A method and system for calibrating, debugging, and monitoring electricity meters based on environmental parameter analysis.

Abstract

This invention belongs to the field of electricity meter calibration and monitoring technology, and relates to an electricity meter calibration, debugging, and monitoring method and system based on environmental parameter analysis. The method includes: constructing a multi-dimensional environmental parameter set using ambient temperature, humidity, and temperature rise offset values ​​of metering sensing components; inputting the parameter set into a reference impedance vector model to determine the parasitic reactance offset of the measurement circuit; calculating the environmentally induced phase deviation angle between voltage and current sampling signals based on the parasitic reactance offset; and correcting the shifted sampling sequence through phase alignment to offset the parasitic phase difference between sampling channels. This invention converts environmental interference into complex impedance drift at the bottom layer of the measurement circuit, achieving in-situ reconstruction of signal phase distortion, eliminating metering deviations caused by physical medium deformation, and improving the accuracy of electricity metering under low power factor load conditions.

Description

Technical Field

[0001] This invention belongs to the field of electricity meter calibration and monitoring technology, and relates to an electricity meter calibration, debugging and monitoring method and system based on environmental parameter analysis. It is used in smart grids for electricity meter metering error compensation under complex operating conditions, and can improve the metering accuracy of electricity meters under inductive low power factor conditions. Background Technology

[0002] Currently, improving the metering accuracy and reliability of smart meters under complex operating conditions by utilizing advanced metering architectures and compensation strategies has become a core approach in smart grid construction. However, existing technologies still have limitations in handling interference from multiple environmental factors, underlying physical drift, and the coordination of system-level compensation. Error fitting techniques based on data-driven and algorithmic modeling, such as the Chinese invention patent CN115561697A which discloses a smart meter error analysis method, utilize neural networks to process the influence of temperature on metering errors under different voltages, phase angles, and currents. However, its shortcomings are: this scheme is essentially a post-hoc state fitting at the software level, and the decision logic lacks the ability to respond instantly to sudden changes in environmental parameters of the underlying hardware. It fails to transform the inherent drift characteristics of physical devices into real-time hardware-level constraints, which may cause the model to output compensation commands with time delays when facing extreme or high-frequency environmental disturbances.

[0003] Local compensation techniques based on single physical hardware modulation, such as the temperature-compensated DC current transformer and method disclosed in Chinese patent CN120809419B, dynamically offset the change in coil internal resistance by connecting an NTC negative temperature resistor system in series with the transformer coil. The drawback is that this technology focuses on single components and single-dimensional open-loop suppression. Although it improves the measurement accuracy of the local temperature dimension, it fails to establish a closed-loop correlation between the overall smart meter metering system and multi-dimensional environmental factors, and cannot cope with the risk of physical compression of the overall metering layer under the cross-coupling of multiple physical fields.

[0004] Based on multi-parameter environmental simulation technology in independent and enclosed spaces, for example, Chinese invention patent application CN115825842A discloses a multi-environmental parameter smart energy meter reliability test simulation device. It constructs independent inner and outer spaces to integrate and output temperature, humidity, salt spray, ultraviolet light, and electromagnetic fields for reliability testing. Its shortcomings are: existing solutions of this kind usually treat the energy meter as a passive test object, and its application scenario is limited to offline evaluation units in the off-grid state, ignoring the inherent anti-interference capability of the device itself in the real-time grid environment; in actual long-term grid connection, if there is a lack of inherent perception and adaptive correction mechanism for complex environmental characteristics at the device end, the prior data of offline testing is prone to mapping drift in actual complex working conditions, making it lose the value of real-time compensation.

[0005] Therefore, how to construct a metrological protection architecture with multi-dimensional environmental feature perception and hardware-software collaborative compensation capabilities, so that the error control logic converges to the true high-precision safety boundary throughout the 24 / 7 operating cycle, is the technical problem to be solved by this invention. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and solve the technical problem that the existing technology cannot isolate the parasitic phase difference induced by the coupling interference of the physical environment, which leads to the limitation of measurement accuracy. The invention provides a method and system for calibrating, debugging and monitoring electricity meters based on environmental parameter analysis.

[0007] To achieve the above-mentioned objectives, this invention provides a method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis. The method is applied to an energy meter including a voltage sampling channel, a current sampling channel, an environmental sensing unit, and a microprocessor, and includes the following steps: Step 101: Obtain temperature, humidity and temperature rise offset values ​​to construct an environmental parameter set: Obtain environmental temperature parameters, environmental humidity parameters and temperature rise offset values ​​of metering sensing components caused by real-time load current that characterize the physical field distribution of the measurement loop, and construct a multi-dimensional environmental parameter set using environmental temperature parameters, environmental humidity parameters and temperature rise offset values. Step 102: Input the parameter set into the model to identify parasitic reactance offset: Input the multidimensional environmental parameter set into the preset reference impedance vector model to quantitatively identify the parasitic reactance offset of the measurement circuit caused by the nonlinear drift of the dielectric constant of the printed circuit board substrate with humidity and the surface geometric deformation of the current transformer under thermal stress. Step 103, determine the environmental induced phase deviation angle using the mapping relationship: use the mapping relationship of parasitic reactance offset on the complex plane of electric vector to determine the environmental induced phase deviation angle between the voltage sampling channel and the current sampling channel as the environment fluctuates; Step 104, Correcting Parasitic Phase Difference in Measurement Circuit by Shifting Sampling Point Index: Determine the phase compensation value of the sampling point based on the environmentally induced phase deviation angle, and perform phase shift correction on the discrete sampling point index of the voltage sampling sequence or current sampling sequence in the digital signal processing stage according to the sampling point phase compensation value, so that the voltage sampling signal and the current sampling signal are physically aligned on the discrete time axis, so as to cancel the nonlinear parasitic phase difference in the measurement circuit and correct the active power measurement deviation.

