A wide voltage electric energy meter precision calibration method based on multi-scene weight coupling

Abstract

The present application relates to a kind of wide voltage electric energy meter precision calibration method based on multi-scene weight coupling. Including: obtaining load characteristic statistical data, based on the distribution frequency of different power factors, determine weight coefficient vector;In multiple preset target voltage levels, control standard source respectively in multiple different power factor operating conditions Output test signal, obtain the initial active error value of electric energy meter in each target voltage level, in each power factor operating condition;The initial active error value of multiple power factors under each target voltage level is weighted calculation, corresponding error reference value is generated, and all error reference values are obtained by repeating calculation;Real-time identification input voltage belongs to voltage level, and corresponding error reference value is automatically called to approach type correction is carried out to measurement parameter.The method can solve the systematic deviation problem existing in the present table calibration technology under different power factors, and realize high-precision measurement in the full power factor range under multi-element voltage environment.

Description

Technical Field

[0001] This invention relates to the field of electricity metering equipment technology, and specifically to a method for calibrating the accuracy of wide-voltage electricity meters based on multi-scenario weighted coupling. Background Technology

[0002] As the core equipment for electricity metering, the accuracy of electricity meters directly affects the fairness of trade settlement and the efficiency of power grid operation. With the globalization of smart grids, electricity meters need to adapt to diverse voltage standards (such as 120V, 220V, or 277V) in different countries and regions, and maintain high accuracy under various load conditions (such as purely resistive or inductive). This poses a severe challenge to the calibration technology in the production and verification of electricity meters, requiring them to achieve high consistency and robustness in automatic calibration across a wide voltage range and all power factors.

[0003] Existing electricity meter calibration methods generally rely on high-precision standard sources to provide voltage, current, and power signals as "true values." During calibration, the meter's internal measurement results are compared with the standard source output, and parameters such as gain and phase angle difference are adjusted to reduce measurement errors. Traditional methods typically operate under a fixed nominal voltage and primarily select a single operating condition with a power factor PF = 0.5L (inductive) for parameter fitting and calibration.

[0004] However, the above-mentioned technologies have obvious drawbacks: on the one hand, traditional calibration modes cannot effectively adapt to a wide voltage range and are difficult to meet the stringent requirements of the international high-end market for the universality of electricity meters; on the other hand, different power factors correspond to different application scenarios, and calibration under only a single power factor is difficult to take into account the metering performance under different scenarios, resulting in poor error consistency across the entire power factor range. Summary of the Invention

[0005] The purpose of this invention is to provide a wide-voltage energy meter accuracy calibration method based on multi-scenario weighted coupling. This method supports unified calibration over a wide voltage range and can solve the systematic deviation problem of existing meter calibration technologies under different power factors. It enables the energy meter to automatically match the error benchmark for adaptive calibration according to the operating voltage, achieving high-precision metering across the entire power factor range in a multi-voltage environment, thus meeting the market's requirements for the universality of energy meters.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows.

[0007] A method for calibrating the accuracy of wide-voltage energy meters based on multi-scenario weighted coupling, the method comprising:

[0008] S01, Obtain load characteristic statistics under the target application scenario, and determine a set of weight coefficient vectors associated with the power factor based on the distribution frequency of different power factors in the load characteristic statistics.

[0009] S02, under multiple preset target voltage levels, control the standard source to output test signals under multiple different power factor conditions, and obtain the initial active power error value of the energy meter under each target voltage level and each power factor condition;

[0010] S03, For each target voltage level, the initial active power error values ​​of multiple power factors under the voltage level are weighted using the weighting coefficient vector to generate an error benchmark value corresponding to the voltage level. The error benchmark values ​​corresponding to each of the target voltage levels are obtained by repeating the calculation.

[0011] S04: Write all error reference values ​​into the storage unit of the energy meter; when the energy meter is running, identify the voltage level of the input voltage in real time, and automatically call the corresponding error reference value to perform approximate correction on the metering parameters.

