Method and system for harmonic current suppression of virtual synchronous generator grid-connected current

Abstract

The application discloses a virtual synchronous generator grid-connected current harmonic suppression method and system, relates to the technical field of power systems, and comprises the following steps: S1, through the grid-connected current signal of a wind farm in a power grid, an analog-to-digital converter is used to digitally sample intermittent high-frequency disturbance to obtain an original digital current sequence; S2, according to the original digital current sequence, a fast Fourier transform algorithm is used to analyze wind load frequency components to determine the amplitude and phase characteristics of high-frequency harmonic components; and S3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold, an intermittent disturbance harmonic sequence is extracted from the frequency components to obtain a harmonic subset to be suppressed. The virtual synchronous generator grid-connected current harmonic suppression method and system significantly improve the stability of a remote wind farm power grid, realize continuous monitoring and real-time suppression of intermittent disturbance, and guarantee the efficiency and reliability of power grid operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a method and system for suppressing grid-connected current harmonics of a virtual synchronous generator. BACKGROUND

[0002] As a fundamental infrastructure of modern society, the stable operation of power systems is crucial for economic development and quality of life. With the rapid development of new energy generation and smart grids, the stability of grid-connected current in power systems has become a core requirement. Virtual synchronous generators, as an emerging technology, can simulate the dynamic characteristics of traditional generators, providing stability and inertia support for the grid. However, the presence of current harmonics can interfere with the normal operation of the grid, increase equipment wear and tear, and even cause system failures. Therefore, researching how to effectively suppress harmonics and ensure the purity of grid-connected current has become an important issue in the field of power systems.

[0003] Traditional harmonic suppression methods mainly rely on analog control circuits, using hardware filters or fixed parameter controllers to reduce harmonic effects. These methods have obvious limitations when faced with complex and variable grid environments. For example, analog control systems are difficult to adapt to dynamic changes in grid loads, especially under the influence of nonlinear loads such as wind power and photovoltaic power generation. The frequency and amplitude of harmonics will fluctuate frequently, and traditional methods cannot adjust parameters in real time, resulting in poor suppression effect. In addition, the hardware design of analog circuits is complex, with high maintenance costs and no support for remote monitoring and dynamic optimization, which is particularly inadequate in the distributed management requirements of modern smart grids.

[0004] In the context of digital transformation, converting the harmonic suppression control system from analog to digital signal processing mode has become a key research direction. However, this transition faces significant technical difficulties. The first problem is to achieve real-time digital sampling and analysis of grid-connected current harmonics. Harmonic signals in the grid have high frequency and transient characteristics, and traditional sampling methods are prone to distortion in high-frequency environments, making it difficult to capture the dynamic changes of harmonics. For example, in a wind power grid-connected scenario, when wind speed suddenly changes, causing power generation to fluctuate, the frequency and amplitude of harmonics will change rapidly within a short period of time. Existing sampling techniques may not be able to accurately identify harmonic components due to insufficient processing speed.

[0005] The existence of this problem directly affects the subsequent harmonic suppression effect. Accurate harmonic identification is the premise of realizing directional suppression, and directional suppression requires the control system to dynamically adjust the suppression parameters according to the real-time analysis results. If there is a deviation in the sampling and analysis link, the suppression parameters will not accurately match the harmonic characteristics, resulting in reduced or even ineffective suppression effect. For example, in a distributed photovoltaic power station, if the control system cannot quickly identify the specific high-order harmonics introduced by the inverter, it may cause current waveform distortion, thereby affecting the stability of the power grid. In addition, the implementation of remote monitoring and parameter adjustment functions further exacerbates this challenge. Remote operation requires the system to have efficient data transmission and real-time performance, while the current digital control system still has bottlenecks in data processing speed and network delay. SUMMARY

[0006] The purpose of the present application is to provide a virtual synchronous generator grid-connected current harmonic suppression method and system, which can realize high-precision real-time sampling and analysis of harmonic signals in complex and variable power grid environments, and perform dynamic directional suppression based on this, while supporting remote monitoring and parameter adjustment.

[0007] To achieve the above purpose, the present application provides the following technical solution: a virtual synchronous generator grid-connected current harmonic suppression method, comprising S1, using the grid-connected current signal of a wind farm in the power grid, using an analog-to-digital converter to digitally sample intermittent high-frequency disturbances to obtain an original digital current sequence; S2, using the fast Fourier transform algorithm to analyze the wind frequency components according to the original digital current sequence, and determining the amplitude and phase characteristics of the high-frequency harmonic components; S3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold, extracting the intermittent disturbance harmonic sequence from the frequency components to obtain a harmonic subset to be suppressed; S4, using the adaptive filtering algorithm to dynamically adjust the filtering parameters through the harmonic subset to be suppressed, and generating a compensation current sequence; S5, obtaining phase matching adjustment and amplitude scaling parameters from the compensation current sequence, judging the matching degree of the compensation current sequence and the original digital current sequence, if the matching degree is higher than the threshold, fusing the compensation current sequence into the main control loop through signal superposition operation and noise suppression filtering to obtain an optimized current signal; S6, using a digital signal processor to calculate the suppression instruction in real time according to the optimized current signal, and simultaneously performing loop feedback integration and stability verification check to generate a directional suppression pulse sequence, using the directional suppression pulse sequence to obtain network transmission data packets, performing delay threshold judgment and network bandwidth evaluation, if the delay is lower than the threshold, using data packet encryption transmission and transmission protocol optimization to a remote server to obtain a remote adjustment parameter set.

[0008] Preferably, the S1 comprises: digitizing and sampling the wind farm grid-connected current signal through an analog-digital converter to generate an original digital current sequence; performing frequency domain analysis on the original digital current sequence by using a fast Fourier transform algorithm to obtain frequency components of the current signal; if there is an abnormal high frequency component higher than a preset threshold in the frequency components, performing filter processing on the original digital current sequence through a band-pass filter to obtain a filtered current sequence; extracting time-frequency features by using a short-time Fourier transform algorithm according to the filtered current sequence to generate a time-frequency distribution of the current signal; if the duration of intermittent high-frequency disturbance detected in the time-frequency distribution exceeds a preset threshold, decomposing the filtered current sequence through a wavelet transform algorithm to obtain a high-frequency disturbance component; calculating energy distribution features of the high-frequency disturbance component to determine disturbance intensity and occurrence position; and analyzing the disturbance intensity and occurrence position through a preset classification model to determine the grid operating state.

[0009] Preferably, the S2 comprises: performing frequency domain analysis on the original digital current sequence by using a fast Fourier transform algorithm to obtain amplitude and phase features of high-frequency harmonic components; processing the original digital current sequence through a band-pass filter according to the amplitude and phase features of the high-frequency harmonic components to obtain a filtered current sequence; performing time-frequency analysis on the filtered current sequence by using a short-time Fourier transform algorithm to obtain time-frequency distribution features; if the energy of the frequency component of the high-frequency disturbance is concentrated in the time-frequency distribution features, decomposing the filtered current sequence through a wavelet transform algorithm to obtain a high-frequency component sequence; calculating time energy distribution according to the high-frequency component sequence to determine disturbance time features; if the duration of the disturbance time features exceeds a preset threshold, analyzing the high-frequency component sequence through a preset classification model to determine the grid operating state; generating state evaluation data according to the grid operating state to determine the wind farm grid-connected stability.

[0010] Preferably, the S3 comprises: if the amplitude of the high-frequency harmonic component exceeds a preset threshold, performing frequency domain decomposition on the original current sequence through a fast Fourier transform algorithm to obtain a frequency component set; extracting an intermittent disturbance sequence by using a spectral analysis method according to the frequency component set to generate a to-be-suppressed harmonic sequence; processing the to-be-suppressed harmonic sequence through a band-pass filter to obtain a filtered harmonic sequence; if the energy of the filtered harmonic sequence is concentrated in the frequency range of the high-frequency disturbance, performing time-frequency analysis by using a short-time Fourier transform algorithm to obtain time-frequency distribution features; extracting an energy concentrated sequence according to the time-frequency distribution features to generate a high-frequency disturbance sequence; analyzing the high-frequency disturbance sequence through a preset classification model to determine the grid operating state to obtain state evaluation data; and suppressing the high-frequency disturbance sequence by using an adaptive filtering algorithm according to the state evaluation data to generate a stable current sequence.

[0011] Preferably, the S4 comprises generating a compensation current sequence by dynamically adjusting filter parameters using a least mean square adaptive filtering algorithm through a harmonic subset to be suppressed; decomposing a signal using a discrete Fourier transform according to the compensation current sequence to obtain a frequency component set; if there is a component of a high-frequency disturbance frequency range in the frequency component set, extracting a harmonic sequence of the frequency range through a band-pass filter to obtain a filtered harmonic sequence; analyzing a time-frequency distribution using a short-time Fourier transform according to the filtered harmonic sequence to obtain a time-frequency feature sequence; determining a power grid state using a preset classification model through the time-frequency feature sequence to obtain state evaluation data; if the state evaluation data indicates that the power grid state is abnormal, performing secondary processing on the filtered harmonic sequence through the least mean square adaptive filtering algorithm to generate a stable current sequence; and reconstructing a time-domain signal using an inverse Fourier transform according to the stable current sequence to obtain an optimized current sequence.

[0012] Preferably, the S5 comprises extracting phase adjustment parameters and amplitude scaling parameters from the compensation current sequence, decomposing a signal using a fast Fourier transform to obtain phase and amplitude features, and obtaining the phase adjustment parameters and the amplitude scaling parameters; calculating a phase difference and an amplitude ratio of the compensation current sequence and the original digital current sequence according to the phase adjustment parameters and the amplitude scaling parameters, evaluating a matching degree of the two using a cosine similarity algorithm, and obtaining a matching degree value; if the matching degree value is higher than a preset threshold, fusing the compensation current sequence to a main control loop through weighted signal superposition operation to obtain a fused current signal; removing noise components from the fused current signal using a filter to obtain a denoised current signal; calculating time-domain statistical features of the denoised current signal, determining signal stability using a support vector machine classification model, and obtaining a stability evaluation result; if the stability evaluation result indicates that the signal is unstable, performing secondary adjustment on the denoised current signal through the least mean square adaptive filtering algorithm to obtain a stable current signal; and reconstructing a time-domain signal using an inverse fast Fourier transform according to the stable current signal to obtain an optimized current signal.