[0008] In step 101 of the present invention, the temperature rise offset value is determined through the following sub-steps: Step 1011: Obtain the effective value of the real-time load current and the intrinsic resistance of the metering sensing component; Step 1012: Call the thermal gradient time delay model to determine the thermal resistance distribution relationship; Step 1013: Calculate the real-time temperature rise data of the metering sensing component in the current sampling period, and store it as the temperature rise offset value in the multi-dimensional environmental parameter set.

[0009] In step 102 of this invention, the reference impedance vector model is pre-stored in the non-volatile memory of the microprocessor. This model is obtained by calibrating the inherent reactance difference between the voltage sampling channel and the current sampling channel under a reference environment of 20°C and 45% relative humidity.

[0010] In step 104 of this invention, the specific method of phase alignment correction is as follows: the microprocessor determines the time step value corresponding to the environmentally induced phase deviation angle based on the sampling frequency of the energy meter, and performs linear displacement offset on the sampling discrete points of the voltage sampling sequence according to the time step value to cancel the nonlinear phase offset in the measurement circuit.

[0011] Between steps 102 and 103 of the present invention, there is also a frequency domain analysis step for nonlinear loads: Step 1021: Calculate the fast Fourier transform of the voltage sampling sequence and the current sampling sequence to determine the frequency distribution of each harmonic component; Step 1022: Map the parasitic reactance offset to a dynamic impedance distribution spectrum that varies with the harmonic order, and determine the corresponding independent phase deviation angle.

[0012] In step 104 of this invention, phase alignment correction is performed on each harmonic component using the corresponding independent phase deviation angle to eliminate the broadband parasitic inductance anti-amplification effect generated by higher harmonics in the measurement circuit.

[0013] The method described in this invention further includes a sensor state diagnosis step: Step 105: Monitor the rate of change of the environmental induced phase deviation angle; Step 106: When the rate of change exceeds a preset threshold within a sampling window of 10ms to 20ms, and the power grid frequency amplitude is below 0.01Hz, determine that the output data of the environmental sensing unit is abnormal, and suspend the update of the multidimensional environmental parameter set.

[0014] In step 106 of this invention, if the output data of the environmental sensing unit is determined to be abnormal, the microprocessor calls the parasitic reactance offset of the previous valid sampling period for phase compensation to prevent the metering logic from collapsing due to distortion jumps in the environmental sensor.

[0015] The method described in this invention further includes a compensation verification step: Step 107: Calculate the power factor residual after compensation. If the fluctuation range of the compensated active power value is less than 0.2% within the range of 0.5L to 0.8L of the load power factor, then the current environmental compensation logic is determined to be effective, and the calibration parameters of the current impedance vector are maintained.

[0016] This invention also provides a power meter calibration, debugging, and monitoring system based on environmental parameter analysis, including an environmental sensing module, a voltage sampling module, a current sampling module, a storage module, and a logic processing module. An environmental sensing module is used to acquire environmental temperature and humidity parameters. The voltage sampling module, together with the current sampling module, is used to synchronously acquire discrete voltage sequences and discrete current sequences. The storage module is used to store the reference impedance vector model; The logic processing module is connected to the environmental sensing module, voltage sampling module, current sampling module, and storage module, respectively. Specifically, the logic processing module determines the temperature rise offset of the metering sensing components based on the real-time load current and constructs a multi-dimensional environmental parameter set using environmental temperature parameters, environmental humidity parameters, and the temperature rise offset. The logic processing module then inputs the multi-dimensional environmental parameter set into the reference impedance vector model to determine the parasitic reactance offset of the measurement loop. Finally, the logic processing module uses the parasitic reactance offset to determine the environmentally induced phase deviation angle and performs phase alignment correction on the voltage sampling sequence or current sampling sequence based on the environmentally induced phase deviation angle.

[0017] Compared with the prior art, the present invention has the following advantages: 1. In the calibration, debugging, and monitoring of electricity meters, by constructing a physical mapping relationship between the environmental parameter matrix and the reference parasitic reactance model, the interference of environmental fluctuations on the metering accuracy is translated from external error numerical correction into dynamic reconstruction of the physical medium properties of the underlying measurement circuit. By using the linear perturbation value of the channel resistivity determined by the current temperature parameter and the nonlinear perturbation value of the channel parasitic capacitance determined by the current humidity parameter, the actual equivalent complex impedance of the measurement circuit can be accurately solved. Thus, at the level of electrical measurement mechanism, signal distortion caused by physical medium deformation is eliminated, enabling the electricity metering process to have the underlying capability to resist nonlinear coupling interference of multidimensional physical fields.

[0018] 2. The environmental distortion phase angle is calculated by using the ratio of the imaginary part to the real part of the actual equivalent complex impedance. In the digital sampling process, the sampling point index of the voltage discrete sequence is phase-shifted according to the time offset value corresponding to the environmental distortion phase angle. This restores the true physical alignment of the voltage sampling signal and the current sampling signal on the time axis, eliminating the parasitic phase difference that cannot be removed by amplitude compensation or data fitting methods in conventional techniques. This solves the active power measurement deviation caused by small phase angle distortion under low power factor conditions.

