An anti-interference method and system for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy.

Abstract

The application discloses a power quality monitoring terminal anti-interference method and system based on adaptive filtering and multi-dimensional shielding cooperation, and relates to the technical field of power quality monitoring.The method constructs a three-level anti-interference system of "hardware shielding-signal filtering-data verification", dynamically suppresses electromagnetic interference, harmonic interference and random noise in combination with an adaptive filtering algorithm, and blocks an interference conduction path in cooperation with a multi-dimensional shielding structure, so that accurate suppression and effective isolation of interference signals are realized.The application solves the problems of weak anti-interference ability, poor adaptability and distorted monitoring data of existing monitoring terminals, significantly improves the accuracy and stability of power quality parameter monitoring in a complex electromagnetic environment, and is suitable for power quality monitoring terminals in multiple scenes such as industrial plants and smart grids.

Description

Technical Field

[0001] This invention belongs to the field of power quality monitoring technology and relates to an anti-interference method for power quality monitoring terminals. Background Technology

[0002] Power quality monitoring terminals are used to monitor parameters such as voltage deviation, harmonic content, and frequency fluctuations in real time. However, these terminals are susceptible to external interference in complex electromagnetic environments, leading to distorted monitoring data and affecting the assessment of the power grid's operational status. Existing anti-interference methods are mainly divided into two categories: hardware shielding and software filtering. Hardware shielding methods often employ a single shielding structure, which is insufficient to completely block multiple types of interference paths, including electromagnetic interference and conducted interference. Software filtering methods often use fixed-coefficient filtering algorithms, which cannot adapt to dynamically changing interference signals in the power grid, resulting in poor anti-interference targeting. Therefore, existing technologies suffer from limitations such as limited anti-interference types, poor adaptability, and low monitoring accuracy. There is an urgent need for an anti-interference method that can coordinate hardware shielding and adaptive software filtering to achieve precise suppression of multiple types of interference. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide an anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy. Through the synergistic effect of hardware shielding and software filtering, the anti-interference capability of the monitoring terminal is comprehensively improved.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: an anti-interference method for a power quality monitoring terminal based on adaptive filtering and multi-dimensional shielding synergy, wherein the multi-dimensional shielding includes module-level shielding, line-level shielding, and interface-level shielding, and shields the signal acquisition module, data processing module, signal transmission line, and signal input / output interface, the steps of which are:

[0005] (1) Collect the raw electrical energy signal, including the three-phase voltage and three-phase current signals collected by the voltage sensor and the current sensor. The raw electrical energy signal is transmitted to the signal conditioning circuit through the signal transmission line and converted into a standard signal that can be stably identified by the weak current side AD converter.

[0006] (2) Interference identification: The frequency and amplitude of harmonic interference and electromagnetic interference are identified by performing spectrum analysis on the original electrical signal through fast Fourier transform.

[0007] (3) Adaptive filtering processing, 31) Receive the conditioned signal, which includes the useful signal and the interference signal; 32) Generate the desired signal. The desired signal is extracted using fundamental frequency synchronization; 33) Calculate the output of the adaptive filter. In the formula: y(n) is the output value of the adaptive filter. for The coefficient vector of the first-order filter; for 34) Calculate the filter error signal, where T is the transpose of the input signal delay vector. In the formula: e(n) is the filtering error. 35) Calculate the dynamic forgetting factor, (The desired signal is given.) In the formula: , ; , fi(n-1) are the current dominant interference frequency and the previous dominant interference frequency, respectively; The attenuation coefficient is... 36) Calculate the adaptive step size The adaptive step size is a two-factor adaptive step size based on error energy and interference frequency. In the formula: As the reference step size, , for The largest eigenvalue; For the most recent The moving average energy of each error ; Useful signal 37) Update the variance of the covariance matrix, where f0 is the fundamental frequency; 38) Update the filter coefficients. In the diagram, w(n+1) represents the coefficient vector of the next L-order filter. For adaptive step size; 39) Output the filtered useful signal; Repeat steps 31)-39) until the filtering error signal is obtained. When the mean square value is less than the set threshold, adaptive filtering is completed;

[0008] (4) Collaborative verification: Combining the interference isolation effect of hardware shielding with the signal processing results of software filtering, residual interference is further eliminated through data consistency verification algorithm.