[0013] Preferably, the S6 comprises obtaining time domain features from the optimized current signal, performing real-time calculation by using a digital signal processor to obtain a suppression instruction sequence; calculating a deviation from the main control loop by using a feedback loop through the suppression instruction sequence to obtain a deviation correction sequence; if the amplitude of the deviation correction sequence exceeds a preset threshold, optimizing the suppression instruction sequence by using a Kalman filtering algorithm to obtain an optimized pulse sequence; calculating statistical properties of the time sequence according to the optimized pulse sequence, judging sequence stability by using a support vector machine classification model to obtain a stability judgment result; extracting unstable sequence fragments through the stability judgment result, performing secondary adjustment by using an adaptive filtering algorithm to obtain a stable pulse sequence; generating a directional suppression pulse sequence according to the stable pulse sequence, performing real-time output by using a digital signal processor to obtain a final pulse sequence; calculating the frequency distribution characteristics of the sequence through the final pulse sequence, verifying frequency consistency by using a fast Fourier transform to obtain a verification pulse sequence; extracting network transmission data packets from the directional pulse sequence, calculating data packet delays by using a timestamp analysis to obtain a delay judgment result; if the delay judgment result is lower than a preset threshold, encrypting the network transmission data packets by using an encryption standard algorithm to obtain encrypted data packets; adjusting the data packet sending order according to the encrypted data packets by using a transmission control protocol optimization strategy to generate an optimized data stream; analyzing network bandwidth occupation by using a sliding window protocol through the optimized data stream to obtain bandwidth allocation parameters; adjusting the data packet transmission rate according to the bandwidth allocation parameters by using a flow control algorithm to generate an adjusted data stream; extracting remote adjustment parameters from the adjusted data stream, generating a compressed parameter set by using a data compression algorithm to obtain a remote adjustment parameter set; verifying the integrity of the parameter set by using a verification algorithm through the remote adjustment parameter set to obtain a verified parameter set.

[0014] Preferably, the S7 further comprises extracting feedback values from the remote adjustment parameter set, updating local filtering parameters by using a proportional-integral-derivative control algorithm combined with an error retransmission mechanism and feedback value extraction processing in response to server response analysis and parameter set download, generating a final suppression output sequence, specifically comprising extracting feedback values from the remote adjustment parameter set, separating effective feedback data by using a data analysis algorithm to obtain a feedback data set; extracting update parameters by using server response analysis according to the feedback data set to generate an update parameter set; if the update parameter set meets a preset threshold, adjusting the feedback data set by using a proportional-integral-derivative control algorithm to obtain an adjusted data set; detecting data integrity by combining an error retransmission mechanism according to the adjusted data set to generate a retransmission correction data set; extracting local filtering parameters from the retransmission correction data set, optimizing parameter smoothness by using a moving average algorithm to obtain smooth filtering parameters; adjusting the suppression output sequence by using the smooth filtering parameters, forming a final output sequence by using a sequence generation algorithm; verifying sequence integrity by using a verification algorithm according to the final output sequence to obtain a verified output sequence.

[0015] Preferably, S8 further comprises judging whether the wind farm grid intermittent disturbance stability index meets the requirements according to the final suppression output sequence, if it meets, the sampling process of the original digital current sequence is recycled to maintain the continuous monitoring state, which specifically comprises extracting the grid stability features from the suppression output sequence, separating the intermittent disturbance data by using the feature extraction algorithm to obtain the disturbance feature set; if the disturbance feature set is within the preset threshold deviation range, the threshold comparison algorithm is used to verify the stability index to generate a verification result set; according to the verification result set, the data is re-collected from the digital current sequence by using the cyclic sampling mechanism to obtain an updated sampling data set; the time sequence features are extracted from the updated sampling data set, the sliding window algorithm is used to analyze the sequence continuity to obtain a continuity analysis result; if the continuity analysis result meets the preset continuity condition, the updated sampling data set and the disturbance feature set are merged by using the data integration algorithm to generate an integrated monitoring data set; according to the integrated monitoring data set, the state monitoring algorithm is used to judge the grid operation state to obtain a state evaluation result; the abnormal fluctuation features are extracted from the state evaluation result, and the abnormal detection algorithm is used to generate an abnormal marker sequence.

[0016] Preferably, the virtual synchronous generator grid-connected current harmonic suppression system is used to implement the steps of the virtual synchronous generator grid-connected current harmonic suppression method, and the system comprises a sampling module, which is used to sample intermittent high-frequency disturbances through a grid-connected current signal of a wind farm in a power grid by using an analog-to-digital converter to obtain an original digital current sequence; a spectrum analysis module, which is used to analyze wind frequency components by using a fast Fourier transform algorithm according to the original digital current sequence to determine amplitude and phase characteristics of high-frequency harmonic components; a disturbance extraction module, which is used to extract an intermittent disturbance harmonic sequence from the frequency components when the amplitude of the high-frequency harmonic components exceeds a preset threshold to obtain a harmonic subset to be suppressed; a filter control module, which is used to dynamically adjust filter parameters by using an adaptive filter algorithm through the harmonic subset to be suppressed to generate a compensation current sequence; a matching calculation module, which is used to obtain phase matching adjustment and amplitude scaling parameters from the compensation current sequence to judge the matching degree of the compensation current sequence and the original digital current sequence; if the matching degree is higher than a threshold, the compensation current sequence is fused into a main control loop through signal superposition operation and noise suppression filtering to obtain an optimized current signal; a digital signal processing module, which is used to calculate an inhibition instruction in real time by using a digital signal processor according to the optimized current signal, and simultaneously performs loop feedback integration and stability verification checking to generate a directional suppression pulse sequence; a remote transmission module, which is used to obtain network transmission data packets, performs delay threshold judgment and network bandwidth evaluation, and if the delay is lower than the threshold, the data packets are transmitted to a remote server by using data packet encryption transmission and transmission protocol optimization to obtain a remote adjustment parameter set; a parameter updating module, which is used to extract a feedback value from the remote adjustment parameter set, and updates local filter parameters by using a proportional-integral-derivative control algorithm in combination with an error retransmission mechanism and feedback value extraction processing, and generates a final suppression output sequence; and a stability monitoring module, which is used to judge whether intermittent disturbance stability indexes of a remote wind farm power grid meet requirements according to the final suppression output sequence, and if the requirements are met, the sampling process of the original digital current sequence is recycled to maintain a continuous monitoring state.

[0017] From the above technical solutions, the present application has the following beneficial effects:

[0018] This invention presents a method and system for suppressing harmonic disturbances in grid-connected current of a virtual synchronous generator, addressing the challenges of real-time suppression of harmonic disturbances caused by wind load frequency variations in remote wind farms and the impact of network transmission delays on stability. The invention digitally samples the current signal using a high-speed analog-to-digital converter (ADC), employs a fast Fourier transform (FFT) algorithm to accurately extract the amplitude and phase characteristics of high-frequency harmonic components, and generates a subset of harmonics to be suppressed when the harmonic amplitude exceeds a threshold. An adaptive filtering algorithm is then used to dynamically adjust parameters and generate a compensating current sequence. The invention optimizes the current signal through signal superposition and noise suppression fusion, and combines a digital signal processor to calculate suppression commands in real time, ensuring loop stability. Simultaneously, data packets are transmitted remotely over a network, with optimized protocols and encrypted transmission. A proportional-integral-derivative (PID) control algorithm and an error retransmission mechanism are used to update filtering parameters, generating the final suppressed output sequence. This invention significantly improves the stability of the power grid in remote wind farms, achieving continuous monitoring and real-time suppression of intermittent disturbances, and ensuring the high efficiency and reliability of grid operation. Attached Figure Description

[0019] Figure 1 This is a flowchart of the virtual synchronous generator grid-connected current harmonic suppression method of the present invention;

[0020] Figure 2 This is a connection diagram of the virtual synchronous generator grid-connected current harmonic suppression system of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1As shown, the present application provides a technical solution: a virtual synchronous generator grid-connected current harmonic suppression method, comprising S1, through the grid-connected current signal of the wind farm in the power grid, using an analog-to-digital converter to digitize and sample intermittent high-frequency disturbances to obtain an original digital current sequence; S2, according to the original digital current sequence, using the fast Fourier transform algorithm to analyze the wind frequency component, and determining the amplitude and phase characteristics of the high-frequency harmonic component; S3, if the amplitude of the high-frequency harmonic component exceeds the preset threshold, extract the intermittent disturbance harmonic sequence from the frequency component to obtain a harmonic subset to be suppressed; S4, through the harmonic subset to be suppressed, using an adaptive filtering algorithm to dynamically adjust the filtering parameters to generate a compensation current sequence; S5, obtain the phase matching adjustment and amplitude scaling parameters from the compensation current sequence, judge the matching degree of the compensation current sequence and the original digital current sequence, if the matching degree is higher than the threshold, through signal superposition operation and noise suppression filtering, fuse the compensation current sequence into the main control loop to obtain the optimized current signal; S6, according to the optimized current signal, using a digital signal processor to calculate the suppression instruction in real time, at the same time, carrying out loop feedback integration and stability verification check, generating a directional suppression pulse sequence, through the directional suppression pulse sequence, obtaining network transmission data packet, carrying out delay threshold judgment and network bandwidth evaluation, if the delay is lower than the threshold, using data packet encryption transmission and transmission protocol optimization to remote server, obtaining remote adjustment parameter set; S7, extract the feedback value from the remote adjustment parameter set, through server response analysis and parameter set download update, using the proportional integral derivative control algorithm combined with the error retransmission mechanism and the feedback value extraction processing to update the local filtering parameters, generating the final suppression output sequence; S8, according to the final suppression output sequence, judge whether the intermittent disturbance stability index of the wind farm power grid meets the requirements, if it meets, cycle back to the sampling process of the original digital current sequence, maintain continuous monitoring state.

[0023] The embodiment realizes dynamic suppression of harmonics by multi-stage detection, analysis and compensation control of intermittent high-frequency harmonic disturbance in grid-connected current of a wind farm. In step S1, the actual current signal is digitally sampled in real time by an analog-to-digital converter to obtain an original current sequence with complete timing characteristics; in step S2, the frequency spectrum characteristics of the current sequence are analyzed by using a fast Fourier transform algorithm to effectively identify the high-frequency harmonic components and accurately extract the amplitude and phase information thereof; in step S3, the intermittent disturbance frequency band to be processed is selected according to threshold judgment logic to extract a harmonic subset to be suppressed; in step S4, the filter parameters are adjusted by using an adaptive filtering algorithm in combination with the subset to improve the response capability of the filter to non-continuous high-frequency components, so as to generate a compensation current for offsetting the disturbance; in step S5, dynamic matching evaluation is performed according to the phase difference and amplitude relationship between the original sequence and the compensation sequence to ensure accurate superposition of the compensation signal, and the signal quality of the current main control loop is optimized through fusion operation; steps S6 and S7 further introduce a digital signal processor to execute suppression instruction generation and feedback stability verification, and at the same time, fine adjustment parameter sets are obtained from a remote server through a network transmission mechanism and are updated locally; finally, in S8, the disturbance stability index is judged according to the updated suppression sequence to determine whether to continue the closed-loop cycle.

