Precision compensation method under extreme temperature range based on direct-current electric energy meter

Abstract

The invention provides a precision compensation method in an extreme temperature range based on a direct current electric energy meter, and the method comprises the following steps: obtaining the circulation experiment data of a sample direct current electric energy meter in a full temperature range, and constructing a zero offset temperature model and a gain temperature model as a preliminary error compensation model through a least square method; independently correcting the direct-current electric energy meter to be measured according to individual differences; temperature data of the direct current electric energy meter are collected in real time, and dynamic real-time compensation is implemented. An automatic compensation method is adopted, and interference of the thermal hysteresis effect on metering performance can be effectively restrained through a dynamic feedback adjusting mechanism.

Description

Technical Field

[0001] This invention belongs to the field of accuracy control technology for DC metering equipment, specifically a method for accuracy compensation under extreme temperature ranges based on DC energy meters. Background Technology

[0002] DC energy meters are core metering devices in fields such as new energy power generation, industrial measurement and control, and rail transportation. Their measurement accuracy is directly related to the fairness of energy trade settlement and the accuracy of industrial process parameter control. They are also an important technical link to ensure the safe and stable operation of related electrical systems.

[0003] Specifically, in photovoltaic power plants, wind farms and supporting energy storage systems, DC power meters need to operate in outdoor environments with extreme day-night temperature differences; in industrial workshops equipped with high-precision manufacturing equipment, current monitoring instruments often face the test of continuous high temperatures; and in outdoor power supply systems for rail transit (especially lines operating in cold or hot regions), DC metering equipment also needs to cope with extreme weather conditions.

[0004] However, the metering accuracy of DC energy meters faces severe challenges under extreme temperature conditions ranging from -40℃ to 85℃. This temperature range covers environments from extreme freeze-thaw cycles to high-temperature and humid environments, causing significant changes in the performance of core components inside the DC meter. Specifically, the temperature drift coefficients of temperature-sensitive components such as precision resistors and operational amplifiers exhibit non-linear fluctuations with temperature, resulting in significant deviations between the nominal parameters and actual operating parameters of these components. This, in turn, causes distortion in the current sampling signal, ultimately leading to a non-negligible error between the measured value and the actual current value, and this error amplitude increases significantly with the expansion of the temperature range.

[0005] Currently, most compensation methods used in the industry rely on hardware compensation circuits with fixed parameters. These methods can only achieve limited accuracy in error correction within the conventional temperature range of 25℃ to 60℃. When facing extreme temperature ranges of -40℃ to 85℃ and the corresponding application scenarios, the effectiveness of traditional compensation methods drops significantly, failing to meet the actual needs of current high-precision metrology. Therefore, there is an urgent need to develop targeted technical solutions to overcome this key technological bottleneck. Summary of the Invention

[0006] The purpose of this invention is to address the above-mentioned problems by proposing an optimization scheme for the metering accuracy of DC meters in extreme temperature ranges, which can ensure that the metering accuracy of DC meters is stably controlled within ±0.5% in extreme temperature ranges of -40 to 85℃.

[0007] The technical solution of this invention is:

[0008] This invention provides a method for accuracy compensation under extreme temperature ranges based on a DC energy meter, the method comprising the following steps:

[0009] S1. Obtain cyclic experimental data of sample DC energy meters in the full temperature range, and construct a zero bias temperature model and a gain temperature model as preliminary error compensation models using the least squares method.

[0010] S2. Individually calibrate the DC energy meter under test according to individual differences, construct a Bayesian linear regression model using compensation values ​​of multiple preset reference temperatures, and introduce the coefficients of the preliminary error compensation model as prior information.

[0011] The first and second-order temperature coefficients of zero bias and gain are iteratively optimized using the Markov chain Monte Carlo sampling algorithm (MCMC) to generate and store the full-temperature-domain continuous compensation curve of the DC energy meter under test.

[0012] S3. Real-time acquisition of temperature data from the DC power meter and implementation of dynamic real-time compensation; when the temperature changes to the current temperature T, the reference temperature T is obtained based on the full-temperature-range continuous compensation curve of the DC power meter generated in S2. ref The zero bias V 0(ref) Zero-biased temperature coefficient K0, zero-biased second-order temperature coefficient K 02 Then the zero-point offset value V after temperature compensation 0(comp) for;

[0013] V 0(comp) =V 0(ref) +K0×(TT ref )+K 02 ×(TT ref ) 2

[0014] When the temperature changes to the current temperature T, the reference temperature T is obtained according to the error compensation model. ref Gain value G (ref) Gain temperature coefficient K G Second-order temperature coefficient K G2 Then the temperature-compensated gain value G (comp) for:

[0015] G (comp) =G (ref) ×[1+K G ×(TT ref )+K G2 ×(TT ref ) 2 ]

[0016] Based on the zero-point offset value V after temperature compensation 0(comp) and gain value G (comp) For the original measured value V meas(raw) Compensation is performed to obtain the final compensated voltage measurement value V. meas(comp) :

[0017]

[0018] Furthermore, S1 includes:

[0019] S11. Simulate the continuous temperature change environment in extreme temperature range using a high and low temperature cycling test chamber, select multiple sample DC power meters to carry out multiple sets of cyclic experiments, each set of cyclic experiments includes a complete process of low temperature-normal temperature-high temperature-normal temperature.

[0020] S12. In the cyclic experiment, set multiple uniform temperature points, and repeat the measurement after the temperature is constant at each temperature point. Simultaneously record the zero-point bias data and the original gain data of voltage and current. The zero-point bias data is the deviation value of the output signal when there is zero input, and the original gain data is the ratio of the output signal to the input standard signal.

[0021] S13. Based on the zero-point bias data and the original gain data, obtain the zero-point bias compensation value and gain attenuation compensation value at each temperature point. Use the least squares method to perform curve fitting and construct the zero-bias temperature model and the gain temperature model as preliminary error compensation models.

[0022] Furthermore, 500 sets of cyclic experiments were conducted in S1, with the temperature range of the high and low temperature cyclic test chamber being -40 to 85℃.

[0023] Furthermore, in S1, the zero-biased temperature model V 0(T) and gain temperature model G (T) They are respectively:

[0024] V 0(T) =V 0(nor) +K 0_nor ×(TT nor )+K 02_nor ×(TT nor ) 2

[0025] G (T) =G (nor) ×[1+K G_nor ×(TT nor )+K G2_nor ×(TT nor ) 2 ]

[0026] Where T: the actual operating temperature of the equipment, in °C;

[0027] T nor Reference temperature (room temperature, usually set to 25℃), in ℃, is the reference temperature point for calibration and coefficient fitting;

[0028] V 0(T)Zero-bias output at actual operating temperature T, in V or A;

[0029] V 0(nor) Reference temperature T nor The zero-bias reference at that time is expressed in V or A.

[0030] K 0_nor The universal zero-bias first-order temperature coefficient, with units of V / ℃ or A / ℃, describes the zero bias as a function of temperature relative to V. 0(nor) The linear drift rate of the offset;

[0031] K 02_nor The universal zero-bias second-order temperature coefficient, measured in V / ℃ or A / ℃, describes the zero-bias voltage as a function of temperature relative to V. 0(nor) The degree of quadratic nonlinear drift in the offset;

[0032] G (T) Gain coefficient at actual operating temperature T, expressed in amplification factor / ℃, is the gain coefficient at temperature G. (nor) The actual gain value;

[0033] G (nor) Reference temperature T nor The gain reference value at that time, in amplification factor;

[0034] K 0_nor General gain first-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The linear variation ratio with offset;

[0035] K 02_nor General gain second-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The proportion of the second nonlinear change with temperature shift.

[0036] Furthermore, in S2,

[0037] The preset reference temperature points include -40℃, 25℃, 60℃ and 85℃, which correspond to the low temperature limit, normal temperature reference, medium and high temperature nodes and high temperature limit, respectively.

[0038] The temperature range of the DC energy meter under test is segmented based on four reference temperature points, and the first and second order temperature coefficients of zero bias and gain corresponding to each segment are obtained.

[0039] Furthermore, in S2, the Markov chain Monte Carlo sampling algorithm is used to iteratively optimize the Bayesian linear regression model. After iteration, the combustion period samples are discarded, and the mean is calculated based on the remaining samples to determine the zero bias temperature coefficient and the gain temperature coefficient, including first-order and second-order coefficients, and to generate a continuous compensation curve for the entire temperature range.

[0040] Furthermore, it collects temperature data in real time to generate a dynamic compensation trajectory and issues an alarm when the error exceeds the threshold.

[0041] An accuracy compensation system based on a DC energy meter under extreme temperature ranges, the system being configured to perform the aforementioned compensation method.

[0042] A computer-readable storage medium having a computer program stored thereon, which, when executed, implements the compensation method.

[0043] The beneficial effects of this invention are:

[0044] This invention combines massive experimental data modeling with precise individual calibration to construct a high-precision full-temperature-range continuous compensation curve. The dynamic real-time compensation algorithm effectively suppresses the measurement error caused by temperature drift, ensuring that the measurement accuracy of the DC energy meter is stably controlled within ±0.5% in the low-temperature range of -40℃ and the high-temperature range of 85℃, and within a wide range (4A~800A). This significantly improves the adaptability and reliability of the DC energy meter in harsh environments.