[0009] Furthermore, in 37) updating the inverse of the covariance matrix, In the formula: δ is the prior accuracy matrix; μ(n-1) is the adaptive step size of the previous step; P(n-1) is the approximate value of the inverse of the covariance matrix of the previous step; and x(n-1) is the L-dimensional input signal delay vector of the previous step.

[0010] Preferred forgetting factor , , The closer the value is to 1, the greater the weight of historical data, and the smoother the filtering. The closer to 0, the smaller the weight of historical data, and the more aggressive the update of the filter coefficients.

[0011] Furthermore, the system acquires grid load change information through the communication module. When the load change rate exceeds 10%, it automatically adjusts the coefficients of the adaptive filtering model, the adaptive step size, and the forgetting factor. At the same time, it optimizes the grounding parameters of the multi-dimensional shielding structure and adapts to anti-interference strategies in real time.

[0012] An anti-interference system for a power quality monitoring terminal based on adaptive filtering and multi-dimensional shielding is disclosed. The multi-dimensional shielding includes module-level shielding, line-level shielding, and interface-level shielding to shield the signal acquisition module, data processing module, signal transmission line, and signal input / output interface. The system comprises the following components:

[0013] (1) The power raw signal acquisition unit includes a voltage sensor and a current sensor, used to acquire the three-phase voltage and three-phase current signals of the power grid;

[0014] (2) Interference identification unit, used to identify the frequency and amplitude of harmonic interference and electromagnetic interference;

[0015] (3) Adaptive filtering processing unit, used to adjust the filtering parameters in real time according to the dynamic changes of the power signal, so as to achieve accurate suppression of harmonic interference and pulse interference;

[0016] (4) Collaborative verification unit, used to compare the interference isolation effect of hardware shielding with the signal processing result of software filtering, and further eliminate residual interference through data consistency verification algorithm.

[0017] Furthermore, it includes an adaptive adjustment and optimization unit, which adjusts the adaptive step size and forgetting factor according to the load change rate, while optimizing the grounding parameters of the multi-dimensional shielding structure and adapting to the anti-interference strategy in real time.

[0018] Preferably, the module-level shielding layer uses an aluminum alloy electromagnetic shielding cover, the line-level shielding layer uses a twisted pair shielded cable, the shielding layer uses a tinned copper wire braided structure, and the interface-level shielding layer uses a shielded aviation plug.

[0019] Preferably, the adaptive filtering processing unit includes:

[0020] The signal input module is used to receive the conditioned 0-5V mixed signal x(n) and perform synchronous sampling and buffering.

[0021] The desired signal generation module is used to track the fundamental phase based on the PLL phase-locked loop and generate a desired signal d(n) that is in phase and frequency with s(n).

[0022] The spectrum analysis module is used to calculate the spectrum of x(n) in real time and extract the dominant interference frequency f. i (n) and frequency change Δf i (n);

[0023] The error calculation module is used to calculate the filtering error e(n) and to calculate the moving average energy E of the error. e (n);

[0024] Adaptive parameter calculation module, used for calculation based on Δf i (n) Calculate the forgetting factor λ(n), based on E e (n) and f i (n) Calculate the step size μ(n);

[0025] The filter coefficient update module is used to update the filter coefficients w(n) based on λ(n), μ(n), x(n), and e(n);

[0026] The filter output module is used to calculate the filter output y(n) based on the updated w(n) and the input vector x(n).

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] (1) Construct a three-level anti-interference system of "hardware shielding-signal filtering-data verification" to coordinate the interference isolation of hardware shielding and the precise suppression of software filtering, comprehensively cover multiple types of interference such as spatial electromagnetic interference, conducted interference, and harmonic interference, and have a wider range of anti-interference capabilities; solve the problems of weak anti-interference capability, poor adaptability and distorted monitoring data of existing monitoring terminals, and significantly improve the accuracy and stability of power quality parameter monitoring in complex electromagnetic environments;

[0029] (2) An improved adaptive filtering model is adopted, and a forgetting factor is introduced to dynamically adjust the filtering coefficients to adapt to the dynamic interference signals in the power grid. Compared with the fixed coefficient filtering algorithm, the anti-interference adaptability is improved by more than 40%.

[0030] (3) The multi-dimensional shielding structure adopts a three-level shielding design with a shielding effectiveness of ≥80dB and a grounding resistance of ≤4Ω, effectively blocking the interference conduction path. Combined with the data consistency verification algorithm, residual interference is further eliminated, and the accuracy of monitoring data is improved to over 99.5%.

