Electric energy meter dual temperature compensation method and device of temperature sensor

Abstract

The invention discloses an electric energy meter dual temperature compensation method and device for a temperature sensor, and relates to the technical field of electric energy meter metering precision compensation. The method comprises the steps that the real-time temperature of an electric energy meter is obtained through the temperature collection function of a built-in metering chip of the electric energy meter, and the environment temperature is calculated by combining the thermodynamic principle and the experiment fitting relation; estimating the real-time temperature of the manganese-copper sampling resistor according to the environment temperature, a pre-stored manganese-copper temperature curve and a three-node thermal network model, calling a dual temperature compensation model to calculate a compensation coefficient, cooperatively correcting the temperature deviations of the two, and outputting a metering result; according to the invention, the metering chip is provided with the temperature acquisition module, no additional hardware temperature sensor is needed, the equipment cost is reduced, the real-time temperature of the manganin sampling resistor is accurately estimated by calculating the environment temperature in combination with a manganin temperature curve and a thermal network model, and the temperature drift of the manganin sampling resistor and the temperature drift of the metering chip are subjected to dual cooperative compensation. The problem of insufficient single heat source compensation precision is solved.

Description

Technical Field

[0001] This invention relates to the field of electricity meter metering accuracy compensation technology, specifically to a dual temperature compensation method and device for electricity meters with a temperature sensor. Background Technology

[0002] A temperature sensor is a device or apparatus that can sense the physical quantity of temperature and convert it into an output electrical signal. Its core significance lies in providing direct data for temperature monitoring of various electronic devices. It is a fundamental component for realizing adaptive temperature adjustment and precision calibration of equipment and is widely used in industrial control, electronic metrology and other fields to ensure the stable operation of equipment in complex temperature environments.

[0003] As the core device in the power system for measuring electricity consumption, the electricity meter directly relates to the distribution of profits between power supply companies and electricity users. Its accuracy and stability are of significant practical importance. During actual operation, temperature variation is one of the key factors affecting the meter's accuracy. The manganese-copper sampling resistor inside the meter generates self-heat due to current flow and is also affected by the external ambient temperature, causing its resistance value to drift. The metering chip and surrounding capacitors also experience abnormal temperature increases due to heat conduction and radiation, leading to performance deviations. The combined effect of these factors results in metering errors. Therefore, temperature compensation technology for electricity meters is a core means to offset temperature interference and ensure metering accuracy, and is crucial for improving the reliability and impartiality of electricity meters.

[0004] However, existing temperature compensation technologies for electricity meters still have certain shortcomings. Some solutions rely on pure software algorithms, using thermodynamic models or empirical formulas for compensation without combining temperature sensors for direct temperature detection. The compensation accuracy is limited by the model fit or empirical data. Some solutions, although employing temperature sensing technology, require additional hardware temperature sensors, increasing equipment costs and installation complexity. Some solutions only compensate for a single heat source, failing to consider the combined effects of temperature drift of the metering chip itself and temperature drift of the manganese-copper resistor, making them difficult to adapt to complex thermal environments. Other solutions reduce the self-heating effect by improving material heat treatment processes, but cannot achieve dynamic compensation and lack practicality. Therefore, developing a dual temperature compensation method and device for electricity meters with a temperature sensor is of great significance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a dual temperature compensation method and device for electricity meters with temperature sensors. It can utilize the temperature acquisition module built into the metering chip, eliminating the need for additional hardware temperature sensors, reducing equipment costs and simplifying the installation structure. By calculating the ambient temperature and combining the manganese-copper temperature curve and thermal network model, it accurately estimates the real-time temperature of the manganese-copper sampling resistor, achieving dual collaborative compensation for the temperature drift of the manganese-copper sampling resistor and the temperature drift of the metering chip itself. With the help of a dynamic thermal network model and dual compensation algorithm, it can adapt to complex thermal environments under different current loads, achieve dynamic compensation, and effectively improve the metering accuracy and long-term stability of the electricity meter.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dual temperature compensation method for an energy meter with a temperature sensor, the method comprising the following steps: S1. Obtain the real-time temperature of the metering chip through the temperature acquisition function of the built-in metering chip in the energy meter. S2. Based on the real-time temperature of the metering chip, and combined with thermodynamic principles, establish a correlation to calculate the operating environment temperature of the electricity meter. S3. Based on the ambient temperature and the pre-stored manganese-copper temperature curve, estimate the real-time temperature of the manganese-copper sampling resistor using the three-node thermal network model of the manganese-copper environment of the metering chip. S4. Call the dual temperature compensation model, input the real-time temperature of the metering chip and the real-time temperature of the manganese copper sampling resistor to calculate the compensation coefficient, use the compensation coefficient to collaboratively correct the real-time temperature deviation of the manganese copper sampling resistor and the real-time temperature deviation of the metering chip, and output the metering result.

