An online self-adaptive calibration method and system for low power factor error of an electric energy meter

Abstract

The application provides an online self-adaptive calibration method and system for low power factor error of an electric energy meter, comprising the following steps: measuring initial phase error at multiple preset low power factor calibration points to establish a data set of power factor and error; constructing an initial error function based on the data set; calculating a second derivative sign change point of the function as a boundary point to divide multiple calibration subintervals; adopting a weighted sum model of Legendre polynomial and hyperbolic tangent function to fit a phase error compensation formula in each subinterval; calculating a compensation value according to a real-time power factor position and a corresponding subinterval compensation formula during actual operation of the electric energy meter; and if the power factor is near the boundary point, then smoothing and fusing adjacent interval results through a Gaussian weighting function to realize online correction of the phase error.

Description

Technical Field

[0001] This application belongs to the field of calibration, and in particular relates to an online adaptive calibration method and system for low power factor error of electricity meters. Background Technology

[0002] In modern power systems, the increase in harmonic components in the power grid leads to a generally low power factor. Under low power factor conditions, the inherent phase shift of components such as the current transformer and voltage sampling circuit inside the electricity meter, superimposed with the phase angle inherent in the voltage and current signals, produces phase errors. In the region where the power factor is close to zero, the error changes more drastically, becoming a major factor affecting the metering accuracy. Therefore, how to accurately calibrate electricity meter errors under low power factor conditions is a pressing technical challenge in the field of electricity metering. Existing electricity meter error calibration techniques employ single-point or finite-point calibration methods, measuring and compensating at a few fixed low power factor points, and estimating the error over the entire low power factor range through linear interpolation or piecewise functions. Due to the highly nonlinear relationship between phase error and power factor, this simplified approach struggles to accurately detect the complex details of error changes, resulting in poor compensation at non-calibration points and limited accuracy. Other methods use a single high-order polynomial to globally fit the entire error curve. While this achieves high consistency at calibration points, it is prone to Runge's phenomenon, characterized by violent oscillations between data points, leading to model instability and ultimately resulting in larger calibration errors. Piecewise calibration methods may exhibit discontinuities or unevenness at the junctions of different intervals, causing compensation values ​​to jump near boundary points, affecting the stability and reliability of the measurement. Therefore, developing a calibration method that can accurately model nonlinear error characteristics and ensure smooth compensation transitions has significant practical implications and application value. Summary of the Invention

[0003] To address the problem that existing technologies cannot accurately model nonlinear error characteristics and cannot guarantee a smooth transition in compensation.

[0004] In the first aspect, the present invention proposes an online adaptive calibration method for low power factor error in electricity meters, comprising the following steps: At multiple preset low power factor calibration points, voltage and current signals are applied to the energy meter, and the initial phase error corresponding to each calibration point is measured and recorded to form a dataset containing the relationship between power factor and initial phase error. Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor value at which the sign of the second derivative of the function changes is determined as the boundary point, thereby dividing the low power factor range into multiple calibration sub-intervals. For each calibration sub-interval, a phase error compensation formula for the sub-interval is fitted using the data within the interval in the dataset; During the real-time operation of the electricity meter, the real-time power factor is acquired, and the phase error compensation value is calculated based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The calculated phase error compensation value is used to correct the energy meter's energy measurement results.

[0005] Optionally, applying voltage and current signals to the energy meter at multiple preset low power factor calibration points includes: The voltage signal is set to the rated voltage and rated frequency. Within the preset range of inductive and capacitive power factors, multiple calibration points are set at preset step intervals, and a corresponding current signal is applied to each calibration point.

[0006] Optionally, the step of establishing a function of the initial phase error with respect to the power factor based on the dataset, calculating the second derivative of the function, and determining the power factor value at which the sign of the second derivative of the function changes as the boundary point includes: A polynomial function is used to globally fit the dataset. Calculate the second derivative of the function; Solve the equations whose second derivative is zero, and select real solutions with changes in the sign of the second derivative as boundary points.

[0007] Optionally, the phase error compensation formula is in the following specific form: Where PF is the power factor, and x is the variable obtained by linearly normalizing PF according to the boundary of the calibration sub-interval. This represents the phase error compensation value within a single calibration sub-interval. For a k-th order Legendre polynomial, , , , These are the undetermined coefficients determined by iterative optimization algorithms fitting the data points within each calibration sub-interval.