[0012] As a preferred embodiment of the present invention, in step S01, the method for determining the weight coefficient vector is specifically as follows:

[0013] Obtain the historical load operation curve of the power grid under the target application scenario, and extract the time series data of active power P(t) and reactive power Q(t).

[0014] According to the formula Calculate the instantaneous power factor sequence;

[0015] The instantaneous power factor sequence is mapped to a preset power factor interval, and the proportion of power contribution or duration of each power factor interval within the sampling period is statistically analyzed and used as the weighting coefficient for the corresponding operating condition.

[0016] As a preferred embodiment of the present invention, in step S01, the weight coefficient vector is determined by a predefined fixed weight strategy, which includes an equal weight strategy and a data table empirical weight strategy.

[0017] As a preferred embodiment of the present invention, the weighting coefficient vector includes at least three weighting coefficients, which correspond to resistive operating conditions with a power factor of 1.0L, inductive operating conditions with a power factor of 0.5L, and capacitive operating conditions with a power factor of 0.8C, respectively.

[0018] As a preferred embodiment of the present invention, the weight coefficient vector includes three weight coefficients, and the data table empirical weighting strategy is specifically as follows:

[0019] For resistive operating conditions with a power factor of 1.0L, the weighting coefficient K 1.0LSet to 0.5;

[0020] For inductive operating conditions with a power factor of 0.5L, the weighting coefficient K 0.5L Set to 0.4;

[0021] For capacitive operating conditions with a power factor of 0.8C, the weighting coefficient K 0.8C Set to 0.1.

[0022] As a preferred embodiment of the present invention, in step S03, the weighted calculation is implemented using the following formula:

[0023]

[0024] in, For the i-th target voltage level; The weighting coefficient corresponding to the j-th power factor operating condition; is the initial active power error value measured under voltage level i and power factor condition j; J is the total number of power factor conditions.

[0025] As a preferred embodiment of the present invention, in step S04, if the input voltage identified by the energy meter in real time is between two adjacent target voltage levels, the real-time error reference value under the current voltage is calculated using the error reference value corresponding to the two adjacent target voltage levels through linear interpolation.

[0026] As a preferred embodiment of the present invention, the specific steps for using the corresponding error reference value to perform approximate correction of the measurement parameters in step S04 are as follows:

[0027] The error reference value corresponding to each target voltage level is sequentially converted to an integer, negativeed, and limited.

[0028] The error reference value corresponding to each target voltage level after the amplitude limiting process is written into the storage unit of the energy meter;

[0029] Once the energy meter identifies the voltage level of the input voltage, it loads the corresponding error reference value from the storage unit and calculates the compensation coefficient k.

[0030] The electricity meter performs an approximate correction to the current metering parameters based on the compensation coefficient k.

[0031] As a preferred embodiment of the present invention, in step S02, the preset target voltage level includes at least two of 57.7V, 120V, 127V, 220V and 277V.

[0032] An electricity meter includes a storage unit that stores an error reference value generated by the aforementioned wide-voltage electricity meter accuracy calibration method based on multi-scenario weight coupling.

[0033] In summary, the present invention has the following beneficial effects:

[0034] This invention enables electricity meters to adapt to diverse voltage standards in different countries or regions by presetting multiple target voltage levels and performing multi-voltage point calibration. During operation, the electricity meter identifies the input voltage level in real time and automatically calls the corresponding error reference value for correction, effectively overcoming the limitations of the traditional single voltage calibration mode. This ensures that the electricity meter maintains high accuracy and consistency in a wide voltage environment, meeting the stringent requirements of the international market for the universality of electricity meters.

[0035] This invention introduces a weighted coefficient vector to calculate the error value under multiple power factor conditions based on the load characteristic statistics of the target application scenario, generating an error benchmark value. This makes the calibration process more in line with actual load changes and avoids the systematic deviation caused by traditional single power factor calibration, thereby significantly improving the metering accuracy and robustness of the electricity meter across the entire power factor range. It is especially suitable for the complex and ever-changing power consumption environment in smart grids. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0039] As used herein, the term "comprising" and its variations are open terms meaning "including but not limited to". The term "based on" means "at least partially based on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. Unless explicitly indicated by the context, the definition of a term shall remain consistent throughout the specification.

