High-precision voltage and current characteristic quantity extraction and anti-interference sampling circuit compensation method

Abstract

The invention discloses a high-precision voltage and current characteristic quantity extraction and anti-interference sampling circuit compensation method, and belongs to the technical field of voltage and current sampling, and the method comprises the steps: employing a 24-bit ADC to collect voltage and current signals at the sampling frequency of 1 kHz to 10 kHz; electromagnetic interference is inhibited through the metal shielding cover and the wave absorbing material; dynamically correcting the sampling data through a temperature compensation model; dynamically correcting the sampling data based on an LMS algorithm; voltage and current effective values are calculated through a root mean square algorithm in a sliding window mode, a processing result is output in real time, and the problems that existing sampling precision is low and the error compensation effect is limited are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage and current sampling, and in particular to a method for extracting high-precision voltage and current characteristic quantities and compensating an anti-interference sampling circuit. Background Art

[0002] In the era of widespread access to smart grids and renewable energy, modern power systems place stringent demands on high-precision, real-time measurement of electrical energy parameters. As a core component of power metering, fault diagnosis, and equipment health management, the performance of voltage and current sampling technology directly impacts the safety, economy, and reliability of power system operations. With the increasing frequency of power electronic devices and the large-scale grid connection of distributed power sources, the system operating environment presents complex characteristics such as strong electromagnetic interference, wide temperature fluctuations, and rapid changes in transient currents. The limitations of traditional sampling technology are becoming increasingly prominent.

[0003] Traditional voltage and current sampling technologies usually rely on analog current and voltage sampling devices (such as voltage transformers and current transformers) and analog-to-digital converters (ADCs) for data acquisition. These traditional methods usually use low-precision sampling technologies or adopt low sampling frequencies to deal with current and voltage changes in power systems. This may result in low sampling accuracy in many application scenarios and fail to effectively capture rapidly changing current or voltage characteristics. In order to improve sampling accuracy, many traditional solutions have added filtering circuits and temperature compensation algorithms, but these compensation measures are often not sufficient to fully resolve errors caused by electromagnetic interference and rapid temperature changes. In addition, traditional technologies also face real-time issues in hardware and algorithms. Especially under the requirements of high-precision sampling, there is a certain contradiction between sampling frequency, computational complexity and hardware performance.

[0004] Existing traditional voltage and current sampling technologies exhibit some obvious limitations when faced with the complex operating environment of modern power systems. First, the accuracy of traditional sampling is limited by low resolution and slow sampling frequency. In particular, during rapid circuit breaker operation or sudden load changes, it may not be possible to accurately capture transient changes in current and voltage, which may lead to measurement errors. Secondly, due to the influence of temperature changes and electromagnetic interference, traditional solutions cannot effectively deal with errors caused by changes in device characteristics. Therefore, they often need to rely on cumbersome calibration processes, and their compensation effect is limited. Most importantly, traditional electromagnetic interference suppression methods mostly rely on passive components (such as electromagnetic shielding and filters) to reduce interference signals, but these measures are often unable to completely eliminate electromagnetic noise in complex environments, especially in high-frequency bands. These technical bottlenecks not only restrict the performance improvement of core equipment such as smart meters and relay protection devices, but also become a key obstacle to the transformation of power systems towards digitalization and intelligence. Summary of the Invention

[0005] The purpose of the present invention is to provide a high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method, which solves the problems of low sampling accuracy and limited error compensation effect in the existing method.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is as follows:

[0007] A method for extracting high-precision voltage and current characteristic quantities and compensating an anti-interference sampling circuit, comprising:

[0008] Use a 24-bit ADC to collect voltage and current signals at a sampling frequency of 1kHz to 10kHz;

[0009] Suppress electromagnetic interference through metal shielding and absorbing materials;

[0010] Dynamically correct sampling data through temperature compensation model;

[0011] Dynamically correct sampling data based on LMS algorithm;

[0012] The sliding window method is used to calculate the effective value of voltage and current through the root mean square algorithm, and the processing results are output in real time.