[0007] Furthermore, the ambient temperature calculation in S2 is performed according to the following procedure: Multiple sets of control experiments were conducted, covering the normal operating temperature range of the electricity meter, and the ambient temperature and the temperature data of the metering chip under the corresponding conditions were recorded simultaneously. Based on the recorded experimental data, a functional relationship between the temperature of the metering chip and the ambient temperature was constructed through data fitting; Substitute the real-time temperature of the metering chip obtained in step S1 into the function relationship to calculate the operating environment temperature of the electricity meter.

[0008] Furthermore, the real-time temperature estimation of the manganese-copper sampling resistor in S3 includes the following steps: Call the pre-stored three-node thermal network model parameters, which include the thermal conductivity and thermal radiation coefficients between the metering chip and manganese copper, manganese copper and the environment, and between the metering chip and the environment; Determine the load range based on the current current load of the electricity meter, and select the corresponding preset thermal time constant; Combining the ambient temperature calculated in step S2, the pre-stored manganese-copper temperature curve, and the selected thermal time constant, the real-time temperature estimation of the manganese-copper sampling resistor is completed by substituting them into the three-node thermal network model.

[0009] Furthermore, the compensation coefficient calculation in step S4 is performed according to the following steps: Based on the real-time temperature of the metering chip, the preset metering chip temperature offset correction coefficient table is consulted to obtain the correction coefficient on the metering chip side. Based on the real-time temperature of the manganese copper sampling resistor and combined with the pre-stored manganese copper temperature curve, the correction coefficient for the manganese copper side is calculated. The correction coefficients for the metering chip side and the manganese-copper side are weighted and summed according to a preset ratio to obtain the final compensation coefficient.

[0010] Furthermore, in S1, temperature acquisition is completed by the temperature acquisition unit built into the metering chip. The acquisition target is the temperature of the core area of ​​the metering chip. The acquisition interval is dynamically adjusted according to the current load of the electricity meter. When the current load exceeds the set threshold, short-interval acquisition is used, and when the current load does not exceed the set threshold, long-interval acquisition is used.

[0011] Furthermore, the manganese-copper temperature curve pre-stored in S3 is obtained through specialized testing. The test covers the extreme temperature range that may occur in the working environment of the electricity meter. The actual resistance value of the manganese-copper sampling resistor is measured at different temperature nodes. The temperature data is correlated with the corresponding resistance value to form a continuous temperature-resistance relationship curve, which is stored in the built-in storage unit of the electricity meter.

[0012] Furthermore, the weight parameters of the dual temperature compensation model in S4 are determined through calibration experiments. The calibration experiments are carried out in batches under different temperature environments and different load conditions, and the measurement error data under each experimental scenario are recorded. The weight parameters are obtained by reverse iterative optimization based on the error correction effect and preset in the model.

[0013] Furthermore, the original metering data used in the collaborative correction in S4 includes the instantaneous voltage sampling value output by the voltage sampling module and the instantaneous current sampling value output by the current sampling module. The compensation coefficient is calculated with the instantaneous voltage sampling value and the instantaneous current sampling value respectively to obtain the corrected voltage data and current data. The power metering calculation is completed based on the corrected data.

[0014] A dual temperature compensation device for an energy meter with a temperature sensor is applicable to the aforementioned dual temperature compensation method for an energy meter with a temperature sensor. The device includes an energy meter body, which integrates a metering chip module, an ambient temperature calculation unit, a manganese-copper temperature estimation unit, a dual compensation calculation unit, and a metering correction unit. The metering chip module is electrically connected to the ambient temperature calculation unit, the ambient temperature calculation unit is electrically connected to the manganese-copper temperature estimation unit, the dual compensation calculation unit is electrically connected to the metering chip module and the manganese-copper temperature estimation unit respectively, and the metering correction unit is electrically connected to the dual compensation calculation unit. The metering chip module has a built-in temperature acquisition unit, which is used to acquire the temperature signal of the metering chip itself in real time and convert it into a processable electrical signal. The ambient temperature estimation unit pre-stores the correlation algorithm between the temperature of the metering chip and the ambient temperature, and is used to receive the temperature signal of the metering chip output by the metering chip module to estimate the real-time ambient temperature. The manganese copper temperature estimation unit pre-stores the manganese copper temperature curve and the parameters of the three-node thermal network model. It is used to receive the ambient temperature signal output by the ambient temperature calculation unit, match the model parameters corresponding to the current load, and estimate the real-time temperature of the manganese copper sampling resistor. The dual compensation calculation unit has a built-in dual temperature compensation model, which is used to receive the real-time temperature signals of the metering chip and the real-time temperature signals of the manganese copper sampling resistor respectively, and calculate and generate dual temperature compensation coefficients. The metering correction unit is used to receive the original metering data from the electricity meter and the compensation coefficient output by the dual compensation calculation unit, to perform collaborative correction on the original metering data and output accurate metering results.