[0031] (5) The anti-interference strategy can be adaptively adjusted according to the power grid operation status. It is applicable to multiple scenarios such as industrial plants, smart grids, and new energy power plants. It has a wide range of applications and strong practicality. Attached Figure Description

[0032] Figure 1 This describes the modules and workflow of the adaptive filtering processing unit of the present invention. Detailed Implementation

[0033] An anti-interference system for a power quality monitoring terminal based on adaptive filtering and multi-dimensional shielding is disclosed. The multi-dimensional shielding includes module-level shielding, line-level shielding, and interface-level shielding to shield the signal acquisition module, data processing module, signal transmission line, and signal input / output interface. The system comprises the following components:

[0034] (1) The power raw signal acquisition unit includes a voltage sensor and a current sensor, used to acquire the three-phase voltage and three-phase current signals of the power grid;

[0035] (2) Interference identification unit, used to identify the frequency and amplitude of harmonic interference and electromagnetic interference;

[0036] (3) Adaptive filtering processing unit, used to adjust the filtering parameters in real time according to the dynamic changes of the power signal, so as to achieve accurate suppression of harmonic interference and pulse interference;

[0037] (4) Collaborative verification unit, used to compare the interference isolation effect of hardware shielding with the signal processing result of software filtering, and further eliminate residual interference through data consistency verification algorithm.

[0038] (5) Adaptive adjustment and optimization unit, used to adjust the adaptive step size and forgetting factor according to the load change rate, and optimize the grounding parameters of the multi-dimensional shielding structure to adapt to the anti-interference strategy in real time.

[0039] The adaptive filtering processing unit includes:

[0040] The signal input module is used to receive the conditioned 0-5V mixed signal x(n) and perform synchronous sampling and buffering.

[0041] The desired signal generation module is used to track the fundamental phase based on the PLL phase-locked loop and generate a desired signal d(n) that is in phase and frequency with s(n).

[0042] The spectrum analysis module is used to calculate the spectrum of x(n) in real time and extract the dominant interference frequency f. i (n) and frequency change Δf i (n);

[0043] The error calculation module is used to calculate the filtering error e(n) and to calculate the moving average energy E of the error. e (n);

[0044] Adaptive parameter calculation module, used for calculation based on Δf i (n) Calculate the forgetting factor λ(n), based on E e (n) and f i (n) Calculate the step size μ(n);

[0045] The filter coefficient update module is used to update the filter coefficients w(n) based on λ(n), μ(n), x(n), and e(n);

[0046] The filter output module is used to calculate the filter output y(n) based on the updated w(n) and the input vector x(n).

[0047] An anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding collaboration includes five steps: construction of multi-dimensional shielding hardware structure, acquisition of raw power signals, adaptive filtering processing, multi-dimensional shielding collaboration verification, and adaptive adjustment and optimization.

[0048] Step 1: Construction of Multi-Dimensional Shielding Hardware Structure for Power Quality Monitoring Terminal

[0049] To address the complex electromagnetic environment of new power systems, a collaborative design of a three-layer shielding structure at the module, line, and interface levels is employed.

[0050] Module-level shielding: The signal acquisition module containing voltage and current sensors and the data processing module containing FPGA chip and MCU are respectively encapsulated in aluminum alloy electromagnetic shielding cover. Conductive foam is pasted around the inner wall of the aluminum alloy electromagnetic shielding cover to fill the gap between the module and the aluminum alloy electromagnetic shielding cover, ensuring shielding effectiveness ≥80dB and effectively blocking spatial electromagnetic interference.

[0051] Line-level shielding: The signal transmission line uses twisted-pair shielded cable to transmit the raw electrical signals collected by voltage and current sensors. The cable shielding layer uses a tinned copper wire braided structure with a shielding coverage of ≥90%. Both ends of the twisted-pair shielded cable are grounded with a grounding resistance of ≤4Ω to ensure that the line-level conducted interference suppression is ≥40dB.

[0052] Interface-level shielding: The signal input and output interfaces adopt shielded aviation plugs. The shielded aviation plugs are equipped with grounding springs and signal pin isolation structures to ensure that the interface-level interference coupling attenuation is ≥30dB.