[0013] Furthermore, the sampling frequency is 2 kHz.

[0014] Furthermore, the dynamically correcting the sampled data using the temperature compensation model includes:

[0015] Real-time monitoring of component temperature characteristics through temperature sensors;

[0016] Calculate the sampling error caused by temperature;

[0017] Calculate temperature compensation coefficient;

[0018] Dynamically correct temperature errors in sampled data.

[0019] Furthermore, the dynamically correcting the sampled data using the temperature compensation model further includes:

[0020] Calibrate temperature sensors and update models regularly.

[0021] Furthermore, the dynamically correcting the sampled data based on the LMS algorithm includes:

[0022] Monitor interference signatures using an electromagnetic interference meter;

[0023] Design digital filters;

[0024] Adopt LMS adaptive filtering algorithm to adjust filter coefficients;

[0025] Calculate electromagnetic interference compensation coefficient;

[0026] Dynamically adjust filter parameters and compensation coefficients.

[0027] Furthermore, the designing of the digital filter includes:

[0028] Determine the filter's cutoff frequency;

[0029] Determines the order of the filter.

[0030] Furthermore, the designed digital filter is accelerated by FPGA or DSP hardware.

[0031] Furthermore, the metal shield at least covers the ADC module, the metal shield adopts multi-point grounding, and the absorbing material is arranged inside the metal shield.

[0032] Furthermore, the effective values of voltage and current are calculated by the root mean square algorithm using a sliding window method, including:

[0033] Divide the sampled data into windows of fixed length;

[0034] The window is updated and the effective value is recalculated each time new data is received.

[0035] Furthermore, the calculation of the effective value of voltage and current by the root mean square algorithm using the sliding window method also includes:

[0036] The window length is dynamically adjusted according to the signal fluctuation characteristics.

[0037] The beneficial effects of the present invention are:

[0038] (1) This high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method uses a 24-bit ADC chip, which can dynamically adjust the sampling frequency between 1kHz and 10kHz according to actual conditions to balance the sampling accuracy and calculation amount, thereby accurately controlling the sampling frequency, reducing the hardware calculation pressure, and ensuring high-precision sampling. According to experimental findings, selecting a sampling frequency of 2kHz can better capture signal changes and avoid the impact of high-frequency noise on data processing. At the same time, it can also avoid the additional processing burden and possible interference caused by excessively high frequencies, and can ensure high-precision sampling of voltage and current signals. According to experiments, it can be determined that the sampling results meet the requirements of monthly metering accuracy ≤0.1% and annual metering accuracy ≤0.2%.

[0039] (2) This high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method can effectively address the impact of temperature changes on sampling errors by modeling and compensating the temperature characteristics of components. The real-time calculation and update of the temperature compensation coefficient ensures that the impact of temperature changes on sampling accuracy during power system operation is effectively suppressed, thereby ensuring long-term stable sampling performance.

[0040] (3) This high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method addresses the strong electromagnetic interference generated by the instantaneous operation of the circuit breaker. This solution uses a compensation algorithm based on electromagnetic interference source analysis. By designing a digital filter and adjusting the filter parameters using the minimum mean square error algorithm, the interference signal is accurately removed, reducing the impact of electromagnetic interference on the sampled data. Through this processing, the system can still maintain high-precision data sampling in complex electromagnetic environments.

[0041] (4) This high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method effectively blocks external electromagnetic interference signals by combining a metal shield with grounding measures. The metal shield not only improves the ability to suppress electromagnetic interference, but also further reduces the impact of high-frequency signals through absorbing materials. The grounding measures enhance the system's anti-interference ability by providing a low-impedance path, ensuring stable operation of the system even in complex electromagnetic environments.