[0015] Furthermore, the manganese-copper temperature estimation unit includes a built-in storage module, which pre-stores the manganese-copper temperature curve, the thermal conductivity coefficient and thermal radiation coefficient of the three-node thermal network model, and the thermal time constant corresponding to different current load ranges. The thermal conductivity coefficient and thermal radiation coefficient are obtained through multiple sets of temperature gradient experiments, and the thermal time constant is divided and stored according to the current load range. After receiving the ambient temperature signal, the manganese-copper temperature estimation unit matches the parameters corresponding to the current load and substitutes them into the model to complete the temperature estimation.

[0016] Compared with existing technologies, the dual temperature compensation method and device for this temperature sensor in energy meters has the following advantages: This invention utilizes the built-in temperature acquisition module of the metering chip, eliminating the need for additional hardware temperature sensors, thus reducing equipment costs and simplifying the installation structure. By calculating the ambient temperature and combining it with the manganese-copper temperature curve and thermal network model, it accurately estimates the real-time temperature of the manganese-copper sampling resistor, achieving dual collaborative compensation for the temperature drift of the manganese-copper sampling resistor and the temperature drift of the metering chip itself. This solves the problem of insufficient compensation accuracy from a single heat source. With the help of a dynamic thermal network model and dual compensation algorithm, it can adapt to complex thermal environments under different current loads, achieving dynamic compensation, effectively improving the metering accuracy and long-term stability of the electricity meter, ensuring fairness of interests between the power supplier and the power consumer, and possessing significant practicality and economy.

[0017] 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

[0018] 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.

[0019] Figure 1 A flowchart illustrating a dual temperature compensation method for an energy meter with a temperature sensor. Figure 2 A flowchart illustrating a dual temperature compensation method for a temperature sensor in an energy meter. Figure 3 This is a schematic diagram of a dual temperature compensation device for an energy meter with a temperature sensor. Detailed Implementation

[0020] 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.

[0021] This invention provides a dual temperature compensation method and device for an energy meter with a temperature sensor, the core of which is to improve metering accuracy and stability. The specific details are as follows: See Figure 1 and Figure 2 The method is as follows: First, the real-time temperature of the core area of ​​the electricity meter is acquired through the built-in temperature acquisition unit of the metering chip. The acquisition interval is dynamically adjusted according to the current load; shorter intervals are acquired when the load exceeds a threshold, and longer intervals are acquired when the load does not exceed a threshold. Next, the operating ambient temperature of the electricity meter is calculated by fitting a functional relationship between the metering chip temperature and the ambient temperature based on multiple sets of comparative experimental data and combining thermodynamic principles. Subsequently, the parameters of a pre-stored three-node thermal network model of metering chip-manganese copper-ambient environment are called, and the corresponding thermal time constant is determined according to the current load. Combined with the ambient temperature and the pre-stored manganese copper temperature curve, the real-time temperature of the manganese copper sampling resistor is estimated. Finally, a dual temperature compensation model is established. The correction coefficient on the metering chip side is obtained by querying the metering chip temperature offset correction coefficient table, and the correction coefficient on the manganese copper side is obtained by combining the manganese copper temperature curve. The compensation coefficient is obtained by weighting and summing according to preset weights. This compensation coefficient is used to correct the instantaneous voltage and current sampling values ​​and output accurate metering results.

[0022] See Figure 3 The device is as follows: The electricity meter integrates a metering chip module, an ambient temperature calculation unit, a manganese-copper temperature estimation unit, a dual compensation calculation unit, and a metering correction unit, all of which are electrically connected in sequence. The metering chip module is responsible for collecting its own temperature signal; the ambient temperature calculation unit calculates the real-time ambient temperature using a pre-stored algorithm; the manganese-copper temperature estimation unit's built-in storage module stores the manganese-copper temperature curve, thermal network model parameters, and thermal time constants for different load ranges, and matches these parameters to complete temperature estimation after receiving the ambient temperature signal; the dual compensation calculation unit has a built-in compensation model that generates dual temperature compensation coefficients; and the metering correction unit receives the original metering data and compensation coefficients, completes collaborative correction, and outputs the results.