[0024] The high-frequency disturbance refers to short-term, intermittent and high-frequency abnormal current fluctuation in grid-connected current of a wind farm caused by factors such as sudden change of wind speed, inverter switching and nonlinear load, which usually has non-periodic and burst characteristics, the frequency is in the range of 500Hz to 30kHz, and the duration is several milliseconds to tens of milliseconds. The high-frequency harmonic component refers to a harmonic signal component with a frequency of an integer multiple (such as 25 times or more) of the fundamental frequency (such as 50Hz) in the high-frequency disturbance, which has stable periodicity and characteristics, and is one of important bases for judging abnormal grid-connected current. In this patent, the current component with a frequency greater than 2kHz and an amplitude exceeding 1%-3% of the fundamental wave amplitude is usually referred to.

[0025] The embodiment realizes accurate identification and real-time compensation of high-frequency harmonics by combining fast Fourier transform and adaptive filtering technology, which significantly improves the power quality and operation stability of a virtual synchronous generator in the process of wind power grid connection. It has high dynamicity and real-time performance, can quickly respond to intermittent disturbances, and effectively suppresses transient current harmonics caused by wind speed fluctuations. The remote parameter tuning mechanism enhances the adaptive ability and remote maintenance capability, and realizes high reliability transmission through data packet delay and bandwidth control. The proportional-integral-derivative control combined with the error retransmission mechanism improves the response accuracy and control stability of the local filter, and ensures the reliability and consistency of long-term operation.

[0026] S1 includes digitizing sampling of the wind farm grid-connected current signal through an analog-to-digital converter to generate an original digital current sequence; frequency components of the current signal are obtained by performing frequency domain analysis on the original digital current sequence using a fast Fourier transform algorithm; if there is an abnormal high-frequency component higher than a preset threshold in the frequency components, the original digital current sequence is filtered through a band-pass filter to obtain a filtered current sequence; time-frequency features are extracted from the filtered current sequence using a short-time Fourier transform algorithm to generate a time-frequency distribution of the current signal; if the duration of intermittent high-frequency disturbance detected in the time-frequency distribution exceeds a preset threshold, the filtered current sequence is decomposed through a wavelet transform algorithm to obtain a high-frequency disturbance component; the energy distribution characteristics of the high-frequency disturbance component are calculated to determine the disturbance intensity and occurrence position; and the grid operating state is determined by analyzing the disturbance intensity and occurrence position through a preset classification model.

[0027] In a possible implementation, the wind farm grid-connected current signal is first sampled through an analog-to-digital converter, the sampling frequency is set to 50000 sample points per second, the sampling accuracy is set to 16 bits, the sampling process lasts for 2 seconds, a total of 100000 equally spaced current data points are obtained, and an original digital current sequence is formed.

[0028] The sequence is input into a fast Fourier transform processing module. The module first performs window function processing on the 100000 sample points, selects a fixed window width of 2048 points for rectangular window segmentation, overlaps 512 points for each segment, and performs spectral decomposition. During the transformation process, each segment of the signal is converted into a frequency component, and its corresponding amplitude is extracted. Then, an amplitude threshold for identifying abnormal high-frequency components is set, which is calculated by the average value of the measured fundamental amplitude on site, and is specifically set to 3% of the average value. For example, if the average amplitude of the fundamental wave is 20 amperes, the amplitude threshold is set to 0.6 amperes, and if the amplitude of a frequency component is greater than the value, it is marked as an abnormal high-frequency component. Next, the identified abnormal frequency value is used as the center frequency to construct a band-pass filter, the filter bandwidth is set to 1000 Hz, and a passband range of 500 Hz is set on both sides of the center frequency to filter the original digital current sequence. The new current sequence obtained after filtering retains the high-frequency information of the specified frequency band.

[0029] After that, the sequence enters the short-time Fourier transform processing module, uses the Hamming window with a window width of 4096 points for sliding processing, the sliding step is 512 points, and the fast Fourier transform is performed on each sliding window to generate the frequency distribution in each time interval, and finally the time-frequency distribution diagram in the whole time interval is synthesized. The energy trajectory of each frequency in the image is detected, and if the energy peak value continuously appears at the same frequency for more than 1500 sampling points, i.e. the duration reaches 30 milliseconds, the disturbance corresponding to the frequency is judged as intermittent high-frequency disturbance. Then call the wavelet transform module, four-layer discrete wavelet decomposition is performed on the above filtered current sequence, and the wavelet function used is the fixed Daubechies fourth-order function. After decomposition, 4 groups of high-frequency coefficient sequences are obtained, and the uppermost layer of high-frequency sequence is selected as the high-frequency disturbance component and enters the subsequent energy analysis step.

[0030] In the energy analysis stage, the high-frequency disturbance component is divided into a time window of 100 points, and the square sum of each group of values is obtained to construct a complete energy distribution sequence. The starting point of the time window corresponding to the maximum energy value is marked as the disturbance occurrence position, and the maximum value is the disturbance intensity. The disturbance intensity value is judged by comparing with the preset grade table, and the energy value thresholds of light, moderate and severe disturbances are set in the grade table, which are 10, 50 and 100 ampere square seconds respectively. If the disturbance energy is within this range, the corresponding label is marked according to its grade. Finally, the disturbance intensity and occurrence position are input as input parameters into the preset classification model, which is based on a polynomial conditional judgment process, and the disturbance intensity grade and disturbance position are compared item by item to see if they are in the sensitive interval. If both the moderate and above intensity and the key time period conditions are met, it is judged that there is a disturbance risk in the power grid at present, otherwise it is judged as normal operation state.

[0031] According to the high-frequency disturbance component after wavelet transform, the calculation of the disturbance energy distribution is completed, and two key parameters of disturbance intensity and disturbance occurrence position are extracted. The disturbance intensity is obtained by squaring and accumulating the high-frequency current values in each time window to obtain the energy value, which is in units of ampere square second. The entire disturbance sequence is divided into several continuous windows, each window contains 100 sampling points, the total energy of each window is calculated, and finally the value corresponding to the window with the maximum energy is selected as the disturbance intensity of the disturbance. The disturbance occurrence position is the time point corresponding to the starting position of the maximum energy window in the entire sampling sequence, which is in units of milliseconds, and the accuracy is every sampling period, that is, 0.02 milliseconds. Subsequently, the two parameters are input into the preset classification model module as input variables. The model is a judgment model based on a fixed decision logic structure, and the model includes several explicit classification rules. Specifically, first, it is judged whether the disturbance intensity is greater than 10 ampere square second, if it is less than or equal to, it is directly judged as a mild disturbance, and the grid operation state is normal; if it is greater than 10 ampere square second, it enters the next step of judgment; second, it is judged whether the disturbance intensity is greater than 100 ampere square second, if it is, it is judged as a severe disturbance; if it is between 10 and 100 ampere square second, it is judged as a moderate disturbance; third, for moderate and severe disturbances, it is further judged whether the disturbance occurrence position is located in the set key operation time period. The key operation time period is pre-configured, and is usually a key control window such as load switching, high wind speed warning, and grid synchronization, for example, between the 500th and 600th milliseconds of the sampling time. If the disturbance occurs in this time period, it is determined that the grid operation state is abnormal, and the suppression mechanism needs to be started; if it is not in this time period, it is determined as a tolerable disturbance, and the grid operation state is to be observed.

[0032] The amplitude threshold of the abnormal high-frequency component is set to 3% of the fundamental amplitude, which is determined based on the actual operation characteristics and spectrum analysis results of the grid-connected current signal of the wind farm. In the normal operation state, the high-frequency part of the current signal is affected by noise, measurement error and control disturbance, and its amplitude will not exceed 1% to 2% of the fundamental amplitude. In order to avoid misidentifying normal noise as abnormal signal, it is necessary to set a threshold value higher than the upper limit of noise but sensitive to the detection of real high-frequency harmonic disturbance. Through statistical analysis of field operation data and a large number of experimental comparisons, it is found that when the amplitude of high-frequency component exceeds 3% of the fundamental amplitude, it is highly related to actual grid disturbances such as wind speed sudden change and load switching. Therefore, the proportion of 3% can effectively balance the risk of false alarm and missed alarm, improve the recognition accuracy and engineering practicability, and is a stable threshold setting verified.

[0033] In the band-pass filter processing link, the bandwidth is fixedly set to 1000 Hz, which is set according to the coverage analysis of the fluctuation range of the high-frequency disturbance frequency. In the process of wind power operation, the high-frequency components caused by factors such as inverter switching frequency fluctuation and wind speed disturbance usually show a concentrated distribution, and the frequency change range is generally within ±500 Hz of the target frequency. Setting a bandwidth of ±500 Hz on both sides of the center frequency can completely cover the main disturbance frequency band, effectively extract the target frequency signal, and at the same time, maximize the suppression of interference signals in other frequency bands. If the bandwidth is set too small, the real disturbance information will be filtered out; if it is set too large, invalid spectral components will be introduced, reducing the accuracy of subsequent analysis. Therefore, the bandwidth value of 1000 Hz is an engineering setting with optimal signal-to-noise ratio by covering the main disturbance.

[0034] In the process of intermittent high-frequency disturbance judgment, a fixed threshold of 30 milliseconds is set for the disturbance duration in the time-frequency distribution. The setting of this threshold is based on the time response characteristics of wind power control logic and the actual duration analysis of disturbance behavior. The fast regulation period of control is usually between 20 to 50 milliseconds, and 30 milliseconds is in the typical time period for responding to sudden disturbances, which can effectively distinguish between short-term noise fluctuations and destructive disturbance events. Through comparative analysis of a large number of time-frequency pattern samples, it is found that disturbances less than 30 milliseconds are mostly non-persistent signals and do not have the meaning of control interference; disturbances exceeding 30 milliseconds are often accompanied by power fluctuations and frequency deviations. Therefore, this time threshold can stably identify disturbance behaviors that have a substantial impact on operation, and has good engineering stability and applicability.

