantee for the accuracy of energy metering.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter, the method comprising the following steps: S1. Construct a segmented load-transformer angle difference correlation model. Based on the application scenario of the segmented smart energy meter and the working characteristics of the transformer, divide the load into multiple load intervals. Collect current transformer angle difference data and voltage transformer impedance drift related data in different load intervals. Combine the inherent parameters of the transformer to establish and store the correlation between each load interval and the corresponding transformer angle difference compensation coefficient. S2. Real-time acquisition of load electrical parameters, the amplitude data of voltage and current signals are acquired through the built-in sampling module of the energy meter and transmitted to the control module; S3. Determine the current load range. The control module determines the specific range where the current load is located based on the preset load range division criteria. S4. Match the current transformer angle difference compensation coefficient. The control module calls the stored association model and automatically matches the corresponding compensation coefficient according to the current load range. S5. Dynamic adaptive compensation and phase angle calculation: The control module obtains the initial phase angle based on the collected voltage and current signals, performs dynamic adaptive compensation using the matched compensation coefficient, and outputs the phase angle measurement result.

[0006] Furthermore, step S1, in constructing the segmented load-transformer angle difference correlation model, includes the following steps: Based on the rated current range of segmented smart energy meters, the load variation pattern in actual power grid operation, and the rated operating parameters of current transformers, four basic load ranges are divided: no-load range, light-load range, medium-load range, and heavy-load range. Each range has a clear current amplitude division boundary. In a laboratory environment, typical operating conditions of various load ranges are simulated. The actual phase difference data of the current transformer and the impedance change data of the voltage transformer are collected synchronously using standard measuring instruments. Multiple sets of data are collected continuously under each operating condition. A data fitting algorithm was used to analyze and process the collected data. After removing abnormal data, a mapping relationship between the load range and the transformer angle difference was established, and the transformer angle difference compensation coefficient corresponding to each load range was determined. The correspondence between load range, transformer angle difference data, and compensation coefficient is organized into a segmented load-transformer angle difference correlation model according to a preset format and stored in the non-volatile storage module of the energy meter. The transformer angle difference compensation coefficient is calculated using the following formula: ,in To provide a comprehensive transformer angle difference compensation coefficient, This is the current transformer angle difference data. This refers to the angle difference data corresponding to the impedance drift of the voltage transformer. and These are weighting coefficients, which are obtained from multiple sets of data collected in the laboratory under different load ranges. , The sample data that deviate from the actual phase measurement are fitted using the least squares method to determine the degree of influence of the two angle differences on the phase measurement.

[0007] Furthermore, step S3, in determining the current load range, includes the following steps: After receiving the voltage and current amplitude data transmitted by the sampling module, the control module performs noise reduction processing on the data to remove abnormal amplitude data caused by instantaneous fluctuations in the power grid. Extract the valid values ​​of the processed amplitude data and compare them one by one with the preset amplitude thresholds for each load range to preliminarily determine the possible range of the current load. Retrieve recent load operation data stored in the electricity meter, analyze load change trends, and revise the preliminary judgment based on trend characteristics; By combining the comparison results and trend correction results, the specific range of the current load is determined, and the range information is transmitted to the compensation coefficient matching unit.

[0008] Furthermore, step S5 includes the following steps when performing dynamic adaptive compensation and phase angle calculation: The control module performs Fourier transform processing on the acquired voltage and current signals to separate the fundamental component, and calculates the initial voltage and current phase angle based on the fundamental component. The validity of the matched transformer angle difference compensation coefficient is verified to confirm that the load range corresponding to the coefficient is consistent with the currently determined load range and that the coefficient value is within the preset reasonable range. The phase angle compensation amount is calculated based on the initial phase angle value, the transformer angle difference compensation coefficient, and the amplitude characteristics of the voltage and current signals using a preset algorithm. The initial phase angle and the compensation amount are superimposed to perform dynamic adaptive correction of the initial phase angle, and finally the compensated voltage and current phase angle measurement results are output. The phase angle compensation amount is calculated using the following formula: ,in This is the phase angle compensation amount. The mutual inductor angle difference compensation coefficient is the one matched in step S4. This is the effective value of the voltage signal. This is the effective value of the current signal. The initial phase angle, It characterizes the voltage and current amplitude ratio, and is used to dynamically adjust the compensation amount based on the actual signal amplitude characteristics. These are the specific coefficients corresponding to the current load interval in the segmented load-transformer angle difference correlation model constructed in step S1.