[0042] (5) This high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method ensures the real-time and stability of the sampling system through the design of filtering algorithms and temperature compensation. By using a sliding window method to calculate the effective value and combining real-time temperature data with electromagnetic interference monitoring, the system can dynamically adjust the compensation parameters to ensure that the sampling accuracy always meets the requirements and avoid data distortion caused by external factors.

[0043] (6) The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method is optimized through the coordinated optimization of hardware and algorithms. In terms of hardware, the reasonable layout of the metal shielding cover, grounding measures and absorbing materials reduces the hardware cost and volume, while improving the anti-interference ability; in terms of algorithms, the system accuracy and reliability are greatly improved through the optimized digital filtering algorithm and compensation coefficient calculation, ensuring the extraction of high-precision voltage and current feature quantities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a voltage and current sampling circuit for the high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method provided by the present invention;

[0045] Figure 2 A graph showing the relationship between sampling frequency and signal frequency for the high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method provided by the present invention;

[0046] Figure 3 A flow chart of the temperature compensation algorithm for the high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method provided by the present invention;

[0047] Figure 4This is a design diagram of the electromagnetic interference compensation filter for the high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method provided by the present invention.

[0048] Figure 5 This is a schematic diagram of the electromagnetic shielding connection of the high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method provided by the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the application without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] like Figures 1 to 5 A high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method is shown, including: using a 24-bit ADC to collect voltage and current signals at a sampling frequency of 1kHz to 10kHz; suppressing electromagnetic interference through a metal shield and absorbing materials; dynamically correcting the sampled data through a temperature compensation model; dynamically correcting the sampled data based on the LMS algorithm; and calculating the effective value of the voltage and current using a root mean square algorithm using a sliding window method, and outputting the processing results in real time.

[0051] A 24-bit ADC chip is used for voltage and current sampling to achieve high-precision sampling requirements of monthly measurement accuracy ≤ 0.1% and annual measurement accuracy ≤ 0.2%. Regarding the setting of the sampling frequency, if the sampling frequency is too high, the data processing burden will affect the processing speed of the hardware. If the sampling frequency is too low, it will not be able to capture all the details of the signal. Therefore, the optimal setting of the sampling frequency is the key to ensuring a balance between accuracy and system performance.

[0052] First, consider the standard operating frequency of power systems, which is 50Hz or 60Hz. To comply with the Nyquist sampling theorem, the sampling frequency needs to be at least twice the power system frequency, that is, 100Hz or 120Hz. However, this frequency is too low to capture all the details in the signal. In practical applications, this is especially true because it cannot capture the rapid changes in large currents, including harmonic components, and the transient operation of circuit breakers.

[0053] Secondly, it is necessary to consider that the sampling signal contains the fundamental frequency and harmonic components of voltage and current. First, assuming that the sampling signal contains the fundamental frequency and harmonic components of voltage and current, the effective sampling frequency of the signal is calculated using the following formula:

[0054] f s ≥2·f max

[0055] Among them, f s is the sampling frequency, f max The highest frequency harmonic component in the signal. To ensure that all frequency components in the signal are accurately sampled, the sampling frequency must be higher than twice the highest frequency of the signal. If the signal contains a fundamental frequency of 60 Hz and its fifth harmonic (300 Hz), the sampling frequency must be 600 Hz or even higher.

[0056] Thirdly, it is necessary to consider the rapid changes in large current during the instantaneous operation of the circuit breaker, and the sampling frequency needs to be increased to a sufficiently high value to capture the high-frequency transient changes in the signal.

[0057] Based on the above reasons, this application sets the sampling frequency to ≥1kHz.

[0058] Similarly, too high a sampling frequency will generate a huge amount of sampling data. For hardware devices that process the sampling data, too much data will cause data processing delays, which will increase the pressure on hardware performance. Therefore, this application sets the sampling frequency to ≤10kHz.