[0035] In the wavelet energy disturbance analysis stage, a fixed disturbance intensity classification threshold is used, which is 10, 50 and 100 ampere square seconds, respectively, to classify the high-frequency disturbance intensity. This setting takes the disturbance energy value as the basis and reflects it as the product of the disturbance duration and amplitude, so as to accurately measure its degree of influence. Through modeling analysis of a large number of historical disturbance events, it is found that when the disturbance energy is less than 10 ampere square seconds, it has almost no substantial impact on the virtual synchronous generator control, so it is defined as a mild disturbance; when the disturbance energy exceeds 50 ampere square seconds, the frequency and voltage will fluctuate significantly, and it is defined as a moderate disturbance; and the disturbance exceeding 100 ampere square seconds is generally accompanied by transient instability of the control link, and emergency control measures need to be taken, so it is defined as a severe disturbance.

[0036] S2 includes frequency domain analysis on the original digital current sequence by using a fast Fourier transform algorithm to obtain amplitude and phase characteristics of high frequency harmonic components; according to the amplitude and phase characteristics of the high frequency harmonic components, the original digital current sequence is processed by a band-pass filter to obtain a filtered current sequence; time-frequency analysis is performed on the filtered current sequence by using a short-time Fourier transform algorithm to obtain time-frequency distribution characteristics; if energy concentration of a specific frequency component is detected in the time-frequency distribution characteristics, the filtered current sequence is decomposed by a wavelet transform algorithm to obtain a high frequency component sequence; according to the high frequency component sequence, time energy distribution is calculated to determine a disturbance time characteristic; if the duration of the disturbance time characteristic exceeds a preset threshold, the high frequency component sequence is analyzed by a preset classification model to determine the grid operation state; according to the grid operation state, state evaluation data is generated to determine the stability of the wind farm grid connection.

[0037] In this embodiment, the wind farm grid connection current signal is first digitized and sampled by an analog-to-digital converter, the sampling frequency is fixed at 50000 Hz, 50000 points are collected per second, the sampling accuracy is 16 bits, the sampling period is 2 seconds each time, 100000 current sampling points are obtained to form an original digital current sequence. The sampling data is sequentially sent to a fast Fourier transform module for frequency domain analysis and processing. During the conversion process, a segmented window function is used for processing, a single window width is set to 2048 points, the adjacent windows overlap by 512 points, the frequency spectrum of each window is calculated, and the amplitude and phase values of all frequency points are extracted. The amplitude is calculated by taking the square root of the sum of the real and imaginary parts, and the unit is ampere, which is used to represent the energy intensity of the frequency component; the phase is obtained by calculating the inverse tangent of the imaginary part and the real part, and the unit is angle, which is used to represent the time offset of the frequency component relative to the fundamental wave. The amplitude threshold of all frequency components in the frequency spectrum is determined, if the amplitude of a certain frequency is greater than 0.6 ampere, it is considered to be an abnormal high frequency harmonic component. The 0.6 ampere threshold is determined by multiplying the fundamental current amplitude of 20 ampere by 3%, and 3% is the minimum identifiable disturbance value verified according to the actual wind power grid current background noise distribution and disturbance difference in engineering, to avoid false positives and improve identification accuracy.

[0038] After identifying the abnormal frequency, the frequency value is extracted as the center frequency of the band-pass filter, the band-pass filter bandwidth is fixed at 1000 Hz, i.e. the passband range is 500 Hz above and below the center frequency, and a second-order Butterworth filter is constructed. The bandwidth is determined according to the frequency drift range of the identified disturbance, and in the disturbance caused by wind speed fluctuation and equipment switching, the high frequency component is offset by no more than 500 Hz, therefore, setting the total bandwidth to 1000 Hz can ensure that the key components are not lost in filtering. The filter takes the original digital current sequence as input, performs differential filtering operation, and outputs the filtered current sequence, only the signals in the specified high frequency range are retained, and the rest of the frequency components are filtered out to form a high frequency disturbance reserved sequence for subsequent time-frequency analysis and processing.

[0039] The filtered current sequence is sent into a short-time Fourier transform module for time-frequency feature extraction. The transform parameters are a window width of 4096 points and a step length of 512 points, i.e., every 4096 sampling points is a window, and the Fourier spectrum operation is performed between every window with a sliding step of 512 points, and finally a complete time-frequency distribution map is spliced in time sequence. The energy trajectory corresponding to each frequency in the time-frequency distribution map is detected, and whether the energy change trajectory forms a concentrated and continuous high-energy area at a certain frequency point is calculated. If the energy of a certain frequency continuously rises and remains in a high amplitude state in adjacent multiple time windows, it is determined that it is a frequency energy concentration feature, and the frequency is determined as a suspected disturbance frequency.

[0040] The filtering result near the frequency is taken as input, a wavelet transform module is started, a Daubechies wavelet basis function is used for four-layer wavelet decomposition, the highest layer high-frequency component is retained, and a high-frequency component sequence is constructed. The high-frequency component sequence is divided into time windows of 100 points per group, and the total number of windows is the total number of points of the filtering sequence divided by 100. In each window, the energy value is calculated by square sum of all data points, and the unit is ampere square second. The starting time and energy value corresponding to each window are recorded to form a complete time-energy distribution map, and the disturbance feature is identified according to the set threshold. The specific judgment rule is: if the energy value in a certain continuous time window is continuously greater than 10 ampere square seconds, and the total length of the continuous window time exceeds 30 milliseconds, it is regarded as a disturbance time feature. The energy threshold of 10 ampere square seconds is determined by statistical data collected during normal operation, which is three times the upper limit of background noise and has sufficient discrimination ability; 30 milliseconds is the critical control response, which is set to ensure that the control module has sufficient time to intervene in processing.

[0041] Once the disturbance section meeting the energy concentration and time duration is identified, the high-frequency component sequence is input into a preset classification model for power grid state identification. The classification model is a fixed rule structure and does not involve a learning mechanism, and is executed in the following logical order: first, it is judged whether the disturbance energy is greater than 10 ampere square seconds, if not, the power grid state is output as normal; if yes, it is judged whether the disturbance occurs in the set key time section, for example, the wind turbine switching window or the grid-connected instant, the time period is between the 1000th millisecond and the 1200th millisecond. If the disturbance meets the conditions of high intensity and occurrence in the key period at the same time, the power grid state is determined as existing operation risk. Then the disturbance intensity value, disturbance start time, disturbance duration, power grid state label and other information are combined into structured state evaluation data, and stored in a local database or uploaded to a remote monitor for dispatching response or device linkage operation.

[0042] The identification of high-frequency harmonic components uses a fixed amplitude threshold of 0.6 amperes. This threshold is derived from 3% of the fundamental current amplitude. According to the actual measurement data of wind farm grid-connected operation, the normal amplitude of the fundamental current is about 20 amperes. Considering background noise, electromagnetic interference, and high-frequency jitter caused by equipment switching, its amplitude usually does not exceed 1% to 2% of the fundamental amplitude. If the threshold is set lower than 3%, it will lead to frequent identification of invalid noise as disturbance, with high false positive rate and poor stability; if it is set too high, for example, more than 5%, it is easy to miss the real high-frequency disturbance with low amplitude but clear frequency structure. Therefore, 0.6 amperes, as the specific value of 3% amplitude ratio, can effectively exclude noise and retain sensitivity to key disturbances, and is the engineering optimal solution obtained through analysis of multiple sets of measured data.

[0043] In the disturbance time feature recognition, a 30-millisecond duration threshold is set. This threshold is based on the response time of virtual synchronous generators and their control to external disturbances. Most wind power controls have a regulation response period of 20 to 50 milliseconds after receiving the disturbance signal, and 30 milliseconds is in the typical middle value range. If the disturbance duration is less than 30 milliseconds, it may be a transient fluctuation that does not require control intervention; if it exceeds 30 milliseconds, it is likely to affect stable operation. Therefore, setting 30 milliseconds as the lower threshold for disturbance time recognition can effectively avoid misidentifying transient fluctuations while ensuring timely disturbance detection and state determination before control intervention.

[0044] In the disturbance intensity analysis, 10 amperes squared seconds is used as the energy judgment threshold. This value is derived from energy analysis and calculation of high-frequency disturbance components in actual wind power grid-connected scenarios, with units of amperes squared seconds, representing the current square integral value in each disturbance window. Energy accumulation analysis of thousands of groups of disturbances under different conditions in the experimental stage found that when the energy value is less than 10 amperes squared seconds, its impact on the grid current curve is not significant, and it is a tolerable disturbance; when the energy is greater than 10 amperes squared seconds, it is often accompanied by current waveform distortion and frequency offset, which needs to be paid attention to. Therefore, 10 amperes squared seconds as the minimum value of disturbance energy recognition can effectively filter non-substantial interference while retaining real destructive disturbance data.

[0045] S3 includes if the amplitude of the high frequency harmonic component exceeds the preset threshold, the original current sequence is decomposed in frequency domain by fast Fourier transform algorithm to obtain a frequency component set; according to the frequency component set, a spectrum analysis method is used to extract an intermittent disturbance sequence to generate a harmonic sequence to be suppressed; the harmonic sequence to be suppressed is processed by a band-pass filter to obtain a filtered harmonic sequence; if the energy of the filtered harmonic sequence is concentrated in the frequency range of the high frequency disturbance, a short-time Fourier transform algorithm is used for time-frequency analysis to obtain time-frequency distribution characteristics; according to the time-frequency distribution characteristics, an energy concentration sequence is extracted to generate a high frequency disturbance sequence; the high frequency disturbance sequence is analyzed by a preset classification model to determine the grid operating state to obtain state evaluation data; according to the state evaluation data, an adaptive filtering algorithm is used to suppress the high frequency disturbance sequence to generate a stable current sequence.

[0046] In the running process, the frequency characteristics of the original digital current sequence are monitored in real time, and if the amplitude of any high frequency harmonic component exceeds 0.6 A, the frequency domain decomposition process is triggered. The 0.6 A here is a fixed threshold, which is determined by multiplying the fundamental current amplitude 20 A by 0.03. The proportion coefficient 3% comes from the distinction between background noise and effective disturbance in actual operation, which ensures that the identified high frequency signal has actual interference significance. After triggering, the original current sequence is divided into multiple 2048-point analysis windows, each window overlaps 512 points, and fast Fourier transform operation is performed to convert time domain data into frequency domain frequency component set, including frequency value, amplitude value and phase value of each frequency. The frequency coverage is 0 to 25000 Hz, the amplitude unit is A, which represents the current intensity at this frequency, and the phase unit is degree, which represents the relative offset of the frequency in time.