[0019] 3. By introducing the Joule heat endogenous parameter generated by the load current into the environmental parameter matrix, a coupled compensation function covering the internal thermal gradient time delay is constructed. The load current is used to characterize the temperature rise state of the core sensing component in real time, and the sensing lag caused by the thermal resistance between the environmental sensor and the core metering component is compensated. This ensures that the microprocessor can obtain the true physical parameters of the measurement circuit during drastic changes in ambient temperature or rapid fluctuations in load, and avoids the over-compensation or under-compensation of phase angle caused by a single environmental sensor. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the phase correction process of the measurement loop driven by multi-dimensional environmental parameters in this invention; Figure 2 This is a schematic diagram of the system architecture for the environmental perception and state diagnosis functions of this invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings.

[0022] Example 1: This example discloses a method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis. The method is applied to an energy meter including a voltage sampling channel, a current sampling channel, an environmental sensing unit, and a microprocessor, and includes the following steps: Step 101: Obtain temperature, humidity and temperature rise offset values ​​to construct an environmental parameter set: Obtain environmental temperature parameters, environmental humidity parameters and temperature rise offset values ​​of metering sensing components caused by real-time load current that characterize the physical field distribution of the measurement loop, and construct a multi-dimensional environmental parameter set using environmental temperature parameters, environmental humidity parameters and temperature rise offset values. Step 102: Input the parameter set into the model to identify parasitic reactance offset: Input the multidimensional environmental parameter set into the preset reference impedance vector model to quantitatively identify the parasitic reactance offset of the measurement circuit caused by the nonlinear drift of the dielectric constant of the printed circuit board substrate with humidity and the surface geometric deformation of the current transformer under thermal stress. Step 103, determine the environmental induced phase deviation angle using the mapping relationship: use the mapping relationship of parasitic reactance offset on the complex plane of electric vector to determine the environmental induced phase deviation angle between the voltage sampling channel and the current sampling channel as the environment fluctuates; Step 104, Correcting Parasitic Phase Difference in Measurement Circuit by Shifting Sampling Point Index: Determine the phase compensation value of the sampling point based on the environmentally induced phase deviation angle, and perform phase shift correction on the discrete sampling point index of the voltage sampling sequence or current sampling sequence in the digital signal processing stage according to the sampling point phase compensation value, so that the voltage sampling signal and the current sampling signal are physically aligned on the discrete time axis, so as to cancel the nonlinear parasitic phase difference in the measurement circuit and correct the active power measurement deviation.

[0023] In step 101 of this embodiment, the temperature rise offset value is determined through the following sub-steps: Step 1011: Obtain the effective value of the real-time load current and the intrinsic resistance of the metering sensor component; Step 1012: Call the thermal gradient time delay model to determine the thermal resistance distribution relationship; Step 1013: Calculate the real-time temperature rise data of the metering sensor component in the current sampling period and store it as the temperature rise offset value in the multi-dimensional environmental parameter set.

[0024] In step 102 of this embodiment, the reference impedance vector model is pre-stored in the non-volatile memory of the microprocessor. This model is obtained by calibrating the inherent reactance difference between the voltage sampling channel and the current sampling channel under a reference environment of 20°C and 45% relative humidity.

[0025] In step 104 of this embodiment, the specific method of phase alignment correction is as follows: the microprocessor determines the time step value corresponding to the environmentally induced phase deviation angle based on the sampling frequency of the energy meter, and performs linear displacement offset on the sampling discrete points of the voltage sampling sequence according to the time step value to cancel the nonlinear phase offset in the measurement circuit.

[0026] Between steps 102 and 103 in this embodiment, a frequency domain analysis step for nonlinear loads is also included: Step 1021: Calculate the fast Fourier transform of the voltage sampling sequence and the current sampling sequence to determine the frequency distribution of each harmonic component; Step 1022: Map the parasitic reactance offset to a dynamic impedance distribution spectrum that varies with the harmonic order, and determine the corresponding independent phase deviation angle.

[0027] In step 104 of this embodiment, phase alignment correction is performed on each harmonic component using the corresponding independent phase deviation angle to eliminate the broadband parasitic inductance anti-amplification effect generated by higher harmonics in the measurement circuit.

[0028] The method described in this embodiment also includes a sensor status diagnosis step: Step 105: Monitor the rate of change of the environmental induced phase deviation angle; Step 106: When the rate of change exceeds a preset threshold within a sampling window of 10ms to 20ms, and the power grid frequency amplitude is lower than 0.01Hz, determine that the output data of the environmental sensing unit is abnormal, and suspend the update of the multi-dimensional environmental parameter set.

[0029] In step 106 of this embodiment, if it is determined that the output data of the environmental sensing unit is abnormal, the microprocessor calls the parasitic reactance offset of the previous valid sampling period for phase compensation to prevent the metering logic from collapsing due to distortion jumps in the environmental sensor.

[0030] The method described in this embodiment also includes a compensation verification step: Step 107: Calculate the power factor residual after compensation. If the fluctuation range of the compensated active power value is less than 0.2% within the range of 0.5L to 0.8L of the load power factor, then the current environmental compensation logic is determined to be effective, and the calibration parameters of the current impedance vector are maintained.