[0008] Optionally, if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula for the sub-interval, including: For a calibration sub-interval consisting of adjacent boundary points, a transition region with a preset width is set at both ends of the calibration sub-interval; When the real-time power factor is located in a sub-interval other than the transition region, the real-time power factor is directly substituted into the phase error compensation formula of the sub-interval for calculation.

[0009] Optionally, if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, a Gaussian weighting function is used to fuse the calculation results of the phase error compensation formula for the two adjacent calibration sub-intervals to obtain a smooth phase error compensation value, including: Let two adjacent calibration sub-intervals be interval i and interval i+1, and the boundary point be... ,by Define the width as the center. transition area , ; When the real-time power factor PF is in the transition region, the compensation value is calculated using the compensation formulas for interval i and interval i+1, respectively. The compensation values ​​are weighted and fused using a weight based on a Gaussian error function to obtain smooth phase error compensation values.

[0010] Optionally, the step of correcting the electricity meter's metering results using the calculated phase error compensation value includes: Acquire digital sampling sequences of voltage and current; Convert the phase error compensation value into a sampling point offset: ; in, Sampling frequency, The fundamental frequency of the power grid; the unit of phase error compensation value is radians. Based on the sampling point offset, the corrected current sampling sequence is obtained by performing digital shift or fractional delay interpolation on the current sampling sequence. The corrected current sampling sequence is multiplied and summed with the voltage sampling sequence to calculate the corrected energy value.

[0011] In another aspect, the present invention also proposes an online adaptive calibration system for low power factor error in electricity meters, comprising the following modules: A module is established to apply voltage and current signals to an energy meter at multiple preset low power factor calibration points, measure and record the initial phase error corresponding to each calibration point, and form a dataset containing the relationship between power factor and initial phase error. Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor values ​​where the sign of the second derivative of the function changes are determined as boundary points, thereby dividing the low power factor range into multiple calibration sub-intervals. A construction module is used to fit a phase error compensation formula for each calibration sub-interval using data within the interval in the dataset. The calculation module is used to acquire the real-time power factor during the real-time operation of the electricity meter, and calculate the phase error compensation value based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The calibration module is used to calibrate the energy meter's energy measurement results using the calculated phase error compensation value.

[0012] Preferably, applying voltage and current signals to the energy meter at multiple preset low power factor calibration points includes: The voltage signal is set to the rated voltage and rated frequency. Within the preset range of inductive and capacitive power factors, multiple calibration points are set at preset step intervals, and a corresponding current signal is applied to each calibration point.

[0013] Preferably, the step of establishing a function of the initial phase error with respect to the power factor based on the dataset, calculating the second derivative of the function, and determining the power factor value at which the sign of the second derivative of the function changes as the boundary point includes: A polynomial function is used to globally fit the dataset. Calculate the second derivative of the function; Solve the equations whose second derivative is zero, and select real solutions with changes in the sign of the second derivative as boundary points.

[0014] Preferably, the phase error compensation formula is in the following form: Where PF is the power factor, and x is the variable obtained by linearly normalizing PF according to the boundary of the calibration sub-interval. This represents the phase error compensation value within a single calibration sub-interval. For a k-th order Legendre polynomial, , , , These are the undetermined coefficients determined by iterative optimization algorithms fitting the data points within each calibration sub-interval.

[0015] Preferably, if the real-time power factor is located within a single calibration sub-interval, then the phase error compensation value is calculated using the phase error compensation formula for the sub-interval, including: For a calibration sub-interval consisting of adjacent boundary points, a transition region with a preset width is set at both ends of the calibration sub-interval; When the real-time power factor is located in a sub-interval other than the transition region, the real-time power factor is directly substituted into the phase error compensation formula of the sub-interval for calculation.

[0016] Preferably, if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, a Gaussian weighted function is used to fuse the calculation results of the phase error compensation formula for the two adjacent calibration sub-intervals to obtain a smooth phase error compensation value, including: Let two adjacent calibration sub-intervals be interval i and interval i+1, and the boundary point be... ,by Define the width as the center. transition area , ; When the real-time power factor PF is in the transition region, the compensation value is calculated using the compensation formulas for interval i and interval i+1, respectively. The compensation values ​​are weighted and fused using a weight based on a Gaussian error function to obtain smooth phase error compensation values.