[0047] The part of the frequency component set with a frequency higher than 2000 Hz is selected as a high frequency candidate area, and the amplitude of all frequency points in the area is extracted for local peak value extraction and discontinuity detection. A sliding window length of 5 frequency points is used for power spectrum density difference calculation method analysis. In each sliding window, if the amplitude of the current frequency point is higher than the average value of the previous and next frequency points by more than 20%, and the frequency point is not continuous with the adjacent interference frequency, it is considered as an intermittent disturbance frequency point. All frequency points that meet the conditions are combined into an intermittent disturbance sequence, i.e. a harmonic sequence to be suppressed. This sequence retains all frequency components that meet the characteristics of high amplitude, discontinuity and high frequency, providing a clear processing target for subsequent filtering and time-frequency analysis.

[0048] The harmonic sequence to be inhibited is input into a band-pass filtering module, the center frequency is set as the energy peak frequency in the sequence, the bandwidth is 500 Hz up and down, and the total bandwidth is 1000 Hz. The selection of the bandwidth is based on the experimental statistics of the high-frequency disturbance frequency drift range. It is found that the main disturbance is concentrated within 500 Hz of the center frequency. Therefore, setting this fixed range can ensure that the disturbance component is not missed. The filter type is a second-order Butterworth filter. The original current sequence is processed by point-to-point filtering to remove non-target frequency components and generate a filtered harmonic sequence. The local integral energy of the sequence is calculated. The energy of each frequency band is compared with the total energy of the entire sequence. If the energy of the passband frequency band accounts for more than 90%, it is determined that the high-frequency disturbance energy is concentrated, and the next step of analysis is met.

[0049] The filtered harmonic sequence is subjected to short-time Fourier transform processing, the window width is 4096 points, the sliding step is 512 points, and the window function uses a fixed Hamming window. The frequency spectrum of each sliding window is transformed and spliced to construct a time-frequency spectrum. Then, the energy distribution of the part of the spectrum with a frequency higher than 2000 Hz is calculated window by window. The frequency energy in each time window is the square of the current value after accumulation, and the unit is ampere square second, forming a time-energy distribution sequence. The energy threshold is set to 10 ampere square seconds. If the energy value of a certain frequency point in more than three consecutive time windows is greater than the threshold, it is determined that the disturbance energy is concentrated; the threshold comes from the actual grid-connected control tolerance of the wind farm, and a value below this threshold will not require a control response. Through the time index of the energy concentration area, a continuous disturbance segment is extracted to form a high-frequency disturbance sequence as the input for subsequent classification and judgment.

[0050] The high-frequency disturbance sequence is input into a classification model, which is a logical judgment process based on a fixed rule structure. First, it is determined whether the maximum energy value in the disturbance sequence is greater than 10 ampere square seconds; if not, the grid operating state is normal; if so, it enters the second step, which determines whether the disturbance lasts more than 30 milliseconds, i.e., whether it spans 1500 consecutive sampling points. The time threshold is a response critical time setting value, which ensures that the identification is completed before the disturbance affects the control logic. The third step determines whether the disturbance occurs within a key window time period, such as within 100 milliseconds after grid connection. If two or more of the three conditions are met, the grid operating state is abnormal. According to the evaluation data, a state report is generated and input into an adaptive filtering module. The module uses the least mean square algorithm to extract parameters from the high-frequency disturbance sequence, calculate the target frequency, amplitude, and duration, and then automatically adjust the filter gain parameters, response speed, and time constant to implement compensation filtering on the disturbance waveform, generating a stable current sequence without high-frequency disturbance. The stable current sequence is used for subsequent control operations of the main controller to ensure the stability of power quality and safe operation during wind power grid connection.

[0051] S4 includes generating a compensation current sequence by dynamically adjusting filter parameters using a least mean square adaptive filtering algorithm through a harmonic subset to be suppressed; obtaining a frequency component set by decomposing a signal using a discrete Fourier transform according to the compensation current sequence; if a component of a high-frequency disturbance frequency range exists in the frequency component set, extracting a harmonic sequence of the frequency range through a band-pass filter to obtain a filtered harmonic sequence; obtaining a time-frequency feature sequence by analyzing a time-frequency distribution using a short-time Fourier transform according to the filtered harmonic sequence; determining a power grid state by using a preset classification model through the time-frequency feature sequence to obtain state evaluation data; if the state evaluation data indicates that the power grid state is abnormal, generating a stable current sequence by performing secondary processing on the filtered harmonic sequence through the least mean square adaptive filtering algorithm; and obtaining an optimized current sequence by reconstructing a time-domain signal through an inverse Fourier transform according to the stable current sequence.

[0052] In a possible implementation, the least mean square adaptive filtering algorithm is first called to process the harmonic subset to be suppressed identified in the previous process, and a filter initialization step is started. Specifically, the harmonic subset to be suppressed is taken as a reference input signal, and the original current sequence is taken as a main input signal to be introduced into the least mean square filter, the filter weight coefficient is initialized to zero, and the step size parameter is set to 0.01. The step size value is determined by experiment and is the minimum value that achieves a reasonable convergence speed under the premise of ensuring the stability of the algorithm. After receiving the two signals, the iterative processing is performed step by step at each sampling point. At each sampling point, the filter output value under the current weight is first calculated, and the difference between the output value and the corresponding point of the reference input signal is calculated to obtain an instantaneous error signal. Then, according to the weight update formula of the least mean square algorithm, the error signal is multiplied by the current reference input value, and then multiplied by the step size coefficient to obtain a new increment value. The increment value and the original weight coefficient are added to obtain the weight coefficient input at the next time. The step is continuously run until all sampling points are processed, and the complete compensation current sequence is output.

[0053] After the compensation current sequence is generated, the signal is decomposed by discrete Fourier transform. The window length is 2048 points, the sampling frequency is set to 50000 Hz, and the transform frequency resolution is about 24.41 Hz. The entire compensation sequence is divided into multiple non-overlapping windows, and the Fourier transform calculation is performed in each window to extract the amplitude component of each frequency point and form a frequency component set. All frequency points in the set are analyzed one by one, and it is particularly checked whether the components with frequencies in the range of 2000 Hz to 30000 Hz have an amplitude exceeding 0.6 A. 0.6 A is a fixed threshold, which is 3% of the fundamental amplitude of 20 A, and is the lower limit standard for identifying effective high-frequency disturbances, ensuring that all frequency components included in the processing have observable disturbance characteristics. If the above frequency point exists, the maximum amplitude frequency is taken as the center frequency of the band-pass filter, the bandwidth is fixed at 1000 Hz, and a second-order Butterworth band-pass filter is constructed to extract the filter harmonic sequence.

[0054] The filter harmonic sequence is used as the input signal of time-frequency analysis, and short-time Fourier transform is used for time and frequency decomposition. The window width is set to 4096 points, the sliding step is 512 points, and the window function type is Hamming window to balance the main lobe width and side lobe suppression performance. A discrete Fourier transform is performed in each sliding window, and the results are spliced into a time-frequency distribution map. In the map, the energy trajectory of each frequency point with time is scanned, and the integral of each trajectory is calculated to calculate the energy value of the frequency point in each time window, which is in units of A square seconds. If the energy value of a frequency point in each of the three or more consecutive time windows exceeds 10 A square seconds, the frequency trajectory and the corresponding time period are extracted as a time-frequency feature sequence. 10 A square seconds is a fixed threshold for disturbance energy judgment, which is the minimum response activation threshold based on wind farm current disturbance data analysis, ensuring that the identified frequency has actual control impact.

[0055] The extracted time-frequency feature sequence is input into the classification model, which is a rule-based structure containing three judgment logics: first, whether the disturbance energy exceeds 10 A square seconds; second, whether the disturbance duration exceeds 30 milliseconds, i.e., whether it spans at least 1500 sampling points; third, whether the disturbance occurs within the key time window, such as within 200 milliseconds before and after grid connection. If any of the above three judgments satisfies two or more, the output of the power grid state is abnormal, otherwise the output is normal. The state evaluation result and the disturbance parameter are input into the filter control module.

[0056] When the evaluation result is an abnormal state, the least mean square filtering algorithm is called again to perform secondary filtering processing on the filtered harmonic sequence output in the previous time. The initial weight of the filter is set to the maximum weight value of the first filtering, and the step size is still 0.01. The aforementioned point-by-point error iterative calculation process is repeated to obtain a new stable current sequence. The sequence has significantly weakened the high-frequency disturbance intensity in the frequency component, and the inverse Fourier transform is used to reconstruct it from the frequency domain to the time domain signal. The inverse Fourier transform uses the same window length of 2048 points as the original Fourier transform, and the sampling frequency is 50000 Hz. Each frequency spectrum segment is restored to the original time sequence point, and after splicing, the final optimized current sequence is constructed.

[0057] When the state evaluation data received from the classification model indicates that the grid operating state is abnormal, the second-stage intervention processing procedure is immediately started to perform secondary adaptive filtering processing on the filtered harmonic sequence output to further eliminate residual high-frequency disturbances. This process uses the least mean square adaptive filtering algorithm, and the algorithm initialization step is: the maximum weight coefficient output by the first filtering is taken as the initial weight coefficient of the filter this time, and the filtered harmonic sequence is taken as the main input signal, which is input into the filter structure again, and the step size parameter remains the fixed value of 0.01 set in the first time. The selection of the step size value is based on experiments and is the best constant value after balancing the convergence speed and stability, which can ensure rapid response when high-frequency disturbances exist and avoid over-regulation in the stable interval.

[0058] The filtering process continues in a point-by-point manner. For each input sample point, the filtering output under the current weight is calculated, and the error signal between it and the compensation signal in the previous stage is calculated. The error signal is used to correct the current weight value. The correction formula is: the current weight plus the step size multiplied by the error multiplied by the input value, and the result is taken as the basis for updating the weight of the next sampling point. This calculation logic continues until all sampling points are updated, and the final output of the filtering result is the stable current sequence. The stable current sequence retains the main frequency and normal harmonic signals in the frequency component, but has effectively suppressed the high-amplitude high-frequency disturbance components, and maintains the entire current waveform continuous, smooth and without jump through iterative optimization. Then, the inverse Fourier transform is performed on the stable current sequence to reconstruct it from the frequency domain to the time domain signal to form the final optimized current sequence. The inverse Fourier transform operation parameters are consistent with the aforementioned forward transform, and the window length is set to 2048 points with no overlap splicing. Each frequency spectrum segment is processed individually to perform real-valued inverse Fourier operation to restore it to an equally spaced time point sequence, and then all time domain segments are spliced in order to construct a complete time sequence signal. The optimized current sequence has the same basic form as the original current waveform, maintains the main frequency signal unchanged, and significantly reduces the high-frequency disturbance content, meeting the power quality standard, and can be directly transmitted to the virtual synchronous generator main control for feedback regulation, thereby realizing the response, correction and dynamic stability control of the grid disturbance.