[0031] This embodiment also provides a power meter calibration and debugging monitoring system based on environmental parameter analysis, including an environmental sensing module, a voltage sampling module, a current sampling module, a storage module, and a logic processing module: An environmental sensing module is used to acquire environmental temperature and humidity parameters. The voltage sampling module, together with the current sampling module, is used to synchronously acquire discrete voltage sequences and discrete current sequences. The storage module is used to store the reference impedance vector model; The logic processing module is connected to the environmental sensing module, voltage sampling module, current sampling module, and storage module, respectively. Specifically, the logic processing module determines the temperature rise offset of the metering sensing components based on the real-time load current and constructs a multi-dimensional environmental parameter set using environmental temperature parameters, environmental humidity parameters, and the temperature rise offset. The logic processing module then inputs the multi-dimensional environmental parameter set into the reference impedance vector model to determine the parasitic reactance offset of the measurement loop. Finally, the logic processing module uses the parasitic reactance offset to determine the environmentally induced phase deviation angle and performs phase alignment correction on the voltage sampling sequence or current sampling sequence based on the environmentally induced phase deviation angle.

[0032] Example 2: In this example, the smart grid edge node is deployed in a coastal high-humidity substation where the load current exhibits severe periodic fluctuations. The electricity meter is in continuous operation, and its internal printed circuit board substrate is affected by the ambient humidity parameter. Penetration causes a nonlinear shift in the dielectric constant, while the metering sensing component experiences real-time load current. The generated Joule thermal stress causes geometric deformation of internal details. This change in physical state induces dynamic parasitic reactance in the measurement circuit that fluctuates with the environment. The energy meter obtains the ambient temperature parameter, which characterizes the physical field distribution of the measurement circuit, through the environmental sensing unit. With environmental humidity parameters And invoke the thermal gradient time delay model, based on the real-time load current. Based on the effective value, intrinsic resistance of the metering sensor component, and the relationship between thermal resistance distribution, the real-time temperature rise data of the metering sensor component in the current sampling period is calculated and determined as the temperature rise offset value of the metering sensor component. The microprocessor utilizes ambient temperature parameters Ambient humidity parameters and the temperature rise offset value of the metering sensor component A multidimensional set of environmental parameters is constructed and input into a pre-defined reference impedance vector model to identify the parasitic reactance offset of the measurement loop caused by substrate dielectric constant drift and geometric deformation of internal details. The surface geometric deformation of the aforementioned current transformer under thermal stress mainly affects the measurement circuit by changing the tightness of the magnetic circuit closure, which is determined by the real-time load current. The Joule heating induced causes non-uniform thermal expansion between the transformer core and the winding frame. The resulting mechanical stress directly acts on the magnetostrictive effect region of the ferromagnetic material, causing a slight deviation in the equivalent permeability of the core. This change in magnetic reluctance at the surface level, compared to a simple overall geometric displacement, generates mutual inductance vector error in electromagnetic field coupling, which in turn induces a parasitic reactance offset in the measurement circuit sufficient to cause phase angle distortion. The reference impedance vector model is stored in the microprocessor's non-volatile memory. This model is obtained by calibrating the inherent reactance difference between the voltage sampling channel and the current sampling channel under a reference environment of 20°C and 45% relative humidity.

[0033] The microprocessor utilizes the parasitic reactance offset of the measurement loop. The mapping relationship on the complex plane of electric vectors is used to determine the environmentally induced phase deviation angle between the voltage sampling channel and the current sampling channel, which fluctuates with environmental changes. Based on this, the microprocessor adjusts the phase deviation angle induced by the environment. The phase compensation value of the sampling point is determined, and the discrete sampling point index of the voltage sampling sequence is shifted and corrected according to the phase compensation value during the digital signal processing stage. This restores the physical alignment of the voltage sampling signal and the current sampling signal on the discrete time axis, thereby offsetting the nonlinear parasitic phase difference in the measurement circuit. This impedance vector deduction and phase in-situ reconstruction method based on environmental parameter analysis transforms the overall structural physical field distortion into the complex impedance drift at the bottom layer of the measurement circuit. The digital domain reset of the signal phase distortion is completed without changing the hardware sampling frequency, so that the fluctuation range of the compensated active power value is less than 0.2% in the range of load power factor from 0.5L to 0.8L.

[0034] Example 3: This example describes a test scenario deployed on a high-precision power verification platform including an environmental test chamber. The test platform uses a standard meter with an accuracy class of 0.02 as a reference. The environmental test chamber provides controlled temperature and humidity fields, with a temperature control range covering -40℃ to 85℃ and a temperature control resolution of 0.1℃. The relative humidity control range covers 10%RH to 98%RH, with a temperature control accuracy of 1%RH. Key parameters of the test design include sampling frequency. Update cycle of the multidimensional environmental parameter set The sampling frequency The settings need to balance the phase compensation resolution with the microprocessor's digital signal processing load. Since the phase compensation value of the sampling point is shifted and corrected on the discrete point index of the sampling sequence, the compensation resolution depends on the number of sampling points in a single period. Increasing the sampling frequency... This can improve the precision of phase compensation, while simultaneously increasing the real-time performance pressure on the microprocessor for Fast Fourier Transform operations. The sampling frequency selected in the experiment... The frequency is set to 12.8kHz, ensuring 256 discrete sampling points per cycle at a 50Hz power frequency. The corresponding minimum phase compensation step is 1.406°, and the update cycle is... The settings depend on the relationship between the thermal equilibrium response time of the measurement loop and the frequency of environmental parameter fluctuations, in order to capture the changes caused by the real-time load current. The resulting transient temperature rise, update cycle Set to 1 second.