[0053] Step 2: Acquisition of raw electrical signals

[0054] Three-phase voltage and current signals from the power grid are acquired by voltage and current sensors with modular shielding layers. The signals are transmitted to the signal conditioning circuit via signal transmission lines. The three-phase voltage and current signals are processed by anti-interference filtering units, programmable amplification units, and level conversion units with modular shielding layers. The high-amplitude and strong interference signals on the high-voltage side are converted into standard signals that can be stably identified by the AD converter on the low-voltage side, i.e., 0~5V. This provides a reliable data source for subsequent analysis of power quality parameters such as harmonics, flicker, voltage sag, voltage dip, and three-phase imbalance.

[0055] Step 3: Adaptive Filtering

[0056] An adaptive filtering model based on the Least Mean Square (LMS) algorithm is adopted, which can adjust the filtering parameters in real time according to the dynamic changes of the power signal, and achieve precise suppression of complex interferences such as harmonic interference and pulse interference.

[0057] Interference identification unit: Performs spectrum analysis on the original sampled signal through fast Fourier transform to identify the frequency and amplitude of harmonic interference and electromagnetic interference, and stores the extracted harmonic and electromagnetic interference features in the database to support the "frequency matching" and dynamic adjustment of suppression intensity of the subsequent coefficient adjustment unit.

[0058] Coefficient adjustment unit: Based on the minimum mean square error criterion, a forgetting factor is introduced. Dynamically adjust the weights of historical data, combined with adaptive step size The filter coefficient update rate is optimized to achieve rapid convergence of the filter coefficients. The suppression strength is dynamically adjusted for interference signals of different frequencies. The specific implementation steps are as follows:

[0059] (1) Signal input

[0060] Receive the conditioned 0-5V mixed signal, which contains both useful and interference signals, expressed by the formula:

[0061]

[0062] in: It is a discrete time series (n=1,2,...,N, where N is the number of sampling points); The useful signal is the fundamental voltage and current of the power grid, with a frequency of 50Hz and an amplitude of 0~5V; For deterministic interference, including harmonic interference Interference such as power frequency harmonics, The fundamental frequency, The sampling period; It is random interference, that is, high-frequency noise generated by electromagnetic radiation, which follows a zero-mean Gaussian distribution.

[0063] In the above formula: No. The amplitude of harmonic interference signal at each sampling time; This represents the total number of harmonics. For the first The amplitude of the subharmonic; For the first The initial phase of the subharmonic.

[0064] (2) Generate the desired signal

[0065] Expected signal The signal is extracted synchronously with the fundamental frequency, and the fundamental frequency phase is tracked and generated by a phase-locked loop (PLL) to ensure synchronization with the fundamental frequency. Same frequency and phase.

[0066] (3) Calculate the output of the adaptive filter

[0067]

[0068] In the formula: y(n) is the output value of the adaptive filter. for The coefficient vector of the first-order filter; for The input signal delay vector is T, which is the transpose.

[0069]

[0070]

[0071] (4) Calculate the error signal

[0072] The filtering error is defined as "the difference between the desired signal and the filter output", where the desired signal is... The signal is extracted using fundamental frequency synchronization.

[0073]

[0074] In the formula: e(n) is the filtering error, The desired signal is obtained by extracting the fundamental component of the electrical energy signal.

[0075] (5) Calculate the forgetting factor

[0076] This model incorporates an exponential forgetting factor. ( A time-varying cost function is constructed, assigning higher weights to recent data and lower weights to older data, thereby enabling the tracking of time-varying signals.

[0077]

[0078] in, Let cost function be The closer the value is to 1, the greater the weight of historical data, and the smoother the filtering. The closer to 0, the smaller the weight of historical data, and the more aggressive the update of the filter coefficients.

[0079] For cost function By taking the derivative and setting it to zero, the optimal update direction for the filter coefficients can be obtained. Expanded to:

[0080]

[0081] The formula for updating the filter coefficients with the forgetting factor is derived recursively:

[0082]

[0083] In the formula: w(n+1) is the coefficient vector of the next L-order filter. For adaptive step size; It is an approximation of the inverse of the covariance matrix, which is updated recursively through the forgetting factor.

[0084] Forgetting factor The adjustment is adaptive based on the time-varying degree of the interference spectrum, as shown in the following formula:

[0085]

[0086] In the formula: , ; , fi(n-1) are the current dominant interference frequency and the previous dominant interference frequency, respectively; The attenuation coefficient is... .

[0087] When the interference spectrum is stable, i.e. , The filter coefficients are updated smoothly, resulting in small steady-state errors; when the interference spectrum changes abruptly, i.e. , The filter coefficients are updated rapidly to track new interference characteristics.