[0059] Considering the balance between comprehensive sampling accuracy and data processing capabilities, this application prefers to set the sampling frequency to 2kHz. A lower sampling frequency, such as 2kHz, can better capture signal changes and avoid the impact of high-frequency noise on data processing. It can also avoid the additional processing burden and possible interference caused by excessively high frequencies. Within this frequency range, the optimization of the sampling frequency needs to comprehensively consider factors such as the operating characteristics of the power system, the harmonic components in the signal, the instantaneous action of the circuit breaker, and the hardware performance.

[0060] In power systems, voltage and current signals are periodic AC signals. The root mean square (RMS) algorithm is a commonly used method for calculating the effective value of voltage and current. This algorithm can accurately reflect the actual working capacity of AC signals and is particularly suitable for periodic waveforms in power systems. The specific calculation process is to square each sampled value of voltage and current, then accumulate all square values and calculate the average value, and finally square the average value to obtain the effective value. Assume that the voltage sample value sequence is V1, V2, ..., V N The current sampling value sequence is I1, I2, ..., I N , where N is the number of window sampling points, then the voltage RMS V rms And the effective value of current I rms The calculation formula is:

[0061]

[0062] In this formula, V i and I iare the values of the i-th sampling point of the voltage and current, respectively, and N is the number of sampling points in the window. By calculating these effective values, the influence of the DC component and even harmonics in the signal can be removed, thereby accurately reflecting the actual power capacity of the voltage and current.

[0063] To improve computational efficiency and meet real-time requirements, a sliding window approach is used for RMS calculation. Specifically, the data is divided into windows of fixed length. Whenever new sampled data enters, the data within the window is updated, and the RMS value is recalculated. This approach ensures that the RMS values of voltage and current are updated in real time as new data is continuously input, accurately reflecting the dynamic changes in the voltage and current signals.

[0064] To avoid overflow problems caused by large amounts of data involved in the calculation process, it is necessary to reasonably select data types and calculation methods to ensure calculation accuracy. When using a 24-bit ADC for sampling, choose the appropriate value type (such as floating-point or fixed-point) to effectively avoid incorrect calculations due to overflow. At the same time, necessary overflow checks must be performed at the software level to ensure the accuracy of the results. For example, in the sliding window RMS calculation, fixed-point acceleration is used for square operations, while floating-point precision is used for square root and division.

[0065] The present application adopts a sliding window method to calculate the effective value of voltage and current through the root mean square algorithm, including: dividing the sampled data into windows of fixed length; updating the window and recalculating the effective value every time new data is received.

[0066] Calculating the RMS values of voltage and current using the sliding window method also involves dynamically adjusting the window length based on signal fluctuation characteristics. Considering the potential for sudden fluctuations or interference in voltage and current signals, the sliding window length is crucial. If the window is too short, the signal may not be adequately smoothed, resulting in unstable calculation results. However, if the window is too long, the response speed may be slow, and signal changes may not be reflected promptly. Therefore, the window length needs to be optimized based on the actual application scenario and data characteristics. For example, in steady-state scenarios such as energy metering and load analysis, a longer window length can be selected. For example, if the fundamental wave is 50 Hz, the sampling frequency is 2 kHz, and the coverage period is one cycle, the window length N = 40. Considering the fifth-order harmonic of 250 Hz, the coverage period is two cycles, and the window length N = 80. For transient detection scenarios such as circuit breaker operation and fault recording, a shorter window length can be selected to ensure a faster response and timely reflection of signal changes. The window length can be calculated based on the maximum allowable delay. For example, if the maximum allowable delay is 5 ms and the sampling frequency is 2 kHz, the window length N = 0.5 ms, then the window length N = 10.

[0067] In the voltage and current sampling circuit, due to the temperature characteristics of the components, temperature changes may cause fluctuations in component parameters, thereby affecting the sampling accuracy. Especially when the circuit breaker is working, the temperature changes of the components are more significant. This is mainly due to the large current passing through and the limitation of space heat dissipation conditions. The performance of components such as resistors and capacitors in the sampling circuit changes, resulting in sampling errors. Figure 3 As shown, the present application corrects errors through temperature compensation to ensure the accuracy of the sampled data. The dynamic correction of sampled data through the temperature compensation model includes: real-time monitoring of component temperature characteristics through temperature sensors; calculating sampling errors caused by temperature; calculating temperature compensation coefficients; and dynamically correcting temperature errors in the sampled data.