[0009] Furthermore, the sampling module used in step S2 is a high-precision synchronous sampling module. This module has the function of synchronously acquiring voltage and current signals. The sampling frequency is adaptively adjusted according to the rated frequency of the power grid. The data is verified in real time during the sampling process, and the data is immediately re-acquired when an abnormal data transmission is detected.

[0010] Furthermore, the number of load intervals divided in step S1 can be adjusted according to the application scenario of the segmented smart energy meter. In industrial power consumption scenarios with frequent load changes, the number of load intervals can be increased, while in residential power consumption scenarios with gradual load changes, the number of load intervals can be reduced. The amplitude threshold of each load interval can be adjusted on-site through the communication interface of the energy meter.

[0011] Furthermore, in step S4, when the control module calls the segmented load-transformer angle difference association model, it uses an index query method to locate the transformer angle difference compensation coefficient corresponding to the current load interval. If the corresponding compensation coefficient cannot be found, it automatically calls the compensation coefficients of the two adjacent load intervals and calculates the temporary compensation coefficient through an interpolation algorithm. At the same time, it records the situation and feeds it back to the background system through the communication module.

[0012] Furthermore, the segmented load-transformer angle difference correlation model constructed in step S1 has a periodic update function. After each preset operating cycle of the energy meter, the model update program is automatically started. Combining the actual load data and transformer operating data collected in step S2 during this period, the transformer angle difference compensation coefficients of each load range are optimized and adjusted. The updated model overwrites the original model and is stored. The update of the transformer angle difference compensation coefficients is achieved through the following formula: ,in The updated compensation coefficients, The original compensation coefficient before the update. The actual compensation coefficient is calculated based on the data collected in step S2 during this period. The iterative weights are determined by the frequency of load fluctuations within the operating cycle of the electricity meter; the lower the frequency of load fluctuations, the better. The larger the value, the higher the frequency of load fluctuations. The smaller the value, the better it is used to balance the stability of historical coefficients with the real-time nature of actual operating data.

[0013] Furthermore, the method for obtaining the initial phase angle in step S5 includes Fourier transform, zero-crossing detection, or phase-locked loop. The control module selects the initial phase angle calculation method according to the degree of distortion of the voltage and current signals. When the signal distortion rate exceeds a preset threshold, it switches to a calculation method with stronger anti-distortion capability.

[0014] Compared with existing technologies, this method for accurately measuring the voltage, current, and phase angle of segmented smart energy meters has the following advantages: This invention pre-constructs a segmented load-transformer phase angle difference correlation model, collects load voltage and current amplitude data in real time to accurately determine the current load range, and automatically matches the corresponding transformer phase angle difference compensation coefficient to dynamically and adaptively compensate the initial phase angle. This solves the phase shift problems caused by nonlinear changes in the phase angle difference of current transformers in different load ranges and impedance drift of voltage transformers in existing technologies. It takes into account the measurement needs under different operating conditions such as light load, heavy load, and no load, avoids phase measurement errors caused by inconsistent transformer phase angle differences under different loads, improves the accuracy of voltage and current phase angle measurement of segmented smart energy meters, provides a reliable guarantee for the accuracy of energy metering, and supports the efficient implementation of grid loss analysis, power factor adjustment, and other tasks, ensuring the safe and stable operation of the power grid and fair trading in the electricity market.