[0088] (6) Calculate the adaptive step size

[0089] Adaptive step size The choice of μ(n) directly affects the convergence speed and steady-state error: too large a value leads to an increased steady-state error, while too small a value results in slow convergence. This model design is based on a two-factor adaptive step size derived from error energy and disturbance frequency, as shown in the following formula:

[0090]

[0091] In the formula: As the reference step size, , for The largest eigenvalue; For the most recent The moving average energy of each error ; Useful signal The variance; This is the current dominant interference frequency.

[0092] The step size adjustment logic is as follows: when the disturbance changes abruptly, the step size... Increasing the step size accelerates the convergence of the coefficients; as the interference frequency increases, the step size... Increase the step size to enhance the response speed for suppressing high-frequency interference; when the signal is stable, increase the step size. Reduce, thereby lowering the steady-state error.

[0093] (7) Update the inverse of the covariance matrix

[0094]

[0095] In the formula: δ is the prior accuracy matrix, μ(n-1) is the adaptive step size of the previous step, P(n-1) is the approximate value of the inverse of the covariance matrix of the previous step, and x(n-1) is the L-dimensional input signal delay vector of the previous step.

[0096] (8) Update the filter coefficients

[0097]

[0098] In the formula: w(n+1) is the coefficient vector of the next L-order filter. It is an approximation of the inverse of the covariance matrix.

[0099] (9) Output the filtered useful signal

[0100]

[0101] (10) Repeat steps (1)-(9) until the error signal is received. If the mean square value is less than the set threshold, adaptive filtering is completed.

[0102] Signal reconstruction unit: Reconstructs the filtered signal in the time domain and corrects the signal attenuation during the filtering process through an amplitude compensation algorithm to ensure that the peak and effective values ​​of voltage and current signals deviate from the actual values ​​by ≤3%.

[0103] Step 4: Multi-dimensional shielding and collaborative verification

[0104] By combining the interference isolation effect of hardware shielding with the signal processing results of software filtering, residual interference is further eliminated through a data consistency verification algorithm.

[0105] Compare the peak value, RMS value, and phase information of the signals before and after filtering, and set the verification thresholds: peak value deviation ≤ 5%, RMS value deviation ≤ 3%, and phase deviation ≤ 2°.

[0106] When the signal parameter deviation exceeds the threshold, residual interference is detected, and secondary filtering is initiated. The filter coefficient step size is adjusted to 0.08, and the grounding parameters of the shielding structure are optimized, reducing the grounding resistance to ≤3Ω, until the signal parameters meet the verification requirements. Power quality parameters are calculated for the verified signal to obtain monitoring data such as voltage deviation, harmonic distortion rate, and frequency fluctuation.

[0107] Step 5: Adaptive Adjustment and Optimization

[0108] The system monitors the power grid's operating status in real time and acquires information on power grid load changes through a communication module. When the load change rate exceeds 10%, it automatically adjusts the coefficients of the adaptive filtering model, the adaptive step size, and the forgetting factor. At the same time, it optimizes the grounding parameters of the multi-dimensional shielding structure to achieve real-time adaptation of anti-interference strategies and ensures good anti-interference performance under different operating conditions.

Claims

1. An anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding, wherein the multi-dimensional shielding includes module-level shielding, line-level shielding, and interface-level shielding, shielding the signal acquisition module, data processing module, signal transmission line, and signal input / output interface, characterized in that: (1) Collect the raw electrical energy signal, including the three-phase voltage and three-phase current signals collected by the voltage sensor and the current sensor. The raw electrical energy signal is transmitted to the signal conditioning circuit through the signal transmission line and converted into a standard signal that can be stably identified by the weak current side AD converter. (2) Interference identification: The frequency and amplitude of harmonic interference and electromagnetic interference are identified by performing spectrum analysis on the original electrical signal through fast Fourier transform. (3) Adaptive filtering processing, 31) Receive the conditioned signal, which includes the useful signal and the interference signal; 32) Generate the desired signal. The desired signal is extracted using fundamental frequency synchronization. 33) Calculate the output of the adaptive filter. In the formula: y(n) is the output value of the adaptive filter. for The coefficient vector of the first-order filter; for The input signal delay vector is T, which is the transpose. 34) Calculate the filtering error signal. In the formula: e(n) is the filtering error. 35) Calculate the dynamic forgetting factor, (The desired signal is given.) In the formula: , ; , fi(n-1) are the current dominant interference frequency and the previous dominant interference frequency, respectively; The attenuation coefficient is... 36) Calculate the adaptive step size The adaptive step size is a two-factor adaptive step size based on error energy and interference frequency. In the formula: As the reference step size, , for The largest eigenvalue; For the most recent The moving average energy of each error ; Useful signal 37) Update the inverse of the covariance matrix; where f0 is the fundamental frequency. 38) Update the filter coefficients. In the diagram, w(n+1) represents the coefficient vector of the next L-order filter. For adaptive step size; 39) Output the filtered useful signal; Repeat steps 31)-39) until the filtering error signal is obtained. When the mean square value is less than the set threshold, adaptive filtering is completed; (4) Collaborative verification: Combining the interference isolation effect of hardware shielding with the signal processing results of software filtering, residual interference is further eliminated through data consistency verification algorithm.