[0023] This solution eliminates the need for additional hardware temperature sensors and addresses the issue of insufficient accuracy in compensation from a single heat source through dual collaborative compensation. It adapts to complex thermal environments under different current loads and achieves dynamic compensation.

[0024] Example 1 This embodiment is applied to power distribution systems in residential communities and smart power distribution scenarios in commercial complexes. In these scenarios, electricity meters need to operate under complex conditions for extended periods, with large fluctuations in current load, frequently switching from low loads for daily residential electricity use to high loads for commercial equipment operation. At the same time, the ambient temperature is affected by seasonal changes and equipment heat dissipation, fluctuating between -20℃ and 60℃. The manganese-copper sampling resistor inside the electricity meter will generate significant self-heating due to current flow, and the metering chip and surrounding components will experience temperature increases due to heat conduction and heat radiation. The temperature drift problem caused by both factors seriously affects the metering accuracy, posing risks to the interests of both the power supplier and the electricity consumer. This embodiment uses dual temperature compensation technology to achieve accurate correction of the above-mentioned dual temperature drift, ensuring the accuracy and impartiality of metering data.

[0025] See Figure 1 , Figure 2 and Figure 3 The specific implementation process is as follows: This embodiment uses a certain model of single-phase smart energy meter with a built-in metering chip of CS5463. This metering chip has a built-in temperature acquisition unit that can directly acquire the temperature of the core area. The manganese copper sampling resistor is a 0.01Ω high-precision manganese copper shunt. The energy meter's built-in storage unit pre-stores the relevant parameters and curves of the experimental calibration. The entire implementation process strictly follows the procedure of temperature acquisition, ambient temperature calculation, manganese copper temperature estimation, and dual compensation calculation.

[0026] Real-time temperature acquisition via the metering chip begins by activating the electricity meter's temperature acquisition function. Utilizing the CS5463 metering chip's built-in temperature acquisition unit, temperature data is collected from the chip's core area. The acquisition interval is dynamically adjusted based on the electricity meter's current load. Specifically, the logic is as follows: the internal current sampling module monitors the load current in real time. When the load current exceeds a set threshold, a short-interval acquisition mode is used; when the load current does not exceed the set threshold, a long-interval acquisition mode is used. During acquisition, the temperature acquisition unit converts the sensed temperature into a 16-bit digital electrical signal and transmits it to the ambient temperature calculation unit. The entire acquisition process requires no additional hardware intervention, relying entirely on the metering chip's existing functions, ensuring low cost and high reliability.

[0027] Before estimating the ambient temperature of the electricity meter, multiple sets of control experiments were conducted. The experiments covered the normal operating temperature range of the electricity meter. The experimental environment was controlled by a high-precision constant temperature chamber with the temperature set from -20℃ to 60℃, in 5℃ increments. Under each temperature gradient, different current load conditions were simulated and run continuously for 2 hours. The actual ambient temperature of the constant temperature chamber and the real-time temperature data of the metering chip were recorded simultaneously. Each set of experiments was repeated 3 times, and the average temperature of the metering chip was taken as the valid data under that operating condition.

[0028] Based on multiple sets of recorded experimental data, a data fitting method was used to construct the correlation between the temperature of the metering chip and the ambient temperature. In the specific implementation of this embodiment, the least squares method was used to perform linear fitting on the experimental data to obtain the ambient temperature estimation formula: ,in This is the ratio of the chip temperature to the ambient temperature. The constants are derived from the linear regression analysis results of the aforementioned multiple sets of control experimental data. By minimizing the error of all experimental data points, the fitting formula is ensured to accurately reflect the actual correlation between the temperature of the metering chip and the ambient temperature. In practical applications, the real-time temperature of the metering chip is collected. By substituting the values ​​into the formula above, the current operating ambient temperature of the electricity meter can be quickly calculated. .

[0029] The real-time temperature estimation of the manganese copper sampling resistor relies on a pre-stored three-node thermal network model and the manganese copper temperature curve. The parameters of the three-node thermal network model include the thermal conductivity coefficient between the metering chip and the manganese copper, the thermal conductivity coefficient between the manganese copper and the environment, the thermal conductivity coefficient between the metering chip and the environment, and the corresponding thermal radiation coefficient. These parameters are calibrated through multiple sets of temperature gradient experiments. The specific calibration process is as follows: different temperature gradients are set in a constant temperature chamber, and the heat transfer rate between the metering chip, the manganese copper, and the environment is measured respectively. Combining the theories of thermal conduction and thermal radiation, the values ​​of each coefficient are calculated and stored in the built-in storage module of the manganese copper temperature estimation unit.