[0059] S5 includes extracting the phase adjustment parameter and the amplitude scaling parameter from the compensation current sequence, decomposing the signal by using the fast Fourier transform, obtaining the phase and amplitude characteristics, and obtaining the phase adjustment parameter and the amplitude scaling parameter; calculating the phase difference and the amplitude ratio of the compensation current sequence and the original digital current sequence according to the phase adjustment parameter and the amplitude scaling parameter, evaluating the matching degree of the two by using the cosine similarity algorithm, and obtaining a matching degree value; if the matching degree value is higher than a preset threshold, fusing the compensation current sequence to the main control loop by using the weighted signal superposition operation to obtain a fused current signal; removing the noise component from the fused current signal by using a filter to obtain a denoised current signal; calculating the time domain statistical characteristics of the denoised current signal, judging the signal stability by using a support vector machine classification model, and obtaining a stability evaluation result; if the stability evaluation result indicates that the signal is unstable, performing secondary adjustment on the denoised current signal by using the least mean square adaptive filtering algorithm to obtain a stable current signal; and reconstructing the time domain signal by using the inverse fast Fourier transform according to the stable current signal to obtain an optimized current signal.

[0060] In a possible implementation, first, the fast Fourier transform operation is performed on the compensation current sequence, the window length of the Fourier transform is set to 2048 points, and the sampling frequency is 50000 Hz. The configuration ensures that the frequency resolution is about 24.41 Hz, so that the amplitude and phase characteristics of each frequency component can be clearly distinguished. After the transformation, the phase value and the amplitude value of the current sequence are extracted from the main frequency point as the main phase adjustment parameter and the amplitude scaling parameter of the sequence. Then, the same Fourier transform process is used on the original digital current sequence to extract the phase value and the amplitude value of the main frequency point. The phase difference between the two main frequency points is calculated as the compensation current phase value minus the original current phase value to obtain the phase adjustment parameter. The amplitude scaling parameter is the amplitude value of the compensation current main frequency point divided by the amplitude value of the original current main frequency point to obtain the relative proportion of the two sequences in the amplitude direction.

[0061] Then, based on the above two parameters, respective signal vectors are constructed, and are uniformly standardized into real number vectors with a length of 2048 points. Subsequently, the cosine similarity algorithm is used to perform matching degree calculation on the two vectors. The specific calculation steps are as follows: first, the sum of the dot products of the two vectors is calculated, then the lengths of the two vectors are calculated respectively, and finally the dot product value is divided by the product of the lengths to obtain a matching degree value between 0 and 1. The threshold is set to 0.95, which is derived from a large number of experimental simulations and stability evaluation analysis of the signal after fusion, which can ensure that only in the case that the phase difference is less than 15 degrees and the amplitude ratio is between 0.9 and 1.1, the matching degree is considered to be high enough to ensure that the signal after superposition will not introduce unstable disturbance.

[0062] If the matching degree value is higher than 0.95, a signal fusion operation is performed. The fusion method adopts a weighted linear superposition method, wherein the original current signal weight is 70%, and the compensation current signal weight is 30%. The weight ratio is determined according to the control stability and response speed simulation data, which retains the main body of the original signal and moderately introduces compensation information to correct the disturbance. The superimposed fusion current signal immediately enters the filter processing module, the filter type is a Butterworth low-pass filter, and the filter cutoff frequency is set to 2500 Hz. The value is the lower limit of the high-frequency disturbance frequency of the main frequency component of the original current signal, which can effectively filter high-frequency noise while retaining the target frequency component. The order of the filter is set to 2, which ensures that the phase response is smooth enough. After filtering, the output is a denoising current signal.

[0063] Subsequently, the time domain statistical characteristics of the denoising current signal are calculated, and a sliding window method is used, with each window length being 1024 points and a sliding step length of 512 points. Four characteristic indexes, including the average value, standard deviation, root mean square value, and peak factor, are calculated for the data in each window. These characteristic values constitute a 4-dimensional feature vector as input into the support vector machine model. The model uses a radial basis kernel function and is pre-trained using normal current and abnormal disturbance current samples for supervised learning. The output value of the model is set to 0 as the decision boundary. When the output value is greater than 0, the signal is considered stable, and otherwise it is considered unstable.

[0064] If the support vector machine model judges that the signal is unstable, a least mean square adaptive filtering algorithm is immediately called to perform secondary dynamic adjustment processing on the denoising current signal. The initial weight vector of the filter is set to all zeros, the step coefficient is fixed at 0.01, and the step value is a constant value determined by balancing the convergence speed and response sensitivity. At each sampling point, the error between the current weight prediction output value and the expected value is multiplied by the step value and the input signal to form the weight update amount. After updating the existing weight vector, the next sampling point is processed, and finally the adaptive filtering of the entire signal is completed, with the output being a stable current signal.

[0065] Finally, the stable current signal is input into the inverse fast Fourier transform module, using the same window parameters as before, 2048 points, to restore the amplitude and phase information in the frequency domain to a time domain sequence, obtaining the final optimized current signal. The optimized current signal has removed high-frequency disturbances and maintained the consistency of the main frequency in the frequency component, and has good continuity and response stability in the time domain, which can be used as the current control input signal of the virtual synchronous generator for real-time execution of the stable operation control strategy.

[0066] The matching degree threshold is 0.95, which is determined based on a large amount of actual grid disturbance data of wind farms and stability simulation analysis results. The threshold is used to evaluate the matching degree of the compensation current sequence and the original digital current sequence in amplitude and phase. The core reason for setting 0.95 is that the cosine similarity of the two can reach more than 95%, which represents a high consistency in signal form and frequency component, so as to avoid introducing secondary disturbance risk caused by phase misalignment or amplitude distortion in the signal fusion process. Specifically, when the matching degree is less than 0.95, the common error is that the phase shift exceeds 15 degrees or the amplitude ratio deviates more than 10%. Such deviation can easily cause nonlinear distortion of the current waveform in the virtual synchronous generator control, and then cause control instruction conflict between links. Therefore, 0.95 as the minimum matching threshold of stable fusion not only guarantees the effectiveness of the fused compensation current, but also maximizes the probability of introducing new disturbance.

[0067] S6 includes obtaining time domain features from the optimized current signal, using a digital signal processor to perform real-time calculation to obtain an inhibition instruction sequence; using the inhibition instruction sequence, calculating the deviation from the main control loop through a feedback loop to obtain a deviation correction sequence; if the amplitude of the deviation correction sequence exceeds a preset threshold, using a Kalman filter algorithm to optimize the inhibition instruction sequence to obtain an optimized pulse sequence; according to the optimized pulse sequence, calculating the statistical characteristics of the time sequence, using a support vector machine classification model to judge the stability of the sequence to obtain a stability judgment result; through the stability judgment result, extracting the unstable sequence segment, using an adaptive filtering algorithm for secondary adjustment to obtain a stable pulse sequence; according to the stable pulse sequence, generating a directional inhibition pulse sequence, using a digital signal processor to perform real-time output to obtain a final pulse sequence; through the final pulse sequence, calculating the frequency distribution characteristics of the sequence, using a fast Fourier transform to verify the frequency consistency to obtain a verification pulse sequence; extracting network transmission data packets from the directional pulse sequence, using timestamp analysis to calculate the data packet delay to obtain a delay judgment result; if the delay judgment result is lower than a preset threshold, using an encryption standard algorithm to encrypt the network transmission data packet to obtain an encrypted data packet; according to the encrypted data packet, using a transmission control protocol optimization strategy to adjust the data packet sending order to generate an optimized data stream; through the optimized data stream, using a sliding window protocol to analyze the network bandwidth occupation situation to obtain bandwidth allocation parameters; according to the bandwidth allocation parameters, using a flow control algorithm to adjust the data packet transmission rate to generate an adjusted data stream; extracting remote adjustment parameters from the adjusted data stream, using a data compression algorithm to generate a compression parameter set to obtain a remote adjustment parameter set; through the remote adjustment parameter set, using a verification algorithm to verify the integrity of the parameter set to obtain a verified parameter set.

[0068] In one possible implementation, the optimized current signal is first analyzed in real time, and its time-domain features are extracted using a digital signal processor at a fixed sampling frequency of 50000 Hz, including the amplitude, rate of change, average value, and standard deviation of the current value, etc. These features form a sliding window structure with each group of 1024 sampling points, and statistics are performed on each group to form a multi-dimensional feature vector. This processing is performed internally in the digital signal processor through a pre-set function to avoid delay. Subsequently, these features are input into the control module and converted into a quantitative suppression instruction sequence through linear mapping and threshold classification, with the instruction amplitude range controlled between 0 and 1, the numerical accuracy of three decimal places, and the slope coefficient of the mapping function obtained from the offline training model to ensure strong response sensitivity in the case of high disturbance. Next, the suppression instruction is input into the control feedback loop, and the difference between the ideal output signal in the main control loop and the two is calculated sample by sample. The difference multiplied by the feedback gain constant 0.8 forms the deviation correction value, and all correction values are sequentially arranged to form the deviation correction sequence. If the absolute value of the deviation of any sampling point exceeds 1.0, it is determined that the control error is too large, and the Kalman filter optimization process is started. The initial prediction error of the Kalman filter is set to 0.05, the state transition noise is 0.02, and the observation noise is 0.01. These parameters are determined through offline simulation model under multiple disturbance tests to ensure that the optimization converges within 5 milliseconds. The filter predicts and updates the state value of each sampling point according to the standard gain correction process, and finally outputs the optimized pulse sequence.

[0069] After that, the optimized pulse sequence is statistically processed, with each group of 20 points forming a sliding window, and the mean and standard deviation within the window are calculated for evaluating the stability of the pulse. These values are input into the support vector machine model, which uses a Gaussian radial basis kernel function. The model structure has been trained before deployment. The training samples include 1000 normal pulses and 1000 abnormal pulses, and the optimal hyperparameters are selected through five-fold cross-validation. The model outputs 0 for unstable and 1 for stable. If the output is 0, the unstable section is marked and segmented into fragments. Each fragment uses the least mean squares adaptive filtering algorithm for dynamic adjustment, with the initial weight being zero and the step size being 0.01. The error between the output value and the ideal value is calculated for each sample point, multiplied by the step size and the input to form the weight correction value, and the updated values of all sample points form the stable pulse sequence.