[0015] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 Flowchart of a method for accurate measurement of voltage, current, and phase angle in segmented smart energy meters; Figure 2 This is a flowchart illustrating a method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This invention patent provides a precise measurement method for the voltage and current phase angle of segmented smart energy meters. It addresses the problems of nonlinear changes in the phase angle difference of current transformers in different load ranges and phase shifts caused by impedance drift of voltage transformers in existing segmented smart energy meter phase angle measurements. It provides a precise measurement method that takes into account the needs of various operating conditions and improves the accuracy of metering.

[0020] See Figure 1 and Figure 2 The technical solution achieves accurate measurement through the following: A segmented load-transformer angle difference correlation model is constructed. Based on the rated current range of the electricity meter, the grid load variation pattern, and the transformer parameters, four basic load intervals are divided: no-load, light-load, medium-load, and heavy-load. Each interval has clearly defined current amplitude boundaries, and the number of intervals can be adjusted according to the application scenario; the number can be increased for industrial power consumption scenarios and decreased for residential power consumption scenarios. The amplitude thresholds can be adjusted on-site. Typical operating conditions of each interval are simulated in the laboratory. Current transformer angle difference data and voltage transformer impedance change data are synchronously collected using standard metering instruments. After data fitting algorithms are used to remove outliers, a mapping relationship between the load interval and the transformer angle difference is established, and a specific compensation coefficient for each interval is determined. The model is then organized into a correlation model according to a preset format and stored in the electricity meter's non-volatile storage module.

[0021] Real-time acquisition of load electrical parameters. A high-precision synchronous sampling module is used to synchronously acquire voltage and current signal amplitude data and transmit them to the control module. The sampling frequency can be adaptively adjusted according to the grid's rated frequency. Data is verified in real time during the sampling process, and re-acquisition is immediately performed in case of transmission abnormalities.

[0022] The current load range is determined. After receiving the data, the control module first performs noise reduction processing to remove abnormal data caused by instantaneous fluctuations in the power grid. It then extracts the effective values ​​and compares them with preset thresholds to initially determine the range. Next, it retrieves recent load operation data to analyze the changing trends, corrects the preliminary results, determines the specific range of the current load, and transmits relevant information.

[0023] Matching the transformer angle difference compensation coefficient. The control module uses an index query method to call the associated model and match the compensation coefficient corresponding to the current load range; if no corresponding coefficient is found, it automatically calls the compensation coefficient of the adjacent range and calculates a temporary coefficient through an interpolation algorithm, records the situation and feeds it back to the background system.

[0024] Dynamic adaptive compensation and phase angle calculation. The control module processes the acquired signal to obtain the initial phase angle. The initial phase angle is calculated using methods such as Fourier transform, and a method with stronger anti-distortion capability can be switched according to the signal distortion rate. Then, the interval consistency and numerical rationality of the compensation coefficient are verified. The compensation amount is calculated in combination with the voltage and current amplitude characteristics. After dynamically correcting the initial phase angle, the final measurement result is output.

[0025] Furthermore, the correlation model has a periodic update function. After the electricity meter operates for a preset period, it optimizes the compensation coefficient by combining the actual collected load and transformer operating data, balancing the stability of historical coefficients with the real-time nature of actual data. This solution effectively solves the problem of traditional measurement errors, improves the accuracy of phase angle measurement, provides a reliable guarantee for the accuracy of electricity metering, and supports grid loss analysis, power factor adjustment, and fair electricity market transactions.

[0026] Example 1 This embodiment is applied to an industrial plant electricity metering scenario. In this scenario, the load includes various production equipment such as motors and air conditioners. The load changes frequently and fluctuates greatly, with various operating conditions such as no-load, light-load, medium-load, and heavy-load switching. In this scenario, traditional segmented smart meters suffer from large phase angle measurement errors due to the nonlinear changes in the phase angle difference of the current transformer in different load intervals and the impedance drift of the voltage transformer during long-term operation. This affects the accuracy of electricity metering and may lead to metering disputes between power supply companies and industrial users. This embodiment provides technical support for high-precision electricity metering in this scenario by accurately measuring the voltage and current phase angles.