[0017] Preferably, the step of correcting the electricity meter's metering results using the calculated phase error compensation value includes: Acquire digital sampling sequences of voltage and current; Convert the phase error compensation value into a sampling point offset: ; in, Sampling frequency, The fundamental frequency of the power grid; the unit of phase error compensation value is radians. Based on the sampling point offset, the corrected current sampling sequence is obtained by performing digital shift or fractional delay interpolation on the current sampling sequence. The corrected current sampling sequence is multiplied and summed with the voltage sampling sequence to calculate the corrected energy value.

[0018] This invention provides a calibration method for low power factor error in electricity meters. By using the second derivative of the initial phase error with respect to the power factor function to determine boundary points, the accurate division of the low power factor range is achieved. For each calibration sub-interval, a phase error compensation formula is constructed using a weighted sum of Legendre polynomials and hyperbolic tangent functions, which can fit complex error variation patterns and improve the accuracy of the compensation model. Near the boundary points, the compensation results of adjacent sub-intervals are smoothly fused using a Gaussian weighted function, avoiding abrupt changes in compensation values ​​caused by interval switching, ensuring the continuity and stability of the entire calibration process, and improving the metering accuracy of the electricity meter throughout the entire low power factor operating range. Attached Figure Description

[0019] Figure 1 A flowchart of the first embodiment; Figure 2 This is a schematic diagram of error curve fitting and calibration sub-interval division; Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the first embodiment, the present invention proposes an online adaptive calibration method for low power factor error in electricity meters, such as... Figure 1 This includes the following steps: S1, At multiple preset low power factor calibration points, voltage and current signals are applied to the energy meter, and the initial phase error corresponding to each calibration point is measured and recorded to form a dataset containing the relationship between the power factor and the initial phase error; Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor value at which the sign of the second derivative of the function changes is determined as the boundary point, thereby dividing the low power factor range into multiple calibration sub-intervals; Low power factor typically refers to an AC circuit where the phase difference between voltage and current is close to 90 degrees, such as PF < 0.5 or lower. According to the error formula... In the low PF region , As the phase approaches infinity, tiny phase errors can be amplified into enormous energy metering errors. Traditional single-point calibration cannot correct for this. To mitigate the nonlinear metering deviations that arise from amplification, this invention utilizes the relationship between low power factor and phase error to reduce these errors. Furthermore, this invention is particularly suitable for high-precision gate meters or inverters in grid-connected renewable energy systems. Specifically, a high-precision power factor standard source is used, and representative calibration points are selected within the low power factor range requiring focused calibration. For example, calibration points are set in 0.05 increments within the inductive range of 0.05L to 0.5L and the capacitive range of 0.05C to 0.5C. At each calibration point, a rated voltage (e.g., 220 volts) and 10% of the rated current are applied to the calibrated energy meter as a test current. The phase angle between the voltage and current is accurately controlled by the standard source to generate the corresponding power factor. By comparing the standard energy value with the energy meter's measured value, the standard source calculates the overall error of the energy meter and uses the approximate relationship between the total error and the phase error to separate and record the initial phase error component. Each power factor calibration point and its corresponding initial phase error are treated as a set of data. For example, a power factor of 0.25L corresponds to an initial phase error of -0.08 degrees. All the data are combined into a list or table to form a dataset.

[0022] Using cubic spline interpolation, the discrete power factor and initial phase error data points in the dataset are connected into a smooth, continuous curve function, such as... Figure 2 The second derivative of the spline function is obtained in analytical form. Starting from one end of the low power factor, for example, 0.1L, the second derivative is calculated for each power factor value by traversing the entire power factor range in a very small step. The points where the second derivative changes from positive to negative or vice versa are recorded as boundary points. For example, if the calculation shows that the second derivative is positive at a power factor of 0.26L and negative at 0.27L, a boundary point is determined between 0.26L and 0.27L. All found boundary points divide the entire low power factor range into several consecutive calibration sub-intervals.

[0023] In an optional embodiment, applying voltage and current signals to the energy meter at multiple preset low power factor calibration points includes: The voltage signal is set to the rated voltage and rated frequency. Within the preset range of inductive and capacitive power factors, multiple calibration points are set at preset step intervals, and a corresponding current signal is applied to each calibration point.