[0068] Temperature sensors can be used to monitor temperature changes in circuits in real time. Using the temperature sensor's output data as input, combined with the aforementioned compensation algorithm, the sampled data is dynamically adjusted. This ensures high accuracy of the sampled data under varying temperature conditions. Furthermore, to improve compensation accuracy, the temperature sensor must be regularly calibrated to ensure accurate temperature measurement. The mathematical model linking temperature and component parameters must also be regularly updated based on the actual operating conditions of the components. Dynamically correcting the sampled data using the temperature compensation model also includes regularly calibrating the temperature sensor and updating the model.

[0069] To calculate the temperature compensation coefficient, it is necessary to experimentally measure the temperature characteristics of key components in the circuit (such as resistors) to obtain the parameter changes of the components at different temperatures. The temperature change of the resistor is accurately measured by a temperature sensor. The relationship between the resistance value and temperature change is described by the following formula:

[0070] R(T)=R0·(1+α·(T-T0))

[0071] Where R(T) is the resistance at temperature T, R0 is the resistance at reference temperature T0, α is the temperature coefficient of resistance, T is the current temperature, and T0 is the reference temperature. This formula describes the linear variation of resistance with temperature and is often used for temperature compensation of resistors.

[0072] On this basis, the model is used to calculate the voltage and current sampling errors caused by temperature changes. For voltage and current signals, temperature changes will cause the values of components such as resistors and capacitors in the circuit to shift, thereby affecting the accuracy of the sampled signals. By correlating the effect of temperature on component parameters with the sampled data, the error value caused by temperature is calculated. Set the temperature compensation coefficient K temp is the influence factor of temperature on the error, which can be expressed as:

[0073]

[0074] Where ΔV is the voltage error caused by temperature change, and ΔT is the temperature change. By inputting the current temperature value in real time, a linear correction is performed on the sampled data to offset the effects of temperature drift.

[0075] In the power system, the instantaneous action of the circuit breaker will generate strong electromagnetic radiation, which will interfere with the normal operation of the sampling circuit and cause deviations in the sampling data. The impact of this electromagnetic interference (EMI) on the circuit is manifested as the introduction of high-frequency noise in the spectrum, which in turn affects the sampling accuracy of voltage and current. Therefore, this application also dynamically corrects the sampling data based on the LMS algorithm, such as Figure 4 As shown, the dynamic correction of sampling data based on the LMS algorithm includes: monitoring interference characteristics through an electromagnetic interference measuring instrument; designing a digital filter; adjusting the filter coefficient using the LMS adaptive filtering algorithm; calculating the electromagnetic interference compensation coefficient; and dynamically adjusting the filter parameters and compensation coefficient.

[0076] Locating and analyzing electromagnetic interference sources involves determining characteristics such as the frequency range, intensity, and propagation path of the interference. When a circuit breaker actuates instantaneously, the frequency range of the interference signal generated is often very wide, encompassing multiple frequency bands. Therefore, an electromagnetic interference meter (EMI meter) is used to collect the spectral characteristics of the interference signal (frequency range, intensity distribution, etc.) in real time to provide data support for filter design. EMI meters are commercially available.

[0077] Based on the characteristics of these interference signals, digital filters are designed to filter the sampled data. The primary task of digital filters is to effectively suppress electromagnetic interference signals while preserving the original characteristics of the voltage and current signals. When designing a filter, filter parameters (such as cutoff frequency and bandwidth) need to be adjusted based on the spectral characteristics of the interference signal. The goal of the filter is to maximize the removal of interference signals while minimizing the loss of the desired signal.