[0027] See Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows: A segmented load-transformer phase difference correlation model was constructed. Based on the rated current range of the segmented smart energy meters used in the industrial plant, the load change pattern in the actual power grid operation of the plant, and the rated operating parameters of the transformers, and considering the frequent load fluctuations, in addition to the basic no-load, light-load, medium-load, and heavy-load intervals, a secondary light-load interval and a secondary heavy-load interval were added. Each interval has a clear current amplitude division boundary.

[0028] Typical operating conditions for various load ranges were simulated in a laboratory environment. The simulation included common equipment startup, operation, and shutdown states in industrial plants. Actual current transformer angle difference data and voltage transformer impedance change data were synchronously collected using standard measuring instruments. Multiple sets of data were continuously collected for each operating condition to ensure data representativeness. A data fitting algorithm was used to analyze and process the collected data sets. After removing abnormal data caused by sudden fluctuations in the simulated operating conditions, a mapping relationship between load ranges and transformer angle differences was established, and the transformer angle difference compensation coefficient corresponding to each load range was determined.

[0029] In the specific implementation of this embodiment, the transformer angle difference compensation coefficient is calculated using the following formula: .in To provide a comprehensive transformer angle difference compensation coefficient, This is the current transformer angle difference data. This refers to the angle difference data corresponding to the impedance drift of the voltage transformer. and These are the weighting coefficients. and The determination method is to collect multiple sets of data under different load ranges in the laboratory. , The sample data that deviates from the actual phase measurement are obtained by fitting with the least squares method. Its purpose is to quantify the influence of the two angle differences on the phase measurement and adapt to the working characteristics of the transformer in industrial scenarios.

[0030] Finally, the correspondence between the load range, transformer angle difference data, and compensation coefficient is organized into a segmented load-transformer angle difference correlation model according to a preset format and stored in the non-volatile storage module of the energy meter for later retrieval.

[0031] Meanwhile, this correlation model has a periodic update function. After each preset operating cycle of the energy meter, the model update program is automatically started. Combining the actual load data and transformer operating data collected during this period, the transformer angle difference compensation coefficient for each load range is optimized and adjusted. The updated model overwrites the original model and is stored. In the specific implementation of this embodiment, the update of the transformer angle difference compensation coefficient is achieved through the following formula: .in The updated compensation coefficients, The original compensation coefficient before the update. The actual compensation coefficient is calculated based on the data collected during this period. For iterative weights. By statistically analyzing the frequency of load fluctuations during the electricity meter's operating cycle, it was determined that industrial plants experience high load fluctuations during daytime production. Smaller values ​​indicate lower frequency of load fluctuations when equipment is shut down at night. The value is set to be large in order to balance the stability of historical coefficients with the real-time nature of actual operating data.

[0032] Real-time acquisition of load electrical parameters utilizes a high-precision synchronous sampling module built into the energy meter. This module features simultaneous acquisition of voltage and current signals, adapting to the complex environment of industrial power grids. The sampling frequency adaptively adjusts according to the grid's rated frequency, ensuring accurate data acquisition even with slight fluctuations in grid frequency. Real-time data verification is performed during sampling; if data transmission anomalies caused by the start-up or shutdown of industrial equipment are detected, data is immediately re-acquired, ensuring the reliability of voltage and current signal amplitude data transmitted to the control module.

[0033] After receiving voltage and current amplitude data from the sampling module, the control module first performs noise reduction processing on the data to remove abnormal amplitude data caused by instantaneous power grid fluctuations and sudden starts of industrial equipment. Then, it extracts the effective values ​​of the processed amplitude data and compares them one by one with preset amplitude thresholds for each load range to preliminarily determine the possible range of the current load. For example, if the effective current value is within the threshold range of the second lightest load range, it is preliminarily determined to be in the second lightest load range.

[0034] Next, the system retrieves recent load operation data stored in the electricity meter, analyzes load change trends, and corrects the initial judgment based on trend characteristics. For example, if recent data shows that the load is gradually increasing from a light load to a medium load, and the initial judgment is a light load, it may be adjusted to a medium load range after trend correction. Finally, by combining the comparison results with the trend correction results, the specific range of the current load is determined, and the range information is transmitted to the compensation coefficient matching unit.