[0024] Perform the calibration on an energy meter calibration test platform, setting the platform to output a fixed voltage signal, for example, setting the voltage to the rated value of 220V and the frequency to the grid reference frequency of 50Hz. Determine the power factor range for which phase error calibration is required; a typical range is from an inductive power factor of -0.5 to a capacitive power factor of 0.5. Within this range, generate a series of calibration points at a preset step interval. For example, setting the step interval to 0.05 will generate 21 calibration points: -0.5, -0.45, -0.4, up to 0.45, 0.5.

[0025] For each set calibration point, the test platform adjusts the phase of the output current signal to form a corresponding phase angle with the voltage signal, thereby achieving the required power factor for that calibration point. For example, for a power factor of 0.5, the test platform applies a current signal with a phase lag of 60 degrees behind the voltage. Under this condition, the inherent phase measurement error of the energy meter is measured and recorded. This process is repeated for all 21 calibration points, resulting in a dataset containing 21 sets of power factor values ​​and their corresponding phase error values.

[0026] To find the critical point where the phase error characteristics of the electricity meter change, in an optional embodiment, the step of establishing a function of the initial phase error with respect to the power factor based on the dataset, calculating the second derivative of the function, and determining the power factor value at which the sign of the second derivative of the function changes as the boundary point includes: A polynomial function is used to globally fit the dataset. Calculate the second derivative of the function; Solve the equations whose second derivative is zero, and select real solutions with changes in the sign of the second derivative as boundary points.

[0027] Using the dataset obtained above—multiple sets of paired power factor and phase error data—we employ fitting techniques such as the least squares method to find a polynomial function that optimally represents the global trend of the data points. For example, a seventh-order polynomial could be chosen. To fit the entire dataset, where These are coefficients obtained through fitting.

[0028] After obtaining the globally fitted polynomial function, in order to locate the inflection point of the error curve, i.e., the extreme point of the rate of change of error, it is necessary to calculate the second derivative of the function. The inflection point of the function, in a physical sense, corresponds to the turning point in the error characteristics and is the ideal boundary for delineating different compensation strategies. This is achieved by solving the equation... This allows us to obtain all real solutions to the equation, and select the real solutions whose second derivative sign changes as boundary points. For example, if we obtain -0.21 and 0.35, these are selected as boundary points to divide the entire power factor range, such as -0.5 to 0.5, into three calibration sub-intervals: [-0.5, -0.21], [-0.21, 0.35], and [0.35, 0.5].

[0029] S2, For each calibration sub-interval, use the data within the interval in the dataset to fit and construct the phase error compensation formula for the sub-interval; For a defined calibration sub-interval, such as 0.1L to 0.5L, select all data points in the dataset whose power factor falls within this interval. To achieve the orthogonality of Legendre polynomials and improve numerical stability, the sub-intervals are first... A linear mapping is performed onto the standard interval [-1, 1] to obtain normalized variables. The phase error compensation formula for this sub-interval is assumed to be a weighted sum of multiple Legendre polynomial terms and a hyperbolic tangent function term. Least squares fitting is used, and iterative optimization algorithms such as Levenberg-Marquardt are employed, while simultaneously adjusting all undetermined coefficients in the model. The goal is to minimize the sum of squares between the phase error calculated by the compensation formula and the actual measured phase error of all data points within the interval. When the sum of squares converges to its minimum, the resulting set of weighting coefficients and parameters determines the phase error compensation formula for this sub-interval. This process is repeated for all other calibration sub-intervals.

[0030] In an optional embodiment, the phase error compensation formula is specifically in the form of: Where PF is the power factor, and x is the variable obtained by linearly normalizing PF according to the boundary of the calibration sub-interval. This represents the phase error compensation value within a single calibration sub-interval. For a k-th order Legendre polynomial, , , , These are the undetermined coefficients determined by iterative optimization algorithms fitting the data points within each calibration sub-interval.

[0031] Legendre polynomials are a set of orthogonal polynomials on the interval from -1 to 1. They possess good numerical stability and approximation performance, making them ideal for representing relatively smooth and gradually changing macroscopic trends in error curves, and they can avoid the Runge phenomenon. For example, by taking n=3, Legendre polynomials of orders 0 to 3 can be used to characterize the overall shape of the error within a subinterval.