[0078] The digital filter circuit uses a finite impulse response (FIR) filter to effectively filter out electromagnetic interference signals while retaining the main frequency components of the voltage and current signals to ensure the accuracy of the sampled data. The main advantage of the FIR filter is its linear phase characteristic, which can ensure that the signal does not produce phase distortion during the filtering process, which is crucial for accurately extracting voltage and current characteristics. When designing an FIR filter, it is first necessary to determine the filter parameters based on the frequency characteristics of the voltage and current signals and the frequency range of the electromagnetic interference to ensure that the filter does not affect the phase of the signal during the filtering process while effectively suppressing electromagnetic interference. The design of the digital filter includes: determining the cutoff frequency of the filter; determining the order of the filter.

[0079] Determining the filter's cutoff frequency is a key step in the design process. The cutoff frequency must be selected to effectively filter out electromagnetic interference signals while retaining the main frequency components of the voltage and current signals to the greatest extent possible. Electromagnetic interference signals are mainly concentrated in the high frequency band, while the useful frequency components of the voltage and current signals are concentrated in the low frequency band. Therefore, the cutoff frequency should be set between the upper edge of the high-frequency interference signal and the effective frequency band of the voltage and current signals. low to f high , and electromagnetic interference is mainly concentrated above f high frequency band, then select the cutoff frequency f c satisfy:

[0080] f low <f c <f high

[0081] This setting ensures that the electromagnetic interference signal can be effectively filtered out while the effective components of the voltage and current signals are not affected.

[0082] Secondly, the order of the filter is also an important parameter in the design. The higher the order of the FIR filter, the better the filtering effect of the filter, because it can more accurately approximate the ideal filtering characteristics, but at the same time the computational complexity and hardware resource requirements will also increase. In practical applications, it is necessary to determine the appropriate order through simulation and experiments based on hardware resources and filtering performance requirements. The filter coefficients are designed by combining FIR filters with window function methods (such as Hanning window and Hamming window) to balance filtering performance and hardware resource consumption to adjust the impulse response of the ideal filter, thereby obtaining the actual FIR filter coefficients. The basic principle of the window function method is to convolve the impulse response of the ideal filter with the window function to obtain filter coefficients of finite length. The impulse response of the ideal filter is h ideal (n), the window function is w(n), then the FIR filter coefficient h(n) is:

[0083] h(n)=h ideal (n)·w(n)

[0084] The main lobe width and side lobe attenuation characteristics of the filter are controlled by adjusting the window function type, thereby optimizing the filter performance.

[0085] When implementing an FIR filter, hardware platforms such as digital signal processors (DSPs) or field-programmable gate arrays (FPGAs) are used to implement the filter algorithm through hardware circuits. Hardware implementation improves filtering processing speed and real-time performance, ensuring that the system can effectively cope with electromagnetic interference in a real-time environment and maintain high-precision sampling.

[0086] Furthermore, to ensure filter stability and reliability, the filter coefficients must be quantized. Due to hardware bit width limitations, quantization of filter coefficients can introduce errors, necessitating appropriate error analysis and compensation to ensure filter accuracy and stability in practical applications.

[0087] Through this design, the FIR filter effectively removes electromagnetic interference while preserving the original signal characteristics, enabling high-precision voltage and current sampling. This design not only theoretically ensures the accuracy of the sampled data but also adapts to complex electromagnetic interference environments and meets the filtering requirements of real-time applications.

[0088] The Least Mean Square Error (LMS) algorithm is a commonly used adaptive filtering algorithm that adjusts the filter coefficients by minimizing the mean square error of the filtered error signal. Specifically, let the filter output signal be y[n] and the actual sampled signal be d[n]. The filter's goal is to minimize the mean square error of the error signal e[n] = d[n] - y[n]. The LMS algorithm achieves this goal by continuously adjusting the filter's weight coefficients w[n], and the update rule is:

[0089] w[n+1]=w[n]+μe[n]x[n]

[0090] Where μ is the learning rate, which controls the step size of the weight update, e[n] is the error signal, x[n] is the input signal, and w[n] is the filter weight vector. This formula shows that by continuously iteratively updating the filter parameters, the error signal is gradually reduced, thereby improving the filter performance.