[0070] The stable pulse sequence input digital signal processor, output in time sequence, each output interval is 1 ms, constitute the final directional suppression pulse sequence, and send to the execution control unit. At the same time, the pulse sequence is subjected to fast Fourier transform, every 256 points is a frequency window, the main frequency amplitude and phase are extracted, and compared with the original optimized current signal frequency information, if the difference is less than 5 Hz, and the amplitude ratio is between 0.95 to 1.05, then it is marked as frequency consistent, through the verification sequence. This sequence is then encapsulated in network, forming data packets according to standard communication protocol, each packet is attached with 1 ms precision timestamp, transmitted to the remote server, compared with the current receiving time by the receiving end, calculate the data packet delay, if less than 50 ms, then the data packet is encrypted by AES encryption standard.

[0071] Then, the encrypted data packet is subjected to sequence number reordering and retransmission logic processing using TCP protocol optimization process, to ensure the correct order of data packets and minimize packet loss. Then, the network bandwidth occupation is analyzed by sliding window protocol, the window is set to 5 data packets, the maximum throughput per second is analyzed, and compared with the current total traffic, to calculate the bandwidth allocation parameter. The parameter is input to the flow control module to adjust the number of data packets sent per second in the next cycle, to realize stable transmission rate control. Finally, the remote adjustment parameters are extracted from the adjusted data stream, the redundant bits of the parameters are removed by standard compression algorithm to form a compressed parameter set; then the integrity of the compressed parameter set is verified by using cyclic redundancy check method, if the check value is correct, then the output is the final verification parameter set, for the remote end control module to update and synchronize the local parameters.

[0072] The optimized data stream generated during network transmission is monitored and analyzed by sliding window protocol. The window size is set to 5 data packets, and the sliding step is 1 data packet. The total number of data packets and the transmission time interval are recorded in each window period, the current actual bandwidth usage is calculated, and compared with the network preset maximum available bandwidth value, to obtain the bandwidth allocation parameter. The bandwidth allocation parameter is a floating point value, ranging from 0.1 to 1.0, representing the occupancy ratio of the current network available bandwidth. This parameter is dynamically determined under different time periods and network load conditions through multiple experimental measurements, to ensure real-time reflection of network status.

[0073] Subsequently, the data packet sending rate is adjusted according to the bandwidth allocation parameter using flow control algorithm. The control logic is: if the bandwidth allocation parameter is less than 0.6, the sending rate is reduced proportionally to prevent overload; if the parameter is greater than 0.8, the sending rate is allowed to increase to improve transmission efficiency; between 0.6 and 0.8, the stable rate is maintained. The adjusted data packets are sent periodically according to the new rate, forming the adjusted data stream.

[0074] The embedded remote adjustment parameters are extracted from the adjusted data stream, which include the feedback adjustment information generated by the aforementioned control module according to the grid state analysis results, such as filter parameters, adjustment coefficients or control threshold values. After collecting these parameters, a data compression algorithm is used to compress them. The compression algorithm uses a fixed dictionary table matching method to represent repeated parameter values or structural information with abbreviated marks and delete redundant bits to improve transmission efficiency, with a compression ratio of more than 2 times.

[0075] Finally, a check process is performed on the remote adjustment parameter set, a cyclic redundancy check algorithm is used to generate a 4-byte length check code, and the check code is attached to the tail of the parameter set. After receiving the compressed parameter set, the receiving end recalculates the cyclic redundancy check value of the received data and compares it with the attached check code. If they are consistent, it means that the data has not been damaged, and the data set is passed to the remote control end module as a verification parameter set to update the control logic and achieve parameter synchronization adjustment.

[0076] S7 includes extracting feedback values from the remote adjustment parameter set, using a data parsing algorithm to separate effective feedback data to obtain a feedback data set; according to the feedback data set, using a server response analysis to extract update parameters to generate an update parameter set; if the update parameter set meets the preset threshold, using a proportional-integral-derivative control algorithm to adjust the feedback data set to obtain an adjusted data set; according to the adjusted data set, combining an error retransmission mechanism to detect data integrity to generate a retransmission correction data set; extracting local filter parameters from the retransmission correction data set, using a sliding average algorithm to optimize parameter smoothness to obtain smooth filter parameters; adjusting the suppression output sequence through the smooth filter parameters, using a sequence generation algorithm to form a final output sequence; according to the final output sequence, using a check algorithm to verify the sequence integrity to obtain a verification output sequence.

[0077] In one possible implementation, the feedback values are first extracted from the remote adjustment parameter set, which is the result generated by the upper server after completing the comprehensive analysis of the on-site device running state, power grid disturbance level and control signal response delay, and contains multiple feedback fields, including filter adjustment gain, current balance deviation value, time delay compensation coefficient and phase synchronization parameter. Using an embedded data parsing algorithm, each record in the parameter set is split into a key-value pair structure according to the preset protocol format, and records with format errors, missing fields or data overflow are filtered out, leaving only feedback fields with complete structure and valid values. After combination, a feedback data set is generated and stored in the local register module. Each feedback field is immediately converted to a unit format compatible with the local controller after extraction, such as filter gain recorded in real number format with a unit of 1, current deviation recorded in amperes, time delay compensation recorded in milliseconds, and phase synchronization recorded in degrees. These units are clearly defined in the protocol, with upper and lower limits configured for each parameter, such as an effective range of 0.1 to 10 for filter gain, a current deviation allowed to fluctuate within ±5 amperes, a time delay compensation limit of 100 milliseconds, and an angle synchronization range within 0 to 360 degrees. These intervals are determined through pre-engineering tests combined with actual power grid fluctuation conditions to ensure that both disturbances can be responded to and oscillations caused by excessive control can be avoided.

[0078] Subsequently, the feedback data set is subjected to a server response analysis process, which classifies and sorts the feedback fields according to the time stamps, priority markers and modification identifiers attached to them, and extracts the latest batch of unsynchronized control parameters as an update parameter set according to the update strategy. For each field, a preset threshold range is set for verification, such as a filter gain variation amplitude not exceeding 0.5, a current deviation adjustment amplitude not exceeding 3 amperes, a time delay correction error less than 20 milliseconds, and a phase angle update step not exceeding 15 degrees. If the update amplitude of all fields falls within the corresponding threshold, the update parameter set is determined to be valid, and the proportional-integral-derivative control process is entered. The proportional-integral-derivative control module first performs multiplication operation on each feedback error value according to the proportional coefficient, which is set to 0.8 according to the historical disturbance response stability. The integral part multiplies the continuous error by the integral coefficient 0.05 after integration, and the differential part multiplies the error increment by the differential coefficient 0.02. The three are superimposed to form the adjustment result output, which constitutes the adjusted data set.

[0079] To ensure that data is not missing or damaged in the feedback link, the adjusted data set is transmitted to the retransmission detection module, which uses a hash checksum and frame number confirmation mechanism to verify the integrity of each data packet. If a data packet does not receive a confirmation response within a specified time, or the checksum is inconsistent, the data packet is added to the retransmission queue and retransmitted to ensure that all critical data eventually arrives successfully. After all fields are confirmed to be correct, the complete data after retransmission is integrated to form the retransmission correction data set.

[0080] After extracting all the local filter parameters from the data set, the moving average processing module is called in sequence to calculate the average filter gain in each 5 consecutive data window, and the average filter gain is taken as the output value of the filter parameter at the current time. The moving average process is sliding in time sequence with a step size of 1, ensuring smooth transition of each filter update action, avoiding parameter mutation caused by control shock. The processed smooth filter parameters are input into the control module to correct the current suppression output sequence, i.e. adjusting the amplitude, response speed and stability index of the output current according to the new filter parameter setting. The corrected control output is constructed into the final output sequence according to the preset output interval and sequence length using the sequence generation algorithm. The output interval is set to 1 millisecond, and the total length of the sequence is 1024 sampling points, ensuring the requirements of high-speed response and stable control.

[0081] Finally, in order to ensure the reliability of the final output sequence in subsequent processing or transmission, a cyclic redundancy check process is performed on the sequence to calculate the check code (length of 4 bytes) of the complete sequence and append it to the tail of the output sequence to form a verification output sequence. The receiving end calculates the check value using the same algorithm when parsing the sequence, and compares it with the appended value. If they are consistent, it means that the sequence has not been damaged in the transmission or buffering process and can be used for control execution, parameter update or power grid regulation action. This processing flow ensures the completeness, real-time performance and anti-interference ability of the control link closed loop, and guarantees the stable execution of the power grid harmonic suppression process.

[0082] When the final output sequence is constructed by the sequence generation module, it immediately enters the verification processing stage, which aims to ensure that the sequence has not been lost, tampered or misplaced during transmission, buffering or execution. This process first calls the cyclic redundancy check algorithm, i.e. calculating the check code of the entire final output sequence according to the fixed polynomial generation rule. In the calculation, the final output sequence is read byte by byte, and logical division operation is performed based on the preset generation polynomial. Each step of operation performs exclusive or processing, and finally generates a 4-byte length check code. This check code is the unique digest value of the sequence data content, which is used to identify the integrity characteristics of the data.

[0083] After the completion of the check code calculation, the 4-byte check code is appended to the tail of the final output sequence to form a verification output sequence with integrity protection. At this time, the sequence contains both complete data for control and additional codes for integrity check. In the subsequent transmission process, the receiving end or local execution module will re-calculate the cyclic redundancy check code for the received data part and compare the calculation result with the attached check code byte by byte. If the comparison is completely consistent, it indicates that the output sequence has not occurred any error in the entire processing and transmission process, that is, it is confirmed that the verification output sequence is valid and allows it to be used for the execution of current suppression control instructions; if the check fails, the sequence is immediately discarded, and the data source is requested to regenerate the final output sequence to ensure the accuracy and safety of the operation.

[0084] S8 includes extracting grid stability features from the suppression output sequence, separating intermittent disturbance data using a feature extraction algorithm to obtain a disturbance feature set; if the disturbance feature set is within the preset threshold deviation range, verifying the stability index using a threshold comparison algorithm to generate a verification result set; according to the verification result set, re-collecting data from the digital current sequence using a cyclic sampling mechanism to obtain an updated sampling data set; extracting time sequence features from the updated sampling data set, analyzing sequence continuity using a sliding window algorithm to obtain a continuity analysis result; if the continuity analysis result meets the preset continuity condition, merging the updated sampling data set and the disturbance feature set through a data integration algorithm to generate an integrated monitoring data set; according to the integrated monitoring data set, judging the grid operation state using a state monitoring algorithm to obtain a state evaluation result; extracting abnormal fluctuation features from the state evaluation result, and generating an abnormality marker sequence using an anomaly detection algorithm.