[0032] The hyperbolic tangent function, with its S-shaped curve characteristics, can simulate local abrupt changes or steep gradient variations in data. By adjusting the coefficients... and This allows control over the steepness and center position of the S-curve, ensuring accurate matching of nonlinear details in the error curve. For example, with m=1, a hyperbolic tangent function can be used to fit a sharp change in error at a specific location within a sub-interval. For each defined calibration sub-interval, the data points within that interval are normalized, and a specific set of coefficients is calculated using a nonlinear iterative optimization algorithm such as Levenberg-Marquardt. , , , This allows for the establishment of a customized compensation model for each sub-interval. The invention utilizes Legendre polynomials and hyperbolic tangent functions to achieve a global orthogonal basis plus local nonlinear correction, enabling the fitting of highly irregular phase error characteristics with fewer parameters, significantly improving calibration accuracy while ensuring computational convergence.

[0033] S3. During the real-time operation of the electricity meter, the real-time power factor is acquired, and the phase error compensation value is calculated based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The internal processor of the electricity meter calculates active power and apparent power in real time by sampling voltage and current values; the ratio of these two values ​​is the real-time power factor. The real-time power factor is then evaluated. If the distance between this value and the nearest boundary point is greater than a preset threshold, it is considered to be within a single calibration sub-interval. In this case, the real-time power factor value is substituted into the phase error compensation formula corresponding to that sub-interval to calculate a specific phase error compensation value. If the distance between the real-time power factor value and a boundary point is less than a preset threshold, it is considered to be near the boundary point. In this case, the phase error compensation formulas of the two adjacent calibration sub-intervals on either side of the boundary point are used simultaneously, and the real-time power factor value is substituted into each for calculation, resulting in two compensation results. Based on the distance from the real-time power factor to the boundary point, two weight values ​​are calculated using a Gaussian function, with the weight decreasing as the distance increases, ensuring that the sum of the two weight values ​​is one. The two compensation results are multiplied by their respective weight values ​​and then summed to obtain a smooth transition phase error compensation value.

[0034] In an optional embodiment, if the real-time power factor is located within a single calibration sub-interval, then calculating the phase error compensation value using the phase error compensation formula for that sub-interval includes: For a calibration sub-interval consisting of adjacent boundary points, a transition region with a preset width is set at both ends of the calibration sub-interval; When the real-time power factor is located in a sub-interval other than the transition region, the real-time power factor is directly substituted into the phase error compensation formula of the sub-interval for calculation.

[0035] During actual operation, the electricity meter monitors the power factor of the power grid in real time. Assume the calibration sub-intervals determined by the above steps are [-0.5, -0.21], [-0.21, 0.35], and [0.35, 0.5], and a transition region with a width of 0.02 is set near each boundary point. For example, the transition region around the boundary point -0.21 is [-0.23, -0.19], and the transition region around the boundary point 0.35 is [0.33, 0.37].

[0036] When the real-time power factor measured by the electricity meter is 0.1, the interval to which this value belongs is determined. Since 0.1 lies within the interval [-0.21, 0.35], and it is not in the transition region [-0.23, -0.19] or [0.33, 0.37] at the two ends of this interval, the phase error compensation formula specifically fitted for the [-0.21, 0.35] sub-interval is called, namely the specific Legendre and hyperbolic tangent weighted sum model. The real-time power factor value of 0.1 is substituted into this formula as an input variable to calculate the corresponding phase error compensation value, which is used in the subsequent correction process.

[0037] To ensure a smooth transition in the calculation of the compensation value when the power factor crosses the boundaries of different calibration sub-intervals, in an optional embodiment, if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, a Gaussian weighted function is used to fuse the calculation results of the phase error compensation formulas for the two adjacent calibration sub-intervals to obtain a smooth phase error compensation value, including: Let two adjacent calibration sub-intervals be interval i and interval i+1, and the boundary point be... ,by Define the width as the center. transition area , ; When the real-time power factor PF is in the transition region, the compensation formula for interval i is used respectively. Compensation formula for interval i+1 Calculate the compensation value; Weights based on Gaussian error function and Weighted fusion is performed to obtain a smoothed phase error compensation value. : ; ; ; in, This is the Gaussian error function.

[0038] Suppose two adjacent subintervals are interval i ([-0.21, 0.35]) and interval i+1 ([0.35, 0.5]), with boundary points as follows: Define an array centered at 0.35 with a width of... The transition region is [0.33, 0.37]. When the real-time power factor measured by the electricity meter is 0.34, this value falls exactly within this transition region.