[0091] Through this adaptive filtering algorithm, the electromagnetic interference compensation coefficient is calculated based on the difference in sampling data before and after filtering. Electromagnetic interference compensation coefficient K emi It can be expressed by the following formula:

[0092]

[0093] Where d[n] is the original sampled signal, y[n] is the filtered signal, and n is the number of sampling points. This formula quantifies the impact of electromagnetic interference on the sampled signal by calculating the error before and after filtering and derives the compensation coefficient.

[0094] In practical applications, due to the complexity and variability of electromagnetic interference environments, the compensation process requires continuous optimization. Regularly monitoring changes in electromagnetic interference sources and adjusting filter parameters in real time are key to ensuring that the sampling circuit can maintain high-precision sampling in strong electromagnetic interference environments.

[0095] The FPGA includes a data acquisition module, a filter calculation module, an LMS update module, and an output module. The data acquisition module receives sampled data from the ADC via a high-speed interface (such as LVDS) and caches it in a FIFO to match the processing speed of subsequent modules. The filter calculation module implements the FIR filter using a distributed arithmetic (DA) architecture. The filter coefficients are stored in block RAM (block random access memory), and input data is sequentially input through shift registers. The LMS coefficient update module calculates the error signal e[n] based on the filter output and the desired signal, and updates the filter coefficients according to w[n+1]=w[n]+μe[n]x[n]. The learning rate μ is configurable through registers. The output module outputs the filtered results through an interface, which can use the AXI4-Stream protocol. The DSP includes three parts: data acquisition, algorithm processing, and result output. During algorithm processing, the DSP's instruction-level parallelism can be utilized to optimize multiplication and accumulation operations. Finally, the filtered results are output through a serial port or other interface.

[0096] Figure 5 As shown, the metal shield at least covers the ADC module and is multi-point grounded. The absorbing material is disposed inside the metal shield. The shield is made of a highly conductive material such as copper or aluminum and at least covers sensitive components such as the ADC module. Its shape and size are optimized based on the circuit board layout to ensure there are no electromagnetic leakage gaps. The shield uses multi-point grounding technology, connecting to the ground plane via short-path, large-cross-sectional area conductors. Assuming the distance d between the shield and the sampling circuit board, the effective shielding effectiveness S of the shield can be approximately estimated by the following formula:

[0097]

[0098] The closer the shielding cover is to the circuit board, the stronger the shielding effectiveness. At the same time, when designing, it is important to avoid gaps or holes between the shielding cover and the circuit to prevent electromagnetic interference from entering through these gaps, thereby reducing the shielding effect.

[0099] By directly connecting the shield to the ground plane and using multi-point grounding, the coupling of electromagnetic waves to the shield can be effectively reduced, ensuring that the shield absorbs and guides electromagnetic interference more efficiently. To ensure the grounding effect, the grounding resistance must be kept as small as possible. The grounding resistance can be calculated using the following formula:

[0100]

[0101] Among them, R gis the ground resistance, ρ is the resistivity of the ground material, and A is the cross-sectional area of the ground path. To minimize ground resistance, the ground path should be as short as possible and use conductors with large cross-sectional areas. Multi-point grounding disperses interference currents and avoids common-mode noise caused by interference signals passing through a single ground point.

[0102] In addition, absorbing materials are added to the interior of the shield to further enhance the electromagnetic shielding effect. These absorbing materials can absorb a portion of high-frequency electromagnetic waves, reducing standing waves within the shield. The design of the absorbing material should consider its matching characteristics with the electromagnetic wave. Its absorbing capacity α is related to the material's dielectric constant ∈ and magnetic permeability μ. The effect of the absorbing material can be expressed as follows:

[0103]

[0104] Where f is the frequency of the electromagnetic wave. By properly selecting absorbing materials, the reflection of interference signals can be effectively reduced, thereby further improving the shielding effect.