[0030] Meanwhile, the pre-stored manganese copper temperature curve was obtained through specialized testing, covering an extreme temperature range of -40℃ to 85℃. Each 2℃ test node was used. At each temperature node, the actual resistance value of the manganese copper sampling resistor was measured using a high-precision resistance tester. The temperature data was correlated with the corresponding resistance value, and a continuous temperature-resistance relationship curve was generated through an interpolation algorithm and stored in the built-in storage module.

[0031] Furthermore, to address the impact of different current loads on the heat transfer rate, the current load has been pre-divided into three intervals, and the corresponding thermal time constant for each interval has been determined experimentally and stored in the storage module. In the specific implementation of this embodiment, combining the three-node thermal network model, ambient temperature, and manganese-copper temperature curve, the real-time temperature estimation formula for the manganese-copper sampling resistor is obtained: ,in The thermal conductivity coupling coefficient is... The coefficients for the current-induced thermal effect and the model calculations are derived from experimental calibration results under multiple conditions of different temperatures and loads. By comparing experimental measurements with model calculations, the coefficients are continuously adjusted to improve estimation accuracy. This represents the thermal time constant corresponding to the current load range, derived from specific experimental data divided according to the current load range. For real-time load current, For ambient temperature The reference resistance value of the manganese copper sampling resistor is taken from the pre-stored manganese copper temperature curve.

[0032] In actual estimation, the manganese-copper temperature estimation unit first receives the output from the ambient temperature calculation unit. Then, based on the real-time load current from the current sampling module, the corresponding load range is determined, and the corresponding thermal time constant is matched. Finally Substituting the parameters into the above formula, the real-time temperature of the manganese copper sampling resistor is obtained. The estimate.

[0033] Dual temperature compensation and measurement result output: First, a dual temperature compensation model is established. To address the temperature drift of the metering chip, the measurement error of the metering chip at different temperatures is pre-tested experimentally, and a table of temperature offset correction coefficients for the metering chip is established. This table is stored in the dual compensation calculation unit. When the real-time temperature of the metering chip is obtained... Subsequently, the dual compensation calculation unit obtains the corresponding correction coefficients on the metering chip side by looking up a table. To address the temperature drift of the manganese copper sampling resistor, based on the pre-stored manganese copper temperature profile, and according to the estimated... The actual change in manganese copper resistance is calculated, and the correction coefficient for the manganese copper side is obtained by combining the correlation between manganese copper resistance and measurement error. .

[0034] In the specific implementation of this embodiment, the final compensation coefficient is calculated using a weighted summation method, as shown in the following formula. ,in The weight of the correction coefficient on the metering chip side. The weights for the manganese-copper side correction coefficients are derived from the inverse iterative optimization results of multiple calibration experiments. The calibration experiments were conducted in batches under different temperature environments and load conditions, recording measurement error data under each experimental scenario. With the goal of achieving optimal error correction, the weight parameters were continuously adjusted through an iterative algorithm to ultimately determine the optimal value. and And it is preset in the model.

[0035] After the compensation coefficient is calculated, the dual compensation calculation unit transmits it to the metering correction unit. The metering correction unit receives the instantaneous voltage sampling value output by the voltage sampling module and the instantaneous current sampling value output by the current sampling module. It multiplies the compensation coefficient with the instantaneous voltage sampling value and the instantaneous current sampling value respectively to obtain the corrected voltage data and current data. Finally, based on the corrected voltage and current data, it calculates the energy consumption value according to the energy metering formula and outputs the final metering result through the display module and communication module of the energy meter.

[0036] In summary, this embodiment, by relying on the built-in temperature acquisition function of the metering chip, eliminates the need for additional hardware temperature sensors, significantly reducing equipment costs. It also simplifies the internal structure of the electricity meter, avoiding reliability risks associated with additional hardware installation. Through three core algorithm formulas, it achieves accurate estimation of ambient temperature and manganese-copper temperature, and scientific calculation of dual compensation coefficients. The coefficients and weights are calibrated or optimized based on multiple sets of experimental data, ensuring the accuracy of the compensation model and significantly improving metering precision. Furthermore, the dynamically adjusted temperature acquisition interval and the thermal time constant adapted to different loads enable the solution to adapt to complex load fluctuations and temperature changes, significantly improving long-term operational stability. This effectively solves the metering injustice caused by temperature drift in residential communities and commercial complexes, fully protecting the legitimate rights and interests of both power suppliers and consumers, and demonstrating significant practicality, economy, and innovation.