[0035] Set ambient temperature parameters The temperature was 55.2℃, and the ambient humidity was [not specified]. 92.5%RH, real-time load current Under operating conditions of 10.15A and a load power factor of 0.5L, the control group experienced a nonlinear parasitic phase difference in the measurement circuit due to the dielectric constant shift caused by moisture absorption of the printed circuit board substrate and thermal stress deformation of the current transformer. The measured measurement error was -0.852%. The present invention's sample group acquired operating condition data through an environmental sensing unit, and the temperature rise offset of the metering sensing component was calculated using a thermal gradient time delay model. The temperature is 12.3℃; the microprocessor inputs a dataset containing temperature, humidity, and temperature rise into the reference impedance vector model to identify the parasitic reactance offset of the measurement circuit. It is 0.458 And determine the environmentally induced phase deviation angle on the complex plane of electric vectors. The value is 0.352°; the microprocessor induces a phase deviation angle based on the environment. The phase compensation value of the sampling point is determined to be a displacement of 1 sampling period. After shifting the voltage sampling sequence index during the digital signal processing stage and completing the phase alignment correction, the measured measurement error of the sample group under the same working conditions is -0.118%.

[0036] When maintaining the same high temperature and high humidity environment, if only the ambient temperature parameter is used... And remove the ambient humidity parameter The corresponding compensation logic has a measured measurement error of -0.564%, confirming the combined effect of multidimensional environmental parameters on impedance vector derivation under complex operating conditions, particularly regarding environmental humidity parameters. During the process of increasing the RH from 45.0% to 75.0% and then to 92.5% RH, the parasitic reactance offset of the measurement circuit was measured. It exhibits a non-linear growth trend, with values ​​of 0.082. 0.215 and 0.458 The corresponding phase deviation correction amount evolves dynamically, keeping the measurement error within the accuracy limit of 0.2%. This is achieved when the ambient humidity parameter is set. When the RH is 99.0% and condensation occurs, the surface impedance of the substrate undergoes a sudden change, causing the output of the reference impedance vector model to exceed the convergence range. The measurement error deteriorates nonlinearly to over -1.25%. Based on the phase in-situ reconstruction method of environmental parameter analysis, the overall structural physical field distortion is transformed into the complex impedance drift of the bottom layer of the measurement circuit. The digital domain reset of the signal phase distortion is completed without changing the hardware sampling frequency, so that the operating state of the energy meter under the inductive low power factor condition is within the design accuracy envelope.

[0037] Example 4: In this example, in the terminal operation of a smart distribution network in an industrial park containing a high-power variable frequency speed control system and high-order harmonic current injection, the energy meter is subjected to transient current surges that fluctuate drastically with the production cycle. This current change causes a non-uniform thermal field distribution in the current transformer core within the measurement circuit, accompanied by electromagnetic distortion caused by the nonlinear load. This transient change in physical state causes microsecond-level asynchrony in the transmission characteristics of the voltage sampling channel and the current sampling channel, resulting in harmonic active power metering deviation. The microprocessor obtains the real-time load current of the current sampling window through the environmental sensing unit. The microprocessor performs recursive calculations, treating the metering sensing components as physical entities with specific thermal diffusion characteristics. Based on Joule's law of heating and Newton's law of cooling, a first-order thermodynamic differential model is constructed. The physical process of dielectric parameter drift caused by moisture diffusion in the printed circuit board substrate is represented by a minute-level time constant, while the physical process of thermal stress distortion in the magnetic core induced by a load current step is represented by a millisecond-level time constant. The microprocessor triggers a multi-scale physical variable isolation procedure. A low-frequency sliding sampling window with a time span of ten seconds is set to extract smoothly fluctuating environmental humidity parameters, while a high-frequency observation window with a time span of twenty milliseconds is simultaneously set to extract transient real-time load current. An objective time window is used to isolate frequency domain crosstalk between slowly changing dielectric parameters and rapidly changing electromagnetic parameters during low-level data fusion. Under independent clock domain constraints, the microprocessor determines the physical temperature rise by calculating the weighted increment of the current heat increment and the thermal equilibrium state of the previous moment. The heat increment and the real-time load current... The square of the equation satisfies a proportional relationship. The weighting operator is determined based on the product of the intrinsic thermal resistance and thermal capacity of the metering sensor component, thereby obtaining the temperature rise offset value of the metering sensor component that characterizes the true deformation trend of the physical medium. The microprocessor acquires ambient humidity parameters. The study utilizes a pre-defined reference impedance vector model to identify physical medium polarization caused by humidity penetration. This physical medium polarization's impact on the loop phase primarily occurs at the high-impedance voltage divider nodes of the voltage sampling network. Although the absolute value of the distributed capacitance generated by the printed circuit board substrate is small, in high-humidity environments, water molecules adsorbed on the substrate surface increase the equivalent dielectric constant of the high-impedance nodes in the sampling channel, causing a picofarad shift in the parasitic capacitance at that location. Since the voltage sampling channel is a typical high-input-impedance RC filter structure, this small fluctuation in distributed capacitance causes a micro-jump in the filter's pole frequency, leading to a drift in the phase frequency characteristics of the sampling signal near a specific cutoff frequency. Ultimately, this manifests as an observable environmentally induced phase deviation in power frequency or harmonic sampling, where the environmental humidity parameter... The parasitic reactance offset of the measurement circuit The contribution is through the humidity sensitivity coefficient This coefficient is determined to characterize the capacitive reactance drift of the equivalent distributed capacitance of the measurement loop caused by a unit change in humidity. The microprocessor combines this with the ambient temperature parameter. Temperature rise offset value of metering sensor component Complete the nonlinear solution of the multidimensional environmental parameter set and output the parasitic reactance offset of the measurement loop. ; .