2. The anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 1, characterized in that: In 37), updating the inverse of the covariance matrix, In the formula: δ is the prior accuracy matrix; μ(n-1) is the adaptive step size of the previous step; P(n-1) is the approximate value of the inverse of the covariance matrix of the previous step; and x(n-1) is the L-dimensional input signal delay vector of the previous step.

3. The anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 1, characterized in that: Forgetting factor , , The closer the value is to 1, the greater the weight of historical data, and the smoother the filtering. The closer to 0, the smaller the weight of historical data, and the more aggressive the update of the filter coefficients.

4. The anti-interference method for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 1, characterized in that: The system acquires grid load change information through the communication module. When the load change rate exceeds 10%, it automatically adjusts the coefficients of the adaptive filtering model, the adaptive step size and forgetting factor, and optimizes the grounding parameters of the multi-dimensional shielding structure to adapt to anti-interference strategies in real time.

5. An anti-interference system for a power quality monitoring terminal based on adaptive filtering and multi-dimensional shielding, wherein the multi-dimensional shielding includes a module-level shielding layer, a line-level shielding layer, and an interface-level shielding layer, shielding the signal acquisition module, data processing module, signal transmission line, and signal input / output interface, characterized in that: It contains the following components: (1) The power raw signal acquisition unit includes a voltage sensor and a current sensor, used to acquire the three-phase voltage and three-phase current signals of the power grid; (2) Interference identification unit, used to identify the frequency and amplitude of harmonic interference and electromagnetic interference; (3) Adaptive filtering processing unit, used to adjust the filtering parameters in real time according to the dynamic changes of the power signal, so as to achieve accurate suppression of harmonic interference and pulse interference; (4) Collaborative verification unit, used to compare the interference isolation effect of hardware shielding with the signal processing result of software filtering, and further eliminate residual interference through data consistency verification algorithm.

6. The anti-interference system for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 5, characterized in that: It contains an adaptive adjustment and optimization unit, which adjusts the adaptive step size and forgetting factor according to the load change rate, while optimizing the grounding parameters of the multi-dimensional shielding structure and adapting to the anti-interference strategy in real time.

7. The anti-interference system for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 5, characterized in that: The module-level shielding layer uses an aluminum alloy electromagnetic shielding cover, the line-level shielding layer uses twisted-pair shielded cable, the shielding layer uses a tinned copper wire braided structure, and the interface-level shielding layer uses a shielded aviation plug.

8. The anti-interference system for power quality monitoring terminals based on adaptive filtering and multi-dimensional shielding synergy as described in claim 5, characterized in that: The adaptive filtering processing unit includes: The signal input module is used to receive the conditioned 0-5V mixed signal x(n) and perform synchronous sampling and buffering. The desired signal generation module is used to track the fundamental phase based on the PLL phase-locked loop and generate a desired signal d(n) that is in phase and frequency with s(n). The spectrum analysis module is used to calculate the spectrum of x(n) in real time and extract the dominant interference frequency f. i (n) and frequency change Δf i (n); The error calculation module is used to calculate the filtering error e(n) and to calculate the moving average energy E of the error. e (n); Adaptive parameter calculation module, used for calculation based on Δf i (n) Calculate the forgetting factor λ(n), based on E e (n) and f i (n) Calculate the step size μ(n); The filter coefficient update module is used to update the filter coefficients w(n) based on λ(n), μ(n), x(n), and e(n); The filter output module is used to calculate the filter output y(n) based on the updated w(n) and the input vector x(n).

Images