[0037] Example 2 This embodiment is applied to industrial production workshops and new energy vehicle charging stations. In industrial workshops, various production equipment frequently start and stop, and the current load exhibits drastic fluctuations in a short period of time, with peak loads reaching hundreds of amperes. New energy charging stations, on the other hand, experience frequent switching between fast and slow charging modes and dynamic adjustments to charging power. The ambient temperature in both scenarios is affected not only by the season but also by factors such as equipment heat dissipation and charging heat generation, with fluctuations ranging from -10℃ to 70℃. Furthermore, there is a strong problem of electromagnetic interference. The self-heating effect of the manganese copper sampling resistor inside the electricity meter is even more significant. The temperature drift of the metering chip and surrounding components due to heat conduction and radiation, combined with electromagnetic interference, leads to a significant increase in the risk of metering errors. Based on the aforementioned embodiments, this embodiment is optimized and adapted for complex working conditions and electromagnetic interference scenarios, achieving accurate implementation of dual temperature compensation.

[0038] See Figure 1 , Figure 2 and Figure 3 The specific implementation process is as follows: This embodiment uses a three-phase smart energy meter with an ADE7758 built-in metering chip. This metering chip has a built-in high-resolution temperature acquisition unit, which can achieve accurate acquisition of the core temperature. The manganese copper sampling resistor uses a 0.005Ω ultra-high precision shunt. In addition to pre-storing the parameters and curves in the aforementioned embodiment, the energy meter's built-in storage unit additionally stores correction parameters calibrated for electromagnetic interference scenarios. The implementation process continues the core framework of temperature acquisition, ambient temperature calculation, manganese copper temperature estimation, and dual compensation calculation, and is optimized for scenario characteristics.

[0039] The metering chip performs real-time temperature acquisition and filtering. Upon activating the electricity meter's temperature acquisition function, the temperature of the core area of ​​the metering chip is collected using the built-in temperature acquisition unit of the ADE7758 metering chip. The dynamic adjustment logic of the acquisition interval is consistent with the previous embodiment, i.e., switching between short-interval and long-interval acquisition modes based on the comparison between the real-time load current and a set threshold. Considering the strong electromagnetic interference in industrial scenarios and charging stations, the acquired digital temperature signal needs to be filtered. A moving average filtering algorithm is used to calculate the average of five consecutive temperature acquisitions, eliminating abnormal fluctuations caused by electromagnetic interference and ensuring the stability of the temperature signal transmitted to the ambient temperature calculation unit. The entire acquisition and filtering process requires no additional hardware support and is implemented entirely through the metering chip's built-in functions and software algorithms, balancing low cost and anti-interference capabilities.

[0040] The calculation of the operating environment temperature and the adaptation of the operating conditions of the electricity meter are based on the previous multiple sets of control experiments. The experimental data for the environmental temperature calculation are supplemented by test data for high temperature and high humidity and electromagnetic interference conditions. The experimental environment temperature covers -10℃ to 70℃, with a gradient set in every 4℃. Under each temperature gradient, dynamic load conditions such as the start-up and shutdown of industrial equipment and the switching of new energy charging power are simulated. At the same time, electromagnetic interference of different intensities is applied and the test is run continuously for 3 hours. The actual ambient temperature of the constant temperature chamber, the real-time temperature data of the metering chip and the electromagnetic interference intensity parameters are recorded simultaneously. Each set of experiments is repeated 4 times, and the average temperature of the metering chip is taken as the valid data.

[0041] Based on the expanded experimental data, the least squares method was still used for linear fitting, and the formula for estimating the ambient temperature remained the same: In practical applications, the real-time temperature of the metering chip after filtering is first substituted into the formula to obtain the preliminary ambient temperature. Then, based on the real-time monitored electromagnetic interference intensity, the pre-stored interference correction coefficient is called to fine-tune the preliminary ambient temperature to ensure the accuracy of the ambient temperature calculation under electromagnetic interference scenarios. The fine-tuning coefficient is calibrated through electromagnetic interference gradient experiments and stored in the ambient temperature calculation unit.

[0042] The real-time temperature estimation and interference correction of the manganese copper sampling resistor relies on the aforementioned three-node thermal network model and manganese copper temperature curve. Based on the temperature gradient experimental calibration, the model parameters are further calibrated to account for the influence of electromagnetic interference. The specific process is as follows: different temperature gradients and electromagnetic interference intensities are set in a constant temperature chamber, the heat transfer rate between the metering chip, the manganese copper environment, and the environment is measured, and the thermal conductivity and thermal radiation coefficients are corrected by combining the influence law of electromagnetic interference on heat transfer and stored in the built-in storage module of the manganese copper temperature estimation unit.