[0040] This embodiment relies on a high-precision standard source, a calibration platform, and a smart energy meter with corresponding processing capabilities and storage space. The calibration process is controlled by host computer software, such as... Figure 1 As shown, the following steps will be executed automatically:

[0041] S01, obtain the load characteristic statistics of the target application scenario, and determine a set of weight coefficient vectors associated with the power factor based on the distribution frequency of different power factors in the load characteristic statistics.

[0042] In one possible implementation, the weighting coefficient vector can be determined by obtaining the historical load operation curve of the power grid under the target application scenario and extracting the time series data of active power P(t) and reactive power Q(t).

[0043] According to the formula Calculate the instantaneous power factor sequence;

[0044] The instantaneous power factor sequence is mapped to a preset power factor interval, and the proportion of electricity contribution or duration of each power factor interval within the sampling period is statistically analyzed and used as the weighting coefficient for the corresponding operating condition.

[0045] A series of power factor ranges are predefined, corresponding to the operating conditions of interest during calibration. For example, they can be divided into:

[0046] Interval 1: (0.95L, 1.00L], close to purely resistive;

[0047] Range 2: (0.85L, 0.95L), slightly sensitive;

[0048] Range 3: (0.60L, 0.85L), moderate sensitivity;

[0049] Interval 4: (0.00L, 0.60L), depth of feeling;

[0050] Range 5: (0.00C, 0.60C), deep capacitive;

[0051] Range 6: (0.60C, 0.85C), moderately soluble;

[0052] Range 7: (0.85C, 1.00C), slightly capacitive.

[0053] It should be noted that the granularity and range of interval division can be flexibly adjusted according to standard requirements and actual needs.

[0054] Next, the weighting coefficients are determined. One possible strategy is to calculate the percentage of sampling points within each power factor interval relative to the total number of sampling points. This method determines the weighting coefficients based on the time probability of various load conditions. Another possible strategy is to calculate the electricity contribution of each sampling point within that interval, and then sum the electricity contributions of all sampling points belonging to the same power factor interval. The weight of each interval is the percentage of its accumulated electricity contribution relative to the total accumulated electricity. This method determines the weighting coefficients based on the contribution of various load conditions to the total energy consumption.

[0055] In another possible implementation, if no historical load characteristic statistics are available, the weighting coefficients can be determined through a predefined fixed weighting strategy, which includes equal weighting strategy and data table experience weighting strategy.

[0056] The equal-weight strategy assigns weights of equal magnitude, which is suitable for simplifying operations or balancing scenarios.

[0057] The data table experience weighting strategy mainly allocates weights based on engineering experience data tables combined with industry-specified test conditions. This strategy emphasizes actual scenarios or mandatory industry requirements.

[0058] For example, in this embodiment, the weighting coefficient vector contains three weighting coefficients, corresponding to the resistive operating condition with a power factor of 1.0L, the inductive operating condition with a power factor of 0.5L, and the capacitive operating condition with a power factor of 0.8C, respectively. The specific weighting strategy based on the data table experience is as follows:

[0059] For resistive operating conditions with a power factor of 1.0L, the weighting coefficient K 1.0L Set to 0.5;

[0060] For inductive operating conditions with a power factor of 0.5L, the weighting coefficient K 0.5L Set to 0.4;

[0061] For capacitive operating conditions with a power factor of 0.8C, the weighting coefficient K 0.8C Set to 0.1.

[0062] S02, under multiple preset target voltage levels, controls the standard source to output test signals under multiple different power factor conditions, and obtains the initial active power error value of the energy meter under each target voltage level and each power factor condition.

[0063] In this embodiment, in order to cover mainstream voltage specifications in North America, Latin America, Asia and other regions, the preset target voltage levels include at least two of 57.7V, 120V, 127V, 220V and 277V.