[0038] The derivation operation follows the fundamental principle of orthogonal decomposition of complex impedance in AC networks. The microprocessor retrieves the pre-calibrated intrinsic equivalent resistance parameters of the measurement circuit from the storage module, distinguishes the differences in the polarity of the interference of the physical fields of the multiphase medium, establishes the polarization effect caused by the environmental humidity parameter as the negative polarity capacitive reactance characteristic vector, and establishes the stress distortion caused by the temperature rise offset of the metering sensing component as the positive polarity capacitive reactance characteristic vector. When constructing the above vectors, the humidity sensitivity coefficient Kh is obtained through a pre-calibration process, that is, the slope of the imaginary part drift of the complex impedance of the sampling channel caused by a unit humidity change is measured in the controlled environment chamber. The specific vector synthesis rule is as follows: in the complex plane coordinate system, the microprocessor sets the inductive reactance drift amount dominated by thermal effects as the positive component of the imaginary axis, and sets the slope of the inductive reactance drift amount dominated by the humidity polarization effect as the positive component of the imaginary axis. The dominant capacitive reactance drift is set as the negative component of the imaginary axis. After algebraic superposition of the two components along the same imaginary axis, the resulting vector net value represents the total reactance offset characteristic caused by multi-physics coupling. This serves as the deterministic input for subsequent calculation of the arctangent phase deviation angle. The microprocessor performs algebraic summation on the bipolar characteristic vector along the complex plane imaginary axis, outputting the total equivalent imaginary part containing multivariable orthogonal decoupling characteristics as the parasitic reactance offset of the measurement loop. The microprocessor calls the arctangent mathematical function to calculate the ratio of the parasitic reactance offset of the measurement loop to the intrinsic equivalent resistance parameter. Based on the calculated arctangent function value, it outputs the true phase angle offset reference in the physical domain. Through a closed quantization chain, the microprocessor utilizes the parasitic reactance offset of the measurement loop... The corresponding environmentally induced phase deviation angle is calculated on the complex plane of the electric vector. And based on the environmentally induced phase deviation angle With sampling step angle The ratio determines the phase compensation value of the sampling point. For the phase compensation value of the sampling point In the case of non-integer values, the microprocessor rounds the calculation result to perform translation correction on the discrete sampling point index of the voltage sampling sequence. At the same time, the phase rounding residual generated during the rounding process is temporarily stored in a register, and in the next calculation cycle, the phase rounding residual is accumulated into the new sampling point phase compensation value. This achieves sub-step physical alignment of the sampling sequence on the time axis, thereby offsetting the nonlinear parasitic phase difference induced by physical field distortion. This adjustment method, which combines thermal inertial recursive simulation and sampling remainder accumulation compensation, translates transient physical field distortion into the dynamic impedance characteristics of the measurement loop. Under the condition that the total harmonic distortion rate is 15% and the ambient temperature fluctuation exceeds 20°C, the system completes the in-situ phase reconstruction of the broadband electrical signal, making the absolute value of the harmonic active power measurement error less than 0.15%, and eliminating the quantization truncation effect caused by discrete domain index translation. This ensures that the metering data of the energy meter is within the stability envelope under dynamic thermodynamic environment.

[0039] In industrial power distribution monitoring scenarios with a large number of inductive and nonlinear rectified loads, the microprocessor acquires voltage and current sampling sequences, converts these sequences into frequency domain data using a Fast Fourier Transform algorithm, identifies the amplitude and phase characteristics of the fundamental and harmonic components, and determines the corresponding frequency domain data based on a preset dynamic impedance distribution spectrum. Parasitic reactance offset of the measurement circuit for subharmonic components Because the dielectric response of printed circuit board substrates exhibited under specific frequency excitation is frequency-dependent, the system incorporates ambient temperature parameters. Ambient humidity parameters and the temperature rise offset value of the metering sensor component The operator for solving the dynamic impedance distribution spectrum is injected to determine the first... Independent phase deviation angle of subharmonic components under the current physical field distribution The microprocessor performs independent phase deviation angle calculations for each harmonic component in the voltage sampling sequence. The corresponding sampling point index shift, in the specific digital signal processing flow, the above-mentioned independent phase correction for each harmonic is achieved through frequency domain phasor shifting technology. The microprocessor uses Fast Fourier Transform to decompose the wideband sampling sequence into a complex frequency domain array containing the fundamental wave and each harmonic, extracts the reference phase angle corresponding to each harmonic frequency point, and for the nth harmonic, the microprocessor adjusts its corresponding complex coefficient phase according to the independent phase deviation angle θn to complete the phase angle compensation in the frequency domain. After completing the full spectrum correction, the microprocessor executes inverse Fast Fourier Transform to reconstruct the calibrated frequency domain data back to the time domain, thereby achieving the differentiated physical alignment of each harmonic on the discrete time axis in an equivalent effect, reconstructing a full-wave sampling sequence that eliminates phase distortion, and realizing phase alignment in the frequency dimension during harmonic active power metering; whereby For harmonic orders, For ambient temperature parameters, For environmental humidity parameters, This is the temperature rise offset value of the metering sensor component. For the first The independent phase deviation angle of each harmonic, this dynamic impedance reconstruction mechanism based on frequency scanning, extends the physical response characteristics of the measurement circuit from a single power frequency point to the full spectrum bandwidth. By independently correcting the phase of each harmonic component, the system cancels the asynchronous deviation caused by the dielectric constant dispersion effect on high-frequency signal acquisition, so that in power system nodes where the proportion of nonlinear load exceeds 30%, the active power measurement results of each harmonic are within the design error limit.