[0039] A fusion calculation will be performed using the compensation formula for interval i. Calculate a compensation value The compensation formula for interval i+1 is used. Calculate another compensation value The weights are calculated based on the Gaussian error function. For example, calculating... From the table or by calculation, we get erf(-0.5)≈-0.5205, then... , The smoothing compensation value will be the weighted sum of the two compensation values ​​mentioned above. As the real-time power factor shifts from 0.33 to 0.37, the weights... It will smoothly drop from 1 to near 0, and It will smoothly rise from 0 to close to 1, thus achieving a seamless switch between the outputs of the two models.

[0040] S4. The calculated phase error compensation value is used to correct the energy metering result.

[0041] After obtaining the phase error compensation value, the energy meter processor directly applies a time delay or advance corresponding to the phase error compensation value to the current sampling sequence through digital signal processing technology, thereby correcting the phase deviation at the signal source before performing power calculation.

[0042] In an optional embodiment, the step of correcting the electricity meter's metering results using the calculated phase error compensation value includes: Acquire digital sampling sequences of voltage and current; Convert the phase error compensation value into a sampling point offset: ; in, Sampling frequency, The fundamental frequency of the power grid; the unit of phase error compensation value is radians. Based on the sampling point offset, the corrected current sampling sequence is obtained by performing digital shift or fractional delay interpolation on the current sampling sequence. The corrected current sampling sequence is multiplied and summed with the voltage sampling sequence to calculate the corrected energy value.

[0043] The analog-to-digital converter of the electricity meter operates at a fixed sampling frequency, such as... The 50Hz grid voltage and current waveforms are sampled to obtain discrete digital sequences. Simultaneously, the above steps have calculated a phase error compensation value, for example, 0.005 radians, based on the current power factor. This radian phase error needs to be converted to time or an offset at sampling points. The sampling point offset is calculated using a formula, yielding a result approximately equal to 0.102 sampling points.

[0044] Since the calculated offset of 0.102 is a decimal, it cannot be simply achieved through integer shifting; therefore, fractional delay techniques from digital signal processing are required. A common approach is to use a fractional delay filter based on polynomial interpolation, such as a Lagrange interpolation or Faroe's structure filter. This filter performs real-time calculations on the original current sampling sequence to generate a new current sampling sequence. This new sequence is offset by 0.102 sampling periods relative to the original sequence, thus correcting the phase error. The energy meter's calculation unit multiplies the corrected current sampling sequence point-by-point with the synchronized voltage sampling sequence to obtain the instantaneous power, and then integrates the instantaneous power values ​​to obtain the energy metering value after phase error correction.

[0045] In a second embodiment, the present invention also provides an online adaptive calibration system for low power factor error in electricity meters, comprising the following modules: A module is established to apply voltage and current signals to an energy meter at multiple preset low power factor calibration points, measure and record the initial phase error corresponding to each calibration point, and form a dataset containing the relationship between power factor and initial phase error. Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor values ​​where the sign of the second derivative of the function changes are determined as boundary points, thereby dividing the low power factor range into multiple calibration sub-intervals. A construction module is used to fit a phase error compensation formula for each calibration sub-interval using data within the interval in the dataset. The calculation module is used to acquire the real-time power factor during the real-time operation of the electricity meter, and calculate the phase error compensation value based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The calibration module is used to calibrate the energy meter's energy measurement results using the calculated phase error compensation value.

[0046] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An online adaptive calibration method for low power factor error in electricity meters, characterized in that, Includes the following steps: At multiple preset low power factor calibration points, voltage and current signals are applied to the energy meter, and the initial phase error corresponding to each calibration point is measured and recorded to form a dataset containing the relationship between power factor and initial phase error. Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor value with the sign change of the second derivative of the function is determined as the boundary point, thereby dividing the low power factor range into multiple calibration sub-intervals; For each calibration sub-interval, a phase error compensation formula for the sub-interval is fitted using the data within the interval in the dataset; During the real-time operation of the electricity meter, the real-time power factor is acquired, and the phase error compensation value is calculated based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The calculated phase error compensation value is used to correct the energy meter's energy measurement results.

2. The method according to claim 1, characterized in that, The step of applying voltage and current signals to the energy meter at multiple preset low power factor calibration points includes: The voltage signal is set to the rated voltage and rated frequency. Within the preset range of inductive and capacitive power factors, multiple calibration points are set at preset step intervals, and a corresponding current signal is applied to each calibration point.