[0064] Set of target voltage levels For example, set the calibration current to I. i =5A, for each target voltage level The control standard source sequentially outputs stable test signals under three operating conditions (j=1, 2, 3) with power factors of 1.0L, 0.5L, and 0.8C. After the output stabilizes, the active power error value measured by the energy meter at each target voltage level is read and recorded through the calibration platform. This forms an initial error data matrix. Similarly, error acquisition is completed at 120V, 220V, and 277V.

[0065] S03. For each target voltage level, the initial active power error values ​​of multiple power factors under that voltage level are weighted using the weighting coefficient vector to generate the error benchmark value corresponding to that voltage level. The error benchmark value corresponding to each of all target voltage levels is obtained by repeating the calculation.

[0066] The weighted calculation is achieved using the following formula:

[0067]

[0068] in, For the i-th target voltage level; The weighting coefficient corresponding to the j-th power factor operating condition; is the initial active power error value measured under voltage level i and power factor condition j; J is the total number of power factor conditions.

[0069] Specifically, using the weighting coefficient vector determined in step S01, the error benchmark values ​​corresponding to the four target voltage levels are calculated based on the above formula. .

[0070] S04: Write all error reference values ​​into the storage unit of the energy meter; when the energy meter is running, identify the voltage level of the input voltage in real time, and automatically call the corresponding error reference value to perform approximate correction on the metering parameters.

[0071] The specific steps for approximating the measurement parameters by calling the corresponding error benchmark value are as follows:

[0072] The error reference value corresponding to each target voltage level is sequentially converted to an integer, negativeed, and limited.

[0073] The error reference value corresponding to each target voltage level after the amplitude limiting process is written into the storage unit of the energy meter;

[0074] Once the energy meter identifies the voltage level of the input voltage, it loads the corresponding error reference value from the storage unit and calculates the compensation coefficient k.

[0075] The electricity meter approximates the correction of the current metering parameters based on the compensation coefficient k.

[0076] Taking a calibration platform with an accuracy of 1 / 10,000 as an example, firstly, each error reference value is multiplied by 10,000 to convert it into an integer in units of 1 / 10,000. To facilitate subsequent compensation calculations, the amplified and rounded value is negative, resulting in ERR(U i Then, a limiting process is performed to ensure that the processed value is within the range of a single-byte two's complement representation. If it exceeds this range, ERR(U) is triggered. i ) [−128, +127], then truncate to the boundary value so that it can be saved by the digital storage unit inside the electricity meter.

[0077] Then, the integer value after the amplitude limiting process is written into the error reference value register area specially allocated in the non-volatile storage unit of the energy meter in 8-bit binary two's complement form, with each voltage level corresponding to a storage address.

[0078] After writing the error baseline value, the formal calibration parameters are fitted.

[0079] In actual operation, the electricity meter monitors the input voltage in real time and identifies the target voltage level to which the current voltage belongs (for example, if the detected input voltage is 225V, it is identified as belonging to the 220V level). For each target voltage level U... i Then, the compensation coefficient k is calculated based on the following formula:

[0080]

[0081] And scale the I, P, and Q values ​​currently measured by the electricity meter:

[0082]

[0083] The final calibration is complete. , and This enables compensation and correction of the metering process, ensuring high-precision metering across the entire voltage range and under various power factor loads.

[0084] In another possible implementation, if the input voltage identified by the energy meter in real time is between two adjacent target voltage levels, the real-time error reference value under the current voltage is calculated using the error reference values ​​corresponding to the two adjacent target voltage levels through linear interpolation, so as to achieve a smooth transition of calibration parameters.

[0085] The following examples demonstrate the technical effects of this method.

[0086] On a three-phase smart energy meter production line, 1000 sample meters were calibrated using the method described in this embodiment. Test results show that, compared to traditional single-voltage, single-power-factor calibration methods, the standard deviation of the energy meter error under PF=0.5L conditions was significantly reduced from 0.013% to 0.006% after adopting this invention. More than 95% of the sample energy meters achieved a comprehensive error control within a narrow range of [-0.05%, +0.05%] across the entire wide voltage range (57.7V~277V) and the full power factor range, fully meeting the international standard requirements of IEC 62053-22 Class 0.2S high-precision energy meters, significantly enhancing the product's competitiveness in the international market.