[0040] Example 5: In this example, in an environment simulating a standard reference environment through a constant temperature and humidity control system, the system determines the parameter reference for the reference impedance vector model of the energy meter. The calibration process is carried out under ambient temperature parameters. Parameters for 20℃ and ambient humidity The microprocessor operates under a 45%RH baseline condition. It synchronously acquires the power frequency signal output from the standard source via the voltage and current sampling channels, calculates the phase deviation between the voltage and current sampling sequences at this point, and determines this phase deviation as the intrinsic phase reference for the measurement circuit. The system adjusts the ambient humidity parameter. The parasitic reactance of the measurement circuit was recorded by varying the value in increments of 5%RH within the range of 10%RH to 98%RH. The microprocessor determines the numerical relationship between ambient humidity and distributed capacitance reactance based on measured data under various humidity gradients, and stores it in non-volatile memory as the data source for the reference impedance vector model, providing a benchmark for impedance derivation under real-time operating conditions; among which Ambient temperature parameter For environmental humidity parameters, As the intrinsic phase reference, To measure the offset of the parasitic reactance of the circuit.

[0041] When the system faces characteristic drift caused by differences in printed circuit board material properties, the humidity sensitivity coefficient of the energy meter should be determined before deployment. The microprocessor uses the measured impedance values ​​of the current batch of substrates in dry and saturated moisture-absorbing states to calculate the rate of change of parasitic reactance of the measurement circuit caused by a unit change in humidity, converting the physical dielectric polarization effect into a humidity sensitivity coefficient. The microprocessor will acquire the ambient humidity parameters Humidity sensitivity coefficient The product of these factors is determined as the humidity component, which is then compared with the temperature rise offset of the metering sensor component determined based on the thermal gradient time delay model. The corresponding thermal components are superimposed to determine the parasitic reactance offset of the measurement circuit. The microprocessor utilizes the parasitic reactance offset of the measurement loop. Determine the environmental induced phase deviation angle Based on this, the discrete sampling point index of the voltage sampling sequence is shifted, so that the energy meter can eliminate the phase deviation caused by the physical field in the measurement circuit with different material properties.

[0042] Example 6: In this example, the system determines the parameter reference for the reference impedance vector model of the energy meter in an environment simulated by a constant temperature and humidity control system. The calibration process is carried out under ambient temperature parameters. Parameters for 20℃ and ambient humidity To start up under a 45%RH baseline condition, the microprocessor records the initial impedance value of the metering sensor component under no-load conditions and adjusts the real-time load current. The current was increased from 0A to 60A in 10A increments, and maintained at each current step for 30 minutes until thermal equilibrium was reached. The temperature rise data of the metering sensor component was then recorded. The thermal response coefficient was determined by fitting the numerical relationship between the square of the current and the temperature rise using the least squares method. Then the microprocessor monitors the real-time load current. Adjusting environmental humidity parameters under constant conditions The humidity sensitivity coefficient was determined by increasing the RH from 10% to 95% in 5% RH increments and recording the reactance vector changes between the voltage and current sampling channels. .

[0043] When the system encounters characteristic drift conditions caused by differences in the dielectric properties of printed circuit board materials from different batches, the energy meter determines the reference offset of a specific batch through calibration procedures before deployment. The microprocessor controls the environmental sensing unit to obtain the ambient temperature parameter at the current deployment point. With environmental humidity parameters The theoretical reactance value under the corresponding environment is determined by retrieving the preset reference impedance vector model. Simultaneously acquire the measured value of the total reactance of the real-time measurement loop under standard source verification. The microprocessor calculates the theoretical reactance value. Measured value of total reactance The deviation is used as an initial deployment calibration item. The parasitic reactance offset of the measurement circuit is stored in non-volatile memory, and the microprocessor calculates it in real time during operation. Overlay initial deployment calibration items Correction to determine the environmentally induced phase deviation angle After completing online alignment for physical differences in specific hardware batches, the discrete sampling point index of the voltage sampling sequence is determined based on the environmentally induced phase deviation angle. The translation is implemented to keep the system in a stable metering state within the range of load power factor fluctuations.