3. The method according to claim 2, characterized in that, Based on the dataset, the process of establishing a function of the initial phase error with respect to the power factor, calculating the second derivative of the function, and determining the power factor values ​​where the sign of the second derivative changes as boundary points includes: A polynomial function is used to globally fit the dataset. Calculate the second derivative of the function; Solve the equations whose second derivative is zero, and select real solutions with changes in the sign of the second derivative as boundary points.

4. The method according to claim 1, characterized in that, The specific form of the phase error compensation formula is as follows: Where PF is the power factor, and x is the variable obtained by linearly normalizing PF according to the boundary of the calibration sub-interval. This represents the phase error compensation value within a single calibration sub-interval. For a k-th order Legendre polynomial, , , , These are the undetermined coefficients determined by iterative optimization algorithms fitting the data points within each calibration sub-interval.

5. The method according to claim 1, characterized in that, If the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula for that sub-interval, including: For a calibration sub-interval consisting of adjacent boundary points, a transition region with a preset width is set at both ends of the calibration sub-interval; When the real-time power factor is located in a sub-interval other than the transition region, the real-time power factor is directly substituted into the phase error compensation formula of the sub-interval for calculation.

6. The method according to claim 1, characterized in that, If the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, a Gaussian weighted function is used to fuse the calculation results of the phase error compensation formula for the two adjacent calibration sub-intervals to obtain a smooth phase error compensation value, including: Let two adjacent calibration sub-intervals be interval i and interval i+1, and the boundary point be... ,by Define the width as the center. transition area , ; When the real-time power factor PF is in the transition region, the compensation value is calculated using the compensation formulas for interval i and interval i+1, respectively. The compensation values ​​are weighted and fused using a weight based on a Gaussian error function to obtain smooth phase error compensation values.

7. The method according to claim 5, characterized in that, The step of correcting the electricity meter's metering results using the calculated phase error compensation value includes: Acquire digital sampling sequences of voltage and current; Convert the phase error compensation value into a sampling point offset: ; in, Sampling frequency, The fundamental frequency of the power grid; the unit of phase error compensation value is radians. Based on the sampling point offset, the corrected current sampling sequence is obtained by performing digital shift or fractional delay interpolation on the current sampling sequence. The corrected current sampling sequence is multiplied and summed with the voltage sampling sequence to calculate the corrected energy value.

8. An online adaptive calibration system for low power factor error in electricity meters, characterized in that, Includes the following modules: A module is established to apply voltage and current signals to the energy meter at multiple preset low power factor calibration points, measure and record the initial phase error corresponding to each calibration point, and form a dataset containing the relationship between power factor and initial phase error. Based on the dataset, a function of the initial phase error with respect to the power factor is established, the second derivative of the function is calculated, and the power factor value with the sign change of the second derivative of the function is determined as the boundary point, thereby dividing the low power factor range into multiple calibration sub-intervals; A construction module is used to fit a phase error compensation formula for each calibration sub-interval using data within the interval in the dataset. The calculation module is used to acquire the real-time power factor during the real-time operation of the electricity meter, and calculate the phase error compensation value based on the relative position of the real-time power factor and the boundary point: if the real-time power factor is located within a single calibration sub-interval, the phase error compensation value is calculated using the phase error compensation formula of the sub-interval; if the real-time power factor is located near the boundary point of two adjacent calibration sub-intervals, the calculation results of the phase error compensation formula of the two adjacent calibration sub-intervals are fused using a Gaussian weighted function to obtain a smooth phase error compensation value; The calibration module is used to calibrate the energy meter's energy measurement results using the calculated phase error compensation value.

9. The system according to claim 8, characterized in that, The step of applying voltage and current signals to the energy meter at multiple preset low power factor calibration points includes: The voltage signal is set to the rated voltage and rated frequency. Within the preset range of inductive and capacitive power factors, multiple calibration points are set at preset step intervals, and a corresponding current signal is applied to each calibration point.

10. The system according to claim 8, characterized in that, Based on the dataset, the process of establishing a function of the initial phase error with respect to the power factor, calculating the second derivative of the function, and determining the power factor values ​​where the sign of the second derivative changes as boundary points includes: A polynomial function is used to globally fit the dataset. Calculate the second derivative of the function; Solve the equations whose second derivative is zero, and select real solutions with changes in the sign of the second derivative as boundary points.

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