[0035] When matching the transformer angle difference compensation coefficient, the control module uses an index lookup method to quickly locate the transformer angle difference compensation coefficient corresponding to the current load range when calling the stored segmented load-transformer angle difference association model. This improves matching efficiency to meet the needs of rapid load switching in industrial scenarios. If the corresponding compensation coefficient cannot be found, for example, in a special load state exceeding the preset range, the compensation coefficients of the two adjacent load ranges are automatically called to calculate a temporary compensation coefficient through an interpolation algorithm. This situation is recorded and fed back to the backend system through the communication module for subsequent investigation and analysis by staff.

[0036] Dynamic adaptive compensation and phase angle calculation: The control module first performs Fourier transform processing on the acquired voltage and current signals to separate the fundamental component, and then calculates the initial voltage and current phase angle based on the fundamental component. The calculation method for the initial phase angle can be flexibly selected according to the degree of distortion of the voltage and current signals. In addition to the Fourier transform method, zero-crossing detection or phase-locked loop methods can also be used. When harmonics generated during the operation of industrial equipment cause the signal distortion rate to exceed a preset threshold, the system automatically switches to a calculation method with stronger anti-distortion capabilities to ensure the accuracy of the initial phase angle calculation.

[0037] Subsequently, the validity of the matched transformer phase angle compensation coefficient is verified to confirm that the load range corresponding to the coefficient is consistent with the currently determined load range, and that the coefficient value is within a preset reasonable range, thus avoiding compensation deviation due to incorrect coefficients. In the specific implementation of this embodiment, based on the initial phase angle value, the transformer phase angle compensation coefficient, and the amplitude characteristics of the voltage and current signals, the phase angle compensation amount is calculated using the following formula: .in This is the phase angle compensation amount. This refers to the angle difference compensation coefficient of the previously matched mutual inductors. This is the effective value of the voltage signal. This is the effective value of the current signal. The initial phase angle, It characterizes the voltage and current amplitude ratio and is used to dynamically adjust the compensation amount based on the actual signal amplitude characteristics, so that the compensation is more in line with the current operating state.

[0038] Finally, the initial phase angle and the compensation amount are superimposed to complete the dynamic adaptive correction of the initial phase angle, and the final output is the voltage and current phase angle measurement result after compensation.

[0039] In summary, this embodiment effectively solves the phase angle measurement error problem of traditional segmented smart meters under conditions of frequent load switching and complex operating conditions by applying it in industrial power consumption scenarios. By constructing a segmented load-transformer phase angle difference correlation model adapted to industrial scenarios, it achieves accurate division of different load ranges and rapid matching of compensation coefficients. Combined with a dynamic adaptive compensation algorithm, it effectively offsets the phase shift effects caused by nonlinear changes in current transformer phase angle difference and impedance drift of voltage transformers. Example

[0040] This embodiment applies to a centralized electricity metering scenario in a residential community. In this scenario, the load mainly consists of household appliances and lighting equipment. Load changes are relatively gradual but exhibit significant diurnal variations. During the day, most households are unoccupied and operating under light or no load conditions, while during peak nighttime electricity consumption, the load is primarily medium, with occasional short-term heavy loads. Traditional segmented smart meters in this scenario lack optimized interval division and compensation strategies for gradual load changes. Furthermore, the cumulative impedance drift from the long-term operation of the voltage transformer leads to persistent deviations in phase angle measurements, affecting the accuracy of residential electricity metering. This embodiment, based on the technical framework of the aforementioned embodiments, optimizes the measurement scheme by considering the characteristics of residential electricity loads, achieving accurate measurement of voltage and current phase angles in this scenario.