[0087] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A method for calibrating the accuracy of wide-voltage energy meters based on multi-scenario weighted coupling, characterized in that, The methods include: S01, Obtain load characteristic statistics data under the target application scenario, and determine a set of weight coefficient vectors associated with the power factor based on the distribution frequency of different power factors in the load characteristic statistics data. S02, under multiple preset target voltage levels, control the standard source to output test signals under multiple different power factor conditions, and obtain the initial active power error value of the energy meter under each target voltage level and each power factor condition; S03, For each target voltage level, the initial active power error values ​​of multiple power factors under the voltage level are weighted using the weighting coefficient vector to generate an error benchmark value corresponding to the voltage level. The error benchmark values ​​corresponding to each of the target voltage levels are obtained by repeating the calculation. S04: Write all error reference values ​​into the storage unit of the energy meter; when the energy meter is running, identify the voltage level of the input voltage in real time, and automatically call the corresponding error reference value to perform approximate correction on the metering parameters.

2. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S01, the method for determining the weight coefficient vector is as follows: Obtain the historical load operation curve of the power grid under the target application scenario, and extract the time series data of active power P(t) and reactive power Q(t). Calculate the instantaneous power factor sequence; The instantaneous power factor sequence is mapped to a preset power factor interval, and the proportion of power contribution or duration of each power factor interval within the sampling period is statistically analyzed and used as the weighting coefficient for the corresponding operating condition.

3. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S01, the weight coefficient vector is determined by a predefined fixed weight strategy, which includes an equal weight strategy and a data table empirical weight strategy.

4. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 3, characterized in that, The weighting coefficient vector contains at least three weighting coefficients, corresponding to resistive operating conditions with a power factor of 1.0L, inductive operating conditions with a power factor of 0.5L, and capacitive operating conditions with a power factor of 0.8C, respectively.

5. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 4, characterized in that, The weight coefficient vector includes three weight coefficients, and the empirical weighting strategy for the data table is specifically as follows: For resistive operating conditions with a power factor of 1.0L, the weighting coefficient K 1.0L Set to 0.5; For inductive operating conditions with a power factor of 0.5L, the weighting coefficient K 0.5L Set to 0.4; For capacitive operating conditions with a power factor of 0.8C, the weighting coefficient K 0.8C Set to 0.

1.

6. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S03, the weighted calculation is performed using the following formula: in, For the i-th target voltage level; The weighting coefficient corresponding to the j-th power factor operating condition; is the initial active power error value measured under voltage level i and power factor condition j; J is the total number of power factor conditions.

7. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S04, if the input voltage identified by the energy meter in real time is between two adjacent target voltage levels, the real-time error reference value under the current voltage is calculated using the error reference value corresponding to the two adjacent target voltage levels through linear interpolation.

8. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S04, the specific steps for applying the corresponding error benchmark value to the measurement parameters in an approximate correction are as follows: The error reference value corresponding to each target voltage level is sequentially converted to an integer, negativeed, and limited. The error reference value corresponding to each target voltage level after the amplitude limiting process is written into the storage unit of the energy meter; Once the energy meter identifies the voltage level of the input voltage, it loads the corresponding error reference value from the storage unit and calculates the compensation coefficient k. The electricity meter performs an approximate correction to the current metering parameters based on the compensation coefficient k.

9. The method for calibrating the accuracy of a wide-voltage energy meter based on multi-scenario weighted coupling according to claim 1, characterized in that, In step S02, the preset target voltage level includes at least two of the following: 57.7V, 120V, 127V, 220V, and 277V.

10. An electricity meter, characterized in that, The electricity meter includes a storage unit that stores an error reference value generated by a wide-voltage electricity meter accuracy calibration method based on multi-scenario weighted coupling as described in any one of claims 1 to 9.

Images