[0043] The test range of the manganese-copper temperature curve is extended to -30℃ to 90℃, with a test node set every 3℃. In addition to measuring the resistance value at different temperatures, the influence of electromagnetic interference intensity on the resistance measurement value is recorded simultaneously. Interference errors are eliminated through data correction to form an accurate temperature-resistance relationship curve. The current load range is divided into three ranges as mentioned above, and an ultra-large load range is added. Special experiments are added for peak loads in industrial workshops and high-power scenarios of fast charging of new energy vehicles to determine the corresponding thermal time constant of this range.

[0044] The formula for real-time temperature estimation using the manganese-copper sampling resistor remains unchanged: In actual estimation, the manganese copper temperature estimation unit first receives the ambient temperature after electromagnetic interference correction, then determines the load range according to the real-time load current, matches the corresponding thermal time constant, and calls the thermal conductivity correction term corresponding to electromagnetic interference to supplement and correct the formula calculation results, and finally obtains the accurate real-time temperature of the manganese copper sampling resistor.

[0045] Dual temperature compensation and dynamic correction: Based on the above, the dual temperature compensation model adds a dynamic adjustment mechanism for the compensation coefficient. In addition to querying the preset metering chip temperature offset correction coefficient table, a real-time metering error feedback link is added to obtain the correction coefficient on the metering chip side. By comparing the original metering data before correction with the reference data of the standard power metering device, the real-time error value is calculated. If the error value exceeds the preset range, the metering chip side correction coefficient obtained from the table is fine-tuned.

[0046] The calculation of the correction coefficient on the manganese copper side is based on the pre-stored manganese copper temperature curve and the estimated real-time temperature of the manganese copper sampling resistor. It also takes into account the rate of change of the real-time load current. If the rate of change of the current exceeds the set threshold, the correction coefficient on the manganese copper side is dynamically adjusted according to the pre-stored rate correction parameter to ensure timely compensation in scenarios with drastic load fluctuations.

[0047] The compensation coefficient is calculated using the weighted summation formula: Based on the reverse iterative optimization of the aforementioned calibration experiment, a real-time feedback iterative mechanism is added to the weight parameters. Every time the electricity meter completes a metering cycle, it compares the corrected metering data with the standard reference data, calculates the error value, and performs small iterative adjustments to the weight parameters with the goal of minimizing the error. The latest weight value is then stored to achieve adaptive optimization of the compensation model.

[0048] The metering correction unit receives the filtered instantaneous voltage and current sample values, multiplies the dynamically adjusted compensation coefficient with the two types of sample values ​​respectively, and obtains the corrected voltage and current data. Based on the corrected data, it completes the three-phase power metering calculation and uploads the metering results to the monitoring platform in real time through the industrial-grade communication module of the power meter, while simultaneously displaying them on the local display module.

[0049] In summary, based on the aforementioned embodiments, this embodiment optimizes electromagnetic interference adaptation and dynamic load response for the complex operating conditions of industrial production workshops and new energy charging stations. Without requiring additional hardware, it achieves precise adaptation to complex scenarios solely through expanded experimental data, algorithm optimization, and parameter calibration. The three core algorithm formulas used, supported by expanded experimental data and a new correction mechanism, maintain high accuracy. Compared to traditional solutions and the aforementioned basic embodiments, its anti-electromagnetic interference capability and dynamic load adaptability are significantly improved. It can accurately handle complex situations involving drastic current fluctuations, rapid temperature changes, and superimposed electromagnetic interference, effectively solving problems such as inaccurate equipment energy consumption metering and disputes over new energy charging metering in industrial production. This protects the legitimate rights and interests of industrial enterprises, charging operators, and electricity users, further expanding the application scenarios of this dual temperature compensation scheme and demonstrating its flexibility and scalability.

[0050] 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 dual temperature compensation method for an energy meter with a temperature sensor, characterized in that, The method includes the following steps: S1. Obtain the real-time temperature of the metering chip through the temperature acquisition function of the built-in metering chip in the energy meter. S2. Based on the real-time temperature of the metering chip, and combined with thermodynamic principles, establish a correlation to calculate the operating environment temperature of the electricity meter. S3. Based on the ambient temperature and the pre-stored manganese-copper temperature curve, estimate the real-time temperature of the manganese-copper sampling resistor using the three-node thermal network model of the manganese-copper environment of the metering chip. S4. Call the dual temperature compensation model, input the real-time temperature of the metering chip and the real-time temperature of the manganese copper sampling resistor to calculate the compensation coefficient, use the compensation coefficient to collaboratively correct the real-time temperature deviation of the manganese copper sampling resistor and the real-time temperature deviation of the metering chip, and output the metering result.

2. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, The ambient temperature calculation in S2 is performed according to the following procedure: Multiple sets of control experiments were conducted, covering the normal operating temperature range of the electricity meter, and the ambient temperature and the temperature data of the metering chip under the corresponding conditions were recorded simultaneously. Based on the recorded experimental data, a functional relationship between the temperature of the metering chip and the ambient temperature was constructed through data fitting; Substitute the real-time temperature of the metering chip obtained in step S1 into the function relationship to calculate the operating environment temperature of the electricity meter.

3. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 2, characterized in that, The real-time temperature estimation of the manganese-copper sampling resistor in S3 includes the following steps: Call the pre-stored three-node thermal network model parameters, which include the thermal conductivity and thermal radiation coefficients between the metering chip and manganese copper, manganese copper and the environment, and between the metering chip and the environment; Determine the load range based on the current current load of the electricity meter, and select the corresponding preset thermal time constant; Combining the ambient temperature calculated in step S2, the pre-stored manganese-copper temperature curve, and the selected thermal time constant, the real-time temperature estimation of the manganese-copper sampling resistor is completed by substituting them into the three-node thermal network model.

4. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, The calculation of the compensation coefficient in S4 is performed according to the following steps: Based on the real-time temperature of the metering chip, the preset metering chip temperature offset correction coefficient table is consulted to obtain the correction coefficient on the metering chip side. Based on the real-time temperature of the manganese copper sampling resistor and combined with the pre-stored manganese copper temperature curve, the correction coefficient for the manganese copper side is calculated. The correction coefficients for the metering chip side and the manganese-copper side are weighted and summed according to a preset ratio to obtain the final compensation coefficient.

5. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, In S1, temperature acquisition is completed by the temperature acquisition unit built into the metering chip. The acquisition object is the temperature of the core area of ​​the metering chip. The acquisition interval is dynamically adjusted according to the current load of the electricity meter. When the current load exceeds the set threshold, short interval acquisition is used, and when the current load does not exceed the set threshold, long interval acquisition is used.

6. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, The manganese-copper temperature curve pre-stored in S3 is obtained through special testing. The test covers the extreme temperature range that may occur in the working environment of the electricity meter. The actual resistance value of the manganese-copper sampling resistor is measured at different temperature nodes. The temperature data is correlated with the corresponding resistance value to form a continuous temperature-resistance relationship curve, which is stored in the built-in storage unit of the electricity meter.

7. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, The weight parameters of the dual temperature compensation model in S4 are determined through calibration experiments. The calibration experiments are carried out in batches under different temperature environments and different load conditions. The measurement error data under each experimental scenario are recorded. The weight parameters are obtained by reverse iterative optimization based on the error correction effect and preset in the model.

8. The dual temperature compensation method for a temperature sensor in an energy meter according to claim 1, characterized in that, The original metering data used in the collaborative correction in S4 includes the instantaneous voltage sample value output by the voltage sampling module and the instantaneous current sample value output by the current sampling module. The compensation coefficient is calculated with the instantaneous voltage sample value and the instantaneous current sample value respectively to obtain the corrected voltage data and current data. The power metering calculation is completed based on the corrected data.

9. A dual temperature compensation device for an energy meter with a temperature sensor, applicable to the dual temperature compensation method for an energy meter with a temperature sensor as described in any one of claims 1-8, characterized in that, The device includes an energy meter body, which integrates a metering chip module, an ambient temperature calculation unit, a manganese-copper temperature estimation unit, a dual compensation calculation unit, and a metering correction unit. The metering chip module is electrically connected to the ambient temperature calculation unit, the ambient temperature calculation unit is electrically connected to the manganese-copper temperature estimation unit, the dual compensation calculation unit is electrically connected to the metering chip module and the manganese-copper temperature estimation unit respectively, and the metering correction unit is electrically connected to the dual compensation calculation unit. The metering chip module collects its own real-time temperature, the ambient temperature estimation unit estimates the ambient temperature based on the metering chip temperature, the manganese copper temperature estimation unit outputs the real-time temperature of the manganese copper sampling resistor, the dual compensation calculation unit generates compensation coefficients, and the metering correction unit corrects the original metering data.

10. A dual temperature compensation device for an energy meter with a temperature sensor according to claim 9, characterized in that, The manganese-copper temperature estimation unit includes a built-in storage module. The storage module pre-stores the manganese-copper temperature curve, the thermal conductivity coefficient and thermal radiation coefficient of the three-node thermal network model, and the thermal time constants corresponding to different current load ranges. The thermal conductivity coefficient and thermal radiation coefficient are obtained through multiple sets of temperature gradient experiments. The thermal time constants are divided and stored according to the current load range. After receiving the ambient temperature signal, the manganese-copper temperature estimation unit matches the parameters corresponding to the current load and substitutes them into the model to complete the temperature estimation.

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