Claims

1. A method for calibrating, debugging, and monitoring electricity meters based on environmental parameter analysis, characterized in that, The method is applied to energy meters that include voltage sampling channels, current sampling channels, environmental sensing units, and microprocessors, and includes the following steps: Step 101: Obtain the ambient temperature parameter, ambient humidity parameter, and temperature rise offset value of the metering sensing component caused by the real-time load current, which characterize the physical field distribution of the measurement loop; and construct a multi-dimensional set of environmental parameters using the ambient temperature parameter, ambient humidity parameter, and temperature rise offset value. Step 102: Input the multidimensional environmental parameter set into the preset reference impedance vector model to quantitatively identify the parasitic reactance offset of the measurement circuit caused by the nonlinear drift of the dielectric constant of the printed circuit board substrate with humidity and the surface geometric deformation of the current transformer under thermal stress. Step 103: Using the mapping relationship of parasitic reactance offset on the electric vector complex plane, determine the environmentally induced phase deviation angle between the voltage sampling channel and the current sampling channel as the environment fluctuates; Step 104: Determine the phase compensation value of the sampling point based on the environmental induced phase deviation angle, and perform phase shift correction on the discrete sampling point index of the voltage sampling sequence or current sampling sequence in the digital signal processing stage according to the sampling point phase compensation value, so that the voltage sampling signal and the current sampling signal are physically aligned on the discrete time axis, so as to cancel the nonlinear parasitic phase difference in the measurement circuit and correct the active power measurement deviation.

2. The method for calibrating, debugging, and monitoring electricity meters based on environmental parameter analysis according to claim 1, characterized in that, In step 101, the temperature rise offset value is determined through the following sub-steps: Step 1011: Obtain the effective value of the real-time load current and the intrinsic resistance of the metering sensor component; Step 1012: Call the thermal gradient time delay model to determine the thermal resistance distribution relationship; Step 1013: Calculate the real-time temperature rise data of the metering sensor component in the current sampling period and store it as the temperature rise offset value in the multi-dimensional environmental parameter set.

3. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 1, characterized in that, In step 102, the reference impedance vector model is pre-stored in the microprocessor's non-volatile memory. This model is obtained by calibrating the inherent reactance difference between the voltage sampling channel and the current sampling channel under a reference environment of 20°C and 45% relative humidity.

4. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 1, characterized in that, In step 104, the specific method of phase alignment correction is as follows: the microprocessor determines the time step value corresponding to the environmentally induced phase deviation angle based on the sampling frequency of the energy meter, and performs linear displacement offset on the sampling discrete points of the voltage sampling sequence according to the time step value to cancel the nonlinear phase offset in the measurement circuit.

5. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 1, characterized in that, Between steps 102 and 103, there is also a frequency domain analysis step for nonlinear loads: Step 1021: Calculate the fast Fourier transform of the voltage sampling sequence and the current sampling sequence to determine the frequency distribution of each harmonic component; Step 1022: Map the parasitic reactance offset to a dynamic impedance distribution spectrum that varies with the harmonic order, and determine the corresponding independent phase deviation angle.

6. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 5, characterized in that, In step 104, phase alignment correction is performed for each harmonic component using the corresponding independent phase deviation angle to eliminate the broadband parasitic inductance anti-amplification effect generated by higher harmonics in the measurement circuit.

7. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 1, characterized in that, It also includes sensor status diagnosis steps: Step 105: Monitor the rate of change of the environmental induced phase deviation angle; Step 106: When the rate of change exceeds the preset threshold within the sampling window of 10ms to 20ms, and the power grid frequency amplitude is lower than 0.01Hz, it is determined that the output data of the environmental sensing unit is abnormal, and the update of the multi-dimensional environmental parameter set is suspended.

8. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 7, characterized in that, In step 106, if the output data of the environmental sensing unit is determined to be abnormal, the microprocessor calls the parasitic reactance offset of the previous valid sampling period for phase compensation to prevent the metering logic from collapsing due to distortion jumps in the environmental sensor.

9. The method for calibrating, debugging, and monitoring an energy meter based on environmental parameter analysis according to claim 1, characterized in that, It also includes a compensation verification step: Step 107: Calculate the power factor residual after compensation. If the fluctuation range of the active power value after compensation is less than 0.2% in the range of load power factor from 0.5L to 0.8L, then the current environmental compensation logic is determined to be effective, and the calibration parameters of the current impedance vector are maintained.

10. A power meter calibration, debugging, and monitoring system based on environmental parameter analysis, used to implement the power meter calibration, debugging, and monitoring method based on environmental parameter analysis as described in claim 1, characterized in that, It includes an environmental sensing module, a voltage sampling module, a current sampling module, a storage module, and a logic processing module: An environmental sensing module is used to acquire environmental temperature and humidity parameters. The voltage sampling module, together with the current sampling module, is used to synchronously acquire discrete voltage sequences and discrete current sequences. The storage module is used to store the reference impedance vector model; The logic processing module is connected to the environmental sensing module, voltage sampling module, current sampling module, and storage module, respectively. Specifically, the logic processing module determines the temperature rise offset of the metering sensing components based on the real-time load current and constructs a multi-dimensional environmental parameter set using environmental temperature parameters, environmental humidity parameters, and the temperature rise offset. The logic processing module then inputs the multi-dimensional environmental parameter set into the reference impedance vector model to determine the parasitic reactance offset of the measurement loop. Finally, the logic processing module uses the parasitic reactance offset to determine the environmentally induced phase deviation angle and performs phase alignment correction on the voltage sampling sequence or current sampling sequence based on the environmentally induced phase deviation angle.

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