[0041] See Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows: A segmented load-transformer angle difference correlation model is constructed. Based on the model constructed in the previous embodiment, and combined with the rated current range of the segmented smart energy meters used in the residential community, the diurnal variation pattern of the community power grid load, and the rated operating parameters of the transformers, and considering the characteristics of the gradual load change, only four basic load intervals are retained: no-load interval, light-load interval, medium-load interval, and heavy-load interval. Each interval is set with a division boundary that conforms to the range of residential electricity current amplitude, and the amplitude threshold of each interval is determined by on-site debugging through the energy meter communication interface to adapt to the actual operating state of the community power grid.

[0042] Typical operating conditions of residential electricity consumption were simulated in a laboratory environment, including common states such as individual household appliances operating, multiple devices operating simultaneously, and devices in standby mode. Actual phase angle difference data of current transformers and impedance change data of voltage transformers were synchronously collected using standard metering instruments. Multiple sets of data were continuously collected for each operating condition to ensure that the data covers various scenarios of daily residential electricity use. Data fitting algorithms were used to analyze and process the collected data sets. After removing abnormal data caused by momentary failures of the simulated equipment, a mapping relationship between load intervals and transformer phase angle differences was established, and the corresponding transformer phase angle difference compensation coefficient for each load interval was determined.

[0043] In the specific implementation of this embodiment, the transformer angle difference compensation coefficient is calculated using the following formula: The relationship between load range, transformer angle difference data, and compensation coefficient is organized into a segmented load-transformer angle difference correlation model according to a preset format and stored in the non-volatile storage module of the energy meter for easy access by the control module.

[0044] This correlation model also has a periodic update function. The model update program is automatically started after each preset operating cycle of the electricity meter. Combining the actual load data of the community and the operating data of the current transformers collected during this period, the model optimizes and adjusts the current transformer angle difference compensation coefficients for each load range. The updated model overwrites the original model and is stored. In the specific implementation of this embodiment, the update of the current transformer angle difference compensation coefficients is achieved through the following formula: Because the frequency of load fluctuations in residential areas is generally low, The values ​​are generally too high, only appearing during peak nighttime electricity consumption periods when load fluctuations are relatively frequent. By appropriately reducing the value, the stability of the historical coefficient can be ensured, while also adapting to the changes in transformer characteristics caused by short-term load variations.

[0045] The electrical parameters of the load are collected in real time. The high-precision synchronous sampling module in the previous embodiment is used as the built-in sampling module of the energy meter. This module maintains the function of synchronously collecting voltage and current signals. The sampling frequency is adaptively adjusted according to the rated frequency of the residential power grid. Since the frequency fluctuation of the residential power grid is small, the adjustment range of the sampling frequency is more gradual, ensuring the continuity and stability of the collected data.

[0046] During the sampling process, the data is verified in real time, with a focus on monitoring data transmission anomalies caused by the start-up and shutdown of household appliances such as refrigerator compressors and air conditioners. Once an anomaly is detected, the data is immediately re-acquired to ensure that the voltage and current signal amplitude data transmitted to the control module are accurate and reliable, laying the foundation for subsequent interval determination and phase angle calculation.

[0047] After determining the current load range, the control module receives the voltage and current amplitude data transmitted by the sampling module. First, it performs noise reduction on the data to remove abnormal amplitude data caused by instantaneous fluctuations in the power grid and sudden starts of household appliances. In view of the small fluctuation range of residential electricity consumption data, the noise reduction algorithm parameters are optimized to improve the efficiency and accuracy of data processing.

[0048] The effective values ​​of the processed amplitude data are then extracted and compared with the preset amplitude thresholds for each load range to preliminarily determine the range in which the current load may be located. For example, when most households are unoccupied during the daytime on weekdays, the effective value of the current is mostly within the no-load or light-load range threshold, and is preliminarily determined to be the corresponding range.

[0049] The system retrieves recent load operation data stored in the electricity meter, analyzes load change trends, and corrects the initial judgment based on the diurnal variation pattern of residential electricity consumption. For example, if recent data indicates that the current period is during the nighttime peak electricity consumption time, and the overall load is in a medium-load state, the initial judgment might be a light-load range, but this would be adjusted to a medium-load range after trend correction. Finally, by comprehensively comparing the results with the trend correction results, the specific range of the current load is determined, and this range information is transmitted to the compensation coefficient matching unit.