[0105] In practical applications, to ensure the long-term effectiveness of the shielding structure, the integrity of the metal shield and its contact with the circuit board must be regularly inspected. Any damage or looseness can cause electromagnetic interference signals to leak, affecting system performance. Grounding measures should also be regularly inspected to ensure that the grounding path is unobstructed and the resistance remains within a reasonable range to ensure stable system operation in complex electromagnetic environments. This comprehensive electromagnetic shielding design significantly improves the anti-interference capabilities of the voltage and current sampling system, ensuring the achievement of high-precision sampling requirements.

[0106] This method achieves a high-precision sampling and anti-interference compensation system through the coordinated optimization of hardware shielding (metal shielding, absorbing materials, and multi-point grounding) and software algorithms (temperature compensation, LMS filtering, and sliding window RMS calculation). Field measurements have verified that this solution maintains voltage and current sampling accuracy within the technical specifications of ≤0.1% for monthly measurement and ≥0.2% for annual measurement, even in a wide temperature range (-40°C to 85°C) and strong electromagnetic interference (RF fields above 10V / m). This effectively addresses the inaccuracy of traditional sampling technologies under complex operating conditions.

[0107] Based on the disclosure and teachings of the above description, those skilled in the art may also make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and any modifications and variations of the present invention should also fall within the scope of protection of the claims of the present invention. In addition, although certain specific terms are used in this description, these terms are only for convenience of description and do not constitute any limitation to the present invention.

Claims

1. A method for extracting high-precision voltage and current characteristic quantities and compensating for anti-interference sampling circuits, characterized by: include: Use a 24-bit ADC to collect voltage and current signals at a sampling frequency of 1kHz to 10kHz; Suppress electromagnetic interference through metal shielding and absorbing materials; Dynamically correct sampling data through temperature compensation model; Dynamically correct sampling data based on LMS algorithm; The sliding window method is used to calculate the effective value of voltage and current through the root mean square algorithm, and the processing results are output in real time.

2. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 1 is characterized in that: The sampling frequency is 2 kHz.

3. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 1 is characterized in that: The dynamic correction of the sampled data by the temperature compensation model includes: Real-time monitoring of component temperature characteristics through temperature sensors; Calculate the sampling error caused by temperature; Calculate temperature compensation coefficient; Dynamically correct temperature errors in sampled data.

4. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 3 is characterized by: The dynamically correcting the sampled data by the temperature compensation model further comprises: Calibrate temperature sensors and update models regularly.

5. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 1 is characterized in that: The dynamic correction of sampling data based on the LMS algorithm includes: Monitor interference signatures using an electromagnetic interference meter; Design digital filters; Adopt LMS adaptive filtering algorithm to adjust filter coefficients; Calculate electromagnetic interference compensation coefficient; Dynamically adjust filter parameters and compensation coefficients.

6. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 5 is characterized in that: The designing of the digital filter comprises: Determine the filter's cutoff frequency; Determines the order of the filter.

7. The method for extracting high-precision voltage and current characteristic quantities and compensating for an anti-interference sampling circuit according to claim 5 or 6, characterized in that: The designed digital filter is accelerated by FPGA or DSP hardware.

8. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 1 is characterized in that: The metal shield at least covers the ADC module, the metal shield adopts multi-point grounding, and the absorbing material is arranged inside the metal shield.

9. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 1 is characterized in that: The RMS value of voltage and current is calculated by the RMS algorithm using a sliding window method, including: Divide the sampled data into windows of fixed length; The window is updated and the effective value is recalculated each time new data is received.

10. The high-precision voltage and current feature extraction and anti-interference sampling circuit compensation method according to claim 9, characterized in that: The calculation of the effective value of voltage and current by the root mean square algorithm using the sliding window method also includes: The window length is dynamically adjusted according to the signal fluctuation characteristics.

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