[0045] Other features and advantages of the present invention will be described in detail in the following detailed description section. Detailed Implementation

[0046] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention have been shown, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0047] This invention provides a method for accuracy compensation under extreme temperature ranges based on a DC energy meter, comprising the following specific steps:

[0048] Step 1: Obtain cyclic experimental data of the sample DC energy meter in the full temperature range, and construct a zero bias temperature model and a gain temperature model using the least squares method as a preliminary error compensation model;

[0049] Specifically, the process includes: S11, simulating a continuous temperature change environment in an extreme temperature range using a high and low temperature cycling test chamber, selecting multiple sample DC energy meters to conduct multiple sets of cyclic experiments, each set of cyclic experiments including a complete process of low temperature-normal temperature-high temperature-normal temperature; S12, setting multiple uniform temperature points in the cyclic experiment, and repeatedly measuring after maintaining a constant temperature at each temperature point, synchronously recording the zero-point bias data and the original gain data of voltage and current, wherein the zero-point bias data is the deviation value of the output signal at zero input, and the original gain data is the ratio of the output signal to the input standard signal; S13, obtaining the zero-point bias compensation value and gain attenuation compensation value of each temperature point based on the zero-point bias data and the original gain data, using the least squares method for curve fitting, and constructing a zero-bias temperature model and a gain temperature model as preliminary error compensation models respectively.

[0050] Among them, there are 500 sets of cyclic experiments, with the temperature range of the high and low temperature cyclic test chamber being -40 to 85℃; and a zero-biased temperature model V. 0(T) and gain temperature model G (T) They are respectively:

[0051] V 0(T) =V 0(nor) +K 0_nor ×(TT nor )+K 02_nor ×(TT nor ) 2

[0052] G (T) =G (nor) ×[1+K G_nor ×(TT nor )+K G2_nor ×(TT nor ) 2 ]

[0053] Where T: the actual operating temperature of the equipment, in °C;

[0054] T nor Reference temperature (room temperature, usually set to 25℃), in ℃, is the reference temperature point for calibration and coefficient fitting;

[0055] V 0(T) Zero-bias output at actual operating temperature T, in V or A;

[0056] V 0(nor) Reference temperature T nor The zero-bias reference at that time is expressed in V or A.

[0057] k 0_nor The universal zero-bias first-order temperature coefficient, with units of V / ℃ or A / ℃, describes the zero bias as a function of temperature relative to V. 0(nor) The linear drift rate of the offset;

[0058] k 02_nor The universal zero-bias second-order temperature coefficient, measured in V / ℃ or A / ℃, describes the zero-bias voltage as a function of temperature relative to V. 0(nor) The degree of quadratic nonlinear drift in the offset;

[0059] G (T) Gain coefficient at actual operating temperature T, expressed in amplification factor / ℃, is the gain coefficient at temperature G. (nor) The actual gain value;

[0060] G (nor) Reference temperature T norThe gain reference value at that time, in amplification factor;

[0061] K 0_nor General gain first-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The linear variation ratio with offset;

[0062] K 02_nor General gain second-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The proportion of the second nonlinear change with temperature shift.

[0063] In this embodiment, full-temperature-range data is collected. First, full-temperature-range data is collected through a high and low temperature cycling test chamber. The test chamber simulates a continuous temperature change environment from -40 to 85℃. More than 500 sets of cyclic experiments are carried out on the DC meter (each set includes a complete process of "low temperature → room temperature → high temperature → room temperature", covering the temperature change hysteresis effect and steady state). A total of 15 uniform temperature points are set (such as -40℃, -30℃, ..., 80℃, 85℃). Each temperature point is held at a constant temperature for 120 minutes until the meter is thermally stable. The measurement is repeated more than 10 times, and the original data of zero bias and gain (the ratio of the output signal to the input standard signal) of voltage and current are recorded simultaneously. Based on these data, the least squares method is used for curve fitting to construct a zero bias-temperature model and a gain-temperature model, respectively, as preliminary error compensation models to roughly compensate for errors under extreme temperature ranges.

[0064] S2. Individual calibration of the DC energy meter under test is performed to address individual differences. A Bayesian linear regression model is constructed using compensation values ​​of multiple preset reference temperatures. The coefficients of the preliminary error compensation model are introduced as prior information. The first and second order temperature coefficients of zero bias and gain are iteratively optimized using the Markov chain Monte Carlo sampling algorithm (MCMC) to generate and store the full-temperature-range continuous compensation curve of the DC energy meter under test.