[0050] When matching the transformer angle difference compensation coefficient, the control module calls the stored segmented load-transformer angle difference association model and uses an index query method to locate the transformer angle difference compensation coefficient corresponding to the current load interval. Since the load intervals in the residential scenario are divided less and the switching frequency is low, the query efficiency is higher and the compensation coefficient matching can be completed quickly.

[0051] If the corresponding compensation coefficient cannot be found, for example, when residents use high-power appliances in a concentrated manner, resulting in an overload situation, the compensation coefficients of the two adjacent load ranges are automatically called to calculate the temporary compensation coefficient through an interpolation algorithm. At the same time, this situation is recorded and fed back to the community power grid management backend through the communication module, so that staff can track and analyze abnormal power consumption.

[0052] Dynamic adaptive compensation and phase angle calculation: The control module first performs Fourier transform processing on the acquired voltage and current signals to separate the fundamental component, and then calculates the initial voltage and current phase angle based on the fundamental component. Since the electrical equipment in residential areas generates fewer harmonics and the signal distortion rate is generally low, the initial phase angle calculation mainly adopts the Fourier transform method. Only when the signal distortion rate is detected to exceed a preset threshold is it switched to a calculation method with stronger anti-distortion capability, balancing calculation accuracy and efficiency.

[0053] Subsequently, the validity of the matched transformer angle difference compensation coefficient is verified to confirm that the load range corresponding to the coefficient is consistent with the currently determined load range, and that the coefficient value is within the preset reasonable range, so as to avoid compensation failure due to range matching error or coefficient abnormality.

[0054] In the specific implementation of this embodiment, based on the magnitude of the initial phase angle, the transformer angle difference compensation coefficient, and the amplitude characteristics of the voltage and current signals, the phase angle compensation amount is calculated using the following formula: Finally, the initial phase angle and the compensation amount are superimposed to perform dynamic adaptive correction of the initial phase angle, and the compensated voltage and current phase angle measurement results are output, providing accurate data support for residential electricity metering.

[0055] In summary, this embodiment, through its application in a residential electricity consumption scenario, effectively solves the phase angle measurement deviation problem caused by insufficient load characteristic adaptation and accumulated transformer impedance drift in traditional electricity meters under this scenario, based on the technology of the aforementioned embodiments and combined with a scenario-specific optimization scheme. By optimizing the load range division and compensation coefficient update strategy, it achieves accurate adaptation to the diurnal variation of residential electricity consumption conditions, and the dynamic adaptive compensation mechanism effectively offsets the effects of current transformer phase angle difference changes and voltage transformer impedance drift.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter, characterized in that, The method includes the following steps: S1. Construct a segmented load-transformer angle difference correlation model. Based on the application scenario of the segmented smart energy meter and the working characteristics of the transformer, divide the load into multiple load intervals. Collect current transformer angle difference data and voltage transformer impedance drift related data in different load intervals. Combine the inherent parameters of the transformer to establish and store the correlation between each load interval and the corresponding transformer angle difference compensation coefficient. S2. Real-time acquisition of load electrical parameters, the amplitude data of voltage and current signals are acquired through the built-in sampling module of the energy meter and transmitted to the control module; S3. Determine the current load range. The control module determines the specific range where the current load is located based on the preset load range division criteria. S4. Match the current transformer angle difference compensation coefficient. The control module calls the stored association model and automatically matches the corresponding compensation coefficient according to the current load range. S5. Dynamic adaptive compensation and phase angle calculation: The control module obtains the initial phase angle based on the collected voltage and current signals, performs dynamic adaptive compensation using the matched compensation coefficient, and outputs the phase angle measurement result.

2. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, Step S1, in constructing the segmented load-transformer angle difference correlation model, includes the following steps: Based on the rated current range of segmented smart energy meters, the load variation pattern in actual power grid operation, and the rated operating parameters of current transformers, four basic load ranges are divided: no-load range, light-load range, medium-load range, and heavy-load range. Each range has a clear current amplitude division boundary. In a laboratory environment, typical operating conditions of various load ranges are simulated. The actual phase difference data of the current transformer and the impedance change data of the voltage transformer are collected synchronously using standard measuring instruments. Multiple sets of data are collected continuously under each operating condition. A data fitting algorithm was used to analyze and process the collected data. After removing abnormal data, a mapping relationship between the load range and the transformer angle difference was established, and the transformer angle difference compensation coefficient corresponding to each load range was determined. The correspondence between load range, transformer angle difference data, and compensation coefficient is organized into a segmented load-transformer angle difference correlation model according to a preset format and stored in the non-volatile storage module of the energy meter.

3. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, Step S3, in determining the current load range, includes the following steps: After receiving the voltage and current amplitude data transmitted by the sampling module, the control module performs noise reduction processing on the data to remove abnormal amplitude data caused by instantaneous fluctuations in the power grid. Extract the valid values ​​of the processed amplitude data and compare them one by one with the preset amplitude thresholds for each load range to preliminarily determine the possible range of the current load. Retrieve recent load operation data stored in the electricity meter, analyze load change trends, and revise the preliminary judgment based on trend characteristics; By combining the comparison results and trend correction results, the specific range of the current load is determined, and the range information is transmitted to the compensation coefficient matching unit.

4. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, Step S5, when performing dynamic adaptive compensation and phase angle calculation, includes the following steps: The control module performs Fourier transform processing on the acquired voltage and current signals to separate the fundamental component, and calculates the initial voltage and current phase angle based on the fundamental component. The validity of the matched transformer angle difference compensation coefficient is verified to confirm that the load range corresponding to the coefficient is consistent with the currently determined load range and that the coefficient value is within the preset reasonable range. The phase angle compensation amount is calculated based on the initial phase angle value, the transformer angle difference compensation coefficient, and the amplitude characteristics of the voltage and current signals using a preset algorithm. The initial phase angle and the compensation amount are superimposed to perform dynamic adaptive correction of the initial phase angle, and finally the compensated voltage and current phase angle measurement results are output.

5. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, The sampling module used in step S2 is a high-precision synchronous sampling module. This module has the function of synchronously acquiring voltage and current signals. The sampling frequency is adaptively adjusted according to the rated frequency of the power grid. The data is verified in real time during the sampling process. When an abnormal data transmission is detected, the data is immediately re-acquired.

6. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, The number of load intervals divided in step S1 can be adjusted according to the application scenario of the segmented smart energy meter. In industrial power consumption scenarios with frequent load changes, the number of load intervals can be increased, while in residential power consumption scenarios with gradual load changes, the number of load intervals can be reduced. The amplitude threshold of each load interval can be adjusted on-site through the communication interface of the energy meter.

7. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, In step S4, when the control module calls the segmented load-transformer angle difference association model, it uses an index query method to locate the transformer angle difference compensation coefficient corresponding to the current load interval. If the corresponding compensation coefficient cannot be found, it automatically calls the compensation coefficients of the two adjacent load intervals and calculates the temporary compensation coefficient through an interpolation algorithm. At the same time, it records the situation and feeds it back to the background system through the communication module.

8. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, The segmented load-transformer angle difference correlation model constructed in step S1 has a periodic update function. After the energy meter runs for a preset period, the model update program is automatically started. Combining the actual load data and transformer operation data collected in step S2 during this period, the transformer angle difference compensation coefficient of each load range is optimized and adjusted. The updated model overwrites the original model and is stored.

9. The method for accurately measuring the voltage, current, and phase angle of a segmented smart energy meter according to claim 1, characterized in that, The method for obtaining the initial phase angle in step S5 includes Fourier transform, zero-crossing detection, or phase-locked loop. The control module selects the initial phase angle calculation method according to the degree of distortion of the voltage and current signals. When the signal distortion rate exceeds the preset threshold, it switches to a calculation method with stronger anti-distortion capability.

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