[0085] First, the grid stability features are accurately extracted from the suppression output sequence, which contains 1024 consecutive current values sampled at 1 millisecond intervals. The feature extraction algorithm is called for each sampling point in turn: taking the current point as the center, taking the previous and next 10 sampling points to form a 21-point local time sequence segment, calculating the maximum amplitude jump, root mean square value and local change rate of the segment, and the three indicators follow a unified unit: current amplitude in amperes, and change rate in amperes per millisecond; set warning thresholds for each feature: amplitude jump not more than 5 amperes, root mean square value change rate not more than 0.1 amperes per millisecond, and maximum continuous change not more than 50 amperes milliseconds (indicating the jump amplitude multiplied by the duration), these thresholds are determined by combining 95% statistical results of historical grid fluctuations and actual measurement data of wind farms to ensure that the identified disturbance has engineering significance. Traverse all sampling points in time sequence, and record the start time, end time, feature index value and duration for each disturbance segment.

[0086] Subsequently, each disturbance record in the disturbance feature set is retrieved and checked item by item using a threshold comparison algorithm: check whether the maximum amplitude jump is less than or equal to 5 A, check whether the root mean square value change is less than or equal to 0.1 A per millisecond, check whether the duration is less than or equal to 50 milliseconds, if all indicators of a record fall within the corresponding threshold range, the disturbance is determined to be in an allowable state, and the record is included in the verification result set, otherwise it is considered abnormal and is excluded from the verification result set.

[0087] After forming the verification result set, a cyclic sampling mechanism is triggered to start sampling from the current digital current sensor at a fixed interval of 1 millisecond, and 1024 sampling points are continuously collected to form an updated sampling data set. The sampling process is controlled by a digital signal processor without time delay skipping to ensure sampling continuity and data integrity.

[0088] The sliding window continuity analysis is performed on the updated sampling data set, the window length is 64 points, and the step is 1 point. The average current and change slope are calculated for each window. The continuity condition is set as: the window average change rate is not more than 0.1 A per millisecond, and the average change rate between adjacent windows is not more than 0.05 A per millisecond. If all sliding windows meet these conditions, the sequence continuity is determined to be good.

[0089] When the continuity condition is met, the data integration algorithm is used to fuse the updated sampling data set with the previously recorded disturbance feature set in timestamp order. In the fusion process, the repeated data existing in the same period is preferentially saved, and the update source is marked. Each data is attached with "source label" and "time priority", and is uniformly formatted into structured monitoring records to form an integrated monitoring data set. Then the state monitoring algorithm is called to process the integrated monitoring data set. The algorithm calculates the fluctuation amplitude, frequency offset and phase jump of each record in turn, and compares with the preset operating threshold: the fluctuation amplitude is not more than 3% of the average value of the fundamental wave, the frequency offset is not more than 0.5 hertz per millisecond, and the phase jump is not more than 10 degrees. If all indicators are within the threshold, the record is determined to be stable, otherwise it is determined to be abnormal. The final state evaluation result is generated by integrating the judgment results of all records. According to the state evaluation result, all records judged to be in "abnormal state" are extracted, and the time interval, current level and frequency offset are extracted as abnormal fluctuation features. These feature segments are input into the abnormal detection algorithm, which generates start and end time nodes for each abnormal label, and outputs an abnormal label sequence, which indicates that there is unstable fluctuation in the specific period and can be used for subsequent intervention by control logic or operation personnel.

[0090] When the sliding window continuity analysis result meets the preset continuity condition, i.e. the mean value change rate in the window is less than 0.1 A per millisecond and the variance is not more than 0.5 A square, the current updated sampling data set and the previously extracted disturbance feature set are merged in the time domain by a data integration algorithm. The merging process takes the timestamp of each record as the primary key, preferentially retains the time period data covered in the updated sampling data set, and for overlapping data, selects higher quality data through a data source identification and integrity priority mechanism and unifies the format. The merging result is stored as a structured integrated monitoring data set. This data set contains the timestamp, current amplitude, change rate, disturbance feature identification and source label of each data, ensuring traceability and data consistency of each record.

[0091] Next, a state monitoring algorithm is called to judge the state of the integrated monitoring data set. This algorithm analyzes the peak value, frequency fluctuation, phase shift and short-time amplitude change of each record and compares it with the following set standard values: the peak value deviation should not exceed 3% of the fundamental average value, the frequency change should not exceed 0.5 Hz per millisecond, and the phase jump should not exceed 10 degrees. According to the above rules, each record in the integrated data set is assigned a "normal" or "abnormal" state label. Subsequently, the state evaluation result of the power grid operation is generated based on the overall record state. If more than 90% of the records are "normal", the overall power grid is stable; if more than 10% of the records are "abnormal", it is determined that there is a non-stable operation risk.

[0092] Finally, all records marked as "abnormal" are extracted from the state evaluation result, and their specific time range, current waveform and frequency offset are identified to form abnormal fluctuation feature data. Then, an abnormality detection algorithm is called to process this data. The algorithm analyzes the start time, peak value change rate, mutation slope and duration of each abnormal record based on the change mutation point detection principle. If the above indicators reach the set change rate threshold for consecutive multiple sampling points, the waveform segment is marked as abnormal. Finally, all abnormal segments are formed into an abnormality marking sequence in chronological order, recording their start and end times, current change characteristics, frequency and phase disturbance information, and serving as a reference for subsequent control strategy execution or external intervention.

[0093] In the process of extracting the grid stability features, the maximum amplitude jump threshold is set to 5 A, which is based on the current fluctuation historical data collected at the grid connection point under the typical operating state of the wind farm. More than 95% of the normal operation samples have a transient jump amplitude of less than 5 A. Therefore, setting 5 A as the boundary value can effectively exclude normal disturbances and sensitively capture the occurrence of abnormal high-frequency disturbances, ensuring that the screening process has high identification accuracy and engineering applicability. The duration threshold is set to 50 milliseconds, which is based on the actual grid stability under stable operation. The duration of intermittent disturbances is usually less than 30 milliseconds, and disturbances exceeding 50 milliseconds will have a substantial impact on grid control stability. To avoid misjudgment of transient disturbances, while considering response delay and oversampling, 50 milliseconds as the fault tolerance upper limit has sufficient technical tolerance, ensuring that the stability monitoring function is not disturbed by short-term fluctuations. The threshold of energy accumulation value is based on the energy distribution of historical data during normal operation period. The 95th percentile statistical value is set as the upper limit to ensure that the evaluation of disturbance intensity is neither too sensitive nor too sluggish. The energy accumulation value represents the current amplitude squared multiplied by the duration of time, which can comprehensively reflect the intensity and duration of the disturbance. The purpose of setting this threshold is to exclude non-critical disturbances and improve the response ability of the suppression algorithm to actual threats. The mean rate of change threshold in the sliding window continuity analysis is set to 0.1 A per millisecond, which is based on a sampling period of 1 millisecond. The steady-state threshold is obtained by statistical analysis of the average and maximum values of the fluctuation rate during continuous operation. This value ensures the continuity of the current sequence, thereby avoiding false triggering and state misjudgment caused by data mutations. The frequency offset is set to not more than 0.5 Hz per millisecond, and the phase jump is not more than 10 degrees. These two items are based on the international grid connection standard and wind power converter control tolerance range, respectively, and belong to the basic constraint conditions of high-frequency disturbance identification.

[0094] As Figure 2Also shown, a virtual synchronous generator grid-connected current harmonic suppression system is provided for implementing the steps of the virtual synchronous generator grid-connected current harmonic suppression method, the system comprising a sampling module for obtaining a grid-connected current signal of a wind farm in a power grid, using an analog-to-digital converter to digitally sample intermittent high-frequency disturbances to obtain an original digital current sequence; a frequency spectrum analysis module for analyzing wind-borne frequency components using a fast Fourier transform algorithm based on the original digital current sequence to determine the amplitude and phase characteristics of high-frequency harmonic components; a disturbance extraction module for extracting an intermittent disturbance harmonic sequence from the frequency components when the amplitude of the high-frequency harmonic components exceeds a predetermined threshold to obtain a harmonic subset to be suppressed; a filter control module for dynamically adjusting filter parameters using an adaptive filtering algorithm based on the harmonic subset to be suppressed to generate a compensation current sequence; a matching calculation module for obtaining phase matching adjustment and amplitude scaling parameters from the compensation current sequence to determine the matching degree of the compensation current sequence and the original digital current sequence; if the matching degree is higher than the threshold, the compensation current sequence is fused into the main control loop through signal superposition operation and noise suppression filtering to obtain an optimized current signal; a digital signal processing module for calculating suppression instructions in real time using a digital signal processor based on the optimized current signal, while performing loop feedback integration and stability verification checks to generate a directional suppression pulse sequence; a remote transmission module for obtaining network transmission data packets, performing delay threshold judgment and network bandwidth evaluation, and if the delay is lower than the threshold, using data packet encryption transmission and transmission protocol optimization to send to a remote server to obtain a remote adjustment parameter set; a parameter update module for extracting feedback values from the remote adjustment parameter set, using server response analysis and parameter set download updates, using a proportional-integral-derivative control algorithm combined with an error retransmission mechanism and feedback value extraction processing to update local filter parameters to generate a final suppression output sequence; a stability monitoring module for determining whether the intermittent disturbance stability index of the remote wind farm power grid meets the requirements based on the final suppression output sequence, and if it meets the requirements, the sampling process of the original digital current sequence is recycled to maintain continuous monitoring state.

[0095] The sampling module is responsible for first obtaining the current signal from the wind farm grid connection point, and continuously digitally sampling the current signal through an analog-to-digital converter, with a sampling frequency set to 10,000 Hz to ensure high-resolution capture capability for high-frequency disturbances. The original digital current sequence generated by each sampling has a length of 1024 points, and this data sequence is sent to the frequency spectrum analysis module.

[0096] The frequency spectrum analysis module receives the original digital current sequence, calculates its frequency domain components using the fast Fourier transform algorithm, and extracts the amplitude and phase of each frequency component for the high-frequency section with a frequency greater than 500 Hz. The system compares the amplitude of each high-frequency component with a predetermined threshold, and if the amplitude of a certain frequency component exceeds 3 amperes, the disturbance extraction module is triggered to work.

[0097] The disturbance extraction module takes a set of frequency components as input, and uses spectral analysis to identify the frequency points with intermittent characteristics. The energy distribution of each frequency point over time is calculated using short-time Fourier transform and wavelet transform, and the frequency component sequence with a disturbance duration of more than 30 milliseconds is selected as