[0065] Specifically, the preset reference temperature points include -40℃, 25℃, 60℃ and 85℃, which correspond to the low temperature limit, normal temperature reference, medium and high temperature node and high temperature limit, respectively. Based on the four reference temperature points, the temperature range of the DC energy meter under test is segmented, and the first and second temperature coefficients of zero bias and gain corresponding to each temperature range are obtained.

[0066] Among them, the Markov chain Monte Carlo sampling algorithm iteratively optimizes the Bayesian linear regression model. After iteration, the combustion period samples are discarded, and the mean is calculated based on the remaining samples to determine the zero bias temperature coefficient and the gain temperature coefficient, including first-order and second-order coefficients, and to generate a continuous compensation curve for the entire temperature range.

[0067] In this embodiment, a Bayesian linear regression model is constructed using compensation values ​​at four preset reference temperatures: -40℃, 25℃, 60℃, and 85℃. Bayesian linear regression is a statistical model that combines prior distribution and likelihood function to estimate the posterior distribution of parameters. The coefficients of the preliminary error compensation model in S1 are introduced as prior information, which can effectively reduce the fitting error when there is insufficient experimental data for a single meter, thereby adapting to the individual differences of the core components of each meter, such as the deviation of the temperature coefficient of resistance.

[0068] Markov chain Monte Carlo sampling is used to sample from complex posterior distributions. It approximates the target distribution by constructing a Markov chain, iterating over 1000 times. The first 500 iterations are used as the combustion period to eliminate initial value interference. The mean of the remaining samples determines the zero bias and the first and second-order temperature coefficients of the gain within each temperature range. Finally, a continuous compensation curve across the entire temperature range is generated and stored in the meter's memory. By introducing historical priors and sampling iterations, the generated continuous compensation curve is more accurate, significantly improving the system's safe and stable operation.

[0069] S3. Real-time acquisition of temperature data from the DC power meter and implementation of dynamic real-time compensation; when the temperature changes to the current temperature T, the reference temperature T is obtained based on the full-temperature-range continuous compensation curve of the DC power meter generated in S2. ref The zero bias V 0(ref) Zero-biased temperature coefficient K0, zero-biased second-order temperature coefficient K 02 Then the zero-point offset value V after temperature compensation 0(comp) for;

[0070] V 0(comp) =V 0(Tref) +K0×(TT ref )+K 02 ×(TT ref ) 2

[0071] When the temperature changes to the current temperature T, the reference temperature T is obtained according to the error compensation model. ref Gain value G (ref) Gain temperature coefficient K G Second-order temperature coefficient K G2 Then the temperature-compensated gain value G (comp) for:

[0072] G (comp) =G (ref) ×[1+K G ×(TT ref )+K G2 ×(TT ref ) 2 ]

[0073] Based on the zero-point offset value V after temperature compensation 0(comp) and gain value G (comp) For the original measured value V meas(raw) Compensation is performed to obtain the final compensated voltage measurement value V. meas(comp) :

[0074]

[0075] In this embodiment, temperature data is collected in real time by the built-in sensor of the meter. When the temperature reaches T, the parameters are obtained from the continuous compensation curve stored in S2, the compensation value is calculated, and then the original measurement value is corrected. The compensation measurement value of the present invention is more accurate, thus improving the accuracy of energy settlement.

[0076] The above content constitutes a specific implementation method for the accuracy compensation method under extreme temperature range based on DC energy meters.

[0077] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for accuracy compensation under extreme temperature ranges based on a DC energy meter, characterized in that, The method includes the following steps: S1. Obtain cyclic experimental data of sample DC energy meters in the full temperature range, and construct a zero bias temperature model and a gain temperature model as preliminary error compensation models using the least squares method. S2. Individually calibrate the DC energy meter under test according to individual differences, construct a Bayesian linear regression model using compensation values ​​of multiple preset reference temperatures, and introduce the coefficients of the preliminary error compensation model as prior information. The first and second-order temperature coefficients of zero bias and gain are iteratively optimized using the Markov chain Monte Carlo sampling algorithm (MCMC) to generate and store the full-temperature-domain continuous compensation curve of the DC energy meter under test. S3. Real-time acquisition of temperature data from the DC power meter and implementation of dynamic real-time compensation; when the temperature changes to the current temperature T, the reference temperature T is obtained based on the full-temperature-range continuous compensation curve of the DC power meter generated in S2. ref The zero bias V 0(ref) Zero-biased temperature coefficient K0, zero-biased second-order temperature coefficient K 02 Then the zero-point offset value V after temperature compensation 0(comp) for; V 0(comp) =V 0(ref) +K0×(T-T ref )+K 02 ×(T-T ref ) 2 When the temperature changes to the current temperature T, the reference temperature T is obtained according to the error compensation model. ref Gain value G (ref) Gain temperature coefficient K G Second-order temperature coefficient K G2 Then the temperature-compensated gain value G (comp) for: G (comp) =G (ref) ×[1+K G ×(T-T ref )+K G2 ×(T-T ref ) 2 ] Based on the zero-point offset value V after temperature compensation 0(comp) and gain value G (comp) For the original measured value V meas(raw) Compensation is performed to obtain the final compensated voltage measurement value V. meas(comp) :

2. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 1, characterized in that... S1 includes: S11. Simulate the continuous temperature change environment in extreme temperature range using a high and low temperature cycling test chamber, select multiple sample DC power meters to carry out multiple sets of cyclic experiments, each set of cyclic experiments includes a complete process of low temperature-normal temperature-high temperature-normal temperature. S12. In the cyclic experiment, set multiple uniform temperature points, and repeat the measurement after the temperature is constant at each temperature point. Simultaneously record the zero-point bias data and the original gain data of voltage and current. The zero-point bias data is the deviation value of the output signal when there is zero input, and the original gain data is the ratio of the output signal to the input standard signal. S13. Based on the zero-point bias data and the original gain data, obtain the zero-point bias compensation value and gain attenuation compensation value at each temperature point. Use the least squares method to perform curve fitting and construct the zero-bias temperature model and the gain temperature model as preliminary error compensation models.

3. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 2, characterized in that... S1 has 500 sets of cyclic experiments, and the temperature range of the high and low temperature cyclic test chamber is -40 to 85℃.

4. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 2, characterized in that... In S1, the zero-biased temperature model V 0(T) and gain temperature model G (T) They are respectively: V 0(T) =V 0(nor) +K 0_nor ×(T-T nor )+K 02_nor ×(T-T nor ) 2 G (T) =G (nor) ×[1+K G_nor ×(T-T nor )+K G2_nor ×(T-T nor ) 2 ] Where T: the actual operating temperature of the equipment, in °C; T nor Reference temperature (room temperature, usually set to 25℃), in ℃, is the reference temperature point for calibration and coefficient fitting; V 0(T) Zero-bias output at actual operating temperature T, in V or A; V 0(nor) Reference temperature T nor The zero-bias reference at that time is expressed in V or A. K 0_nor The universal zero-bias first-order temperature coefficient, with units of V / ℃ or A / ℃, describes the zero bias as a function of temperature relative to V. 0(nor) The linear drift rate of the offset; K 02_nor The universal zero-bias second-order temperature coefficient, measured in V / ℃ or A / ℃, describes the zero-bias voltage as a function of temperature relative to V. 0(nor) The degree of quadratic nonlinear drift in the offset; G (T) Gain coefficient at actual operating temperature T, expressed in amplification factor / ℃, is the gain coefficient at temperature G. (nor) The actual gain value; G (nor) Reference temperature T nor The gain reference value at that time, in amplification factor; K 0_nor General gain first-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The linear variation ratio with offset; K 02_nor General gain second-order temperature coefficient, in units of gain factor / ℃, describes the gain relative to G. (nor) The proportion of the second nonlinear change with temperature shift.

5. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 1, characterized in that... In S2, The preset reference temperature points include -40℃, 25℃, 60℃ and 85℃, which correspond to the low temperature limit, normal temperature reference, medium and high temperature nodes and high temperature limit, respectively. The temperature range of the DC energy meter under test is segmented based on four reference temperature points, and the first and second order temperature coefficients of zero bias and gain corresponding to each segment are obtained.

6. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 1, characterized in that... In S2, the Markov chain Monte Carlo sampling algorithm is used to iteratively optimize the Bayesian linear regression model. After iteration, the combustion period samples are discarded, and the mean is calculated based on the remaining samples to determine the zero bias temperature coefficient and the gain temperature coefficient, including first-order and second-order coefficients, and to generate a continuous compensation curve for the entire temperature range.

7. The accuracy compensation method for extreme temperature range based on a DC energy meter according to claim 1, characterized in that... The system collects temperature data in real time, generates a dynamic compensation trajectory, and issues an alarm when the error exceeds a threshold.

8. A precision compensation system based on a DC energy meter under extreme temperature ranges, characterized in that, The system is configured to perform the compensation method as described in any one of claims 1-8.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that... When the program is executed, the compensation method as described in any one of claims 1-8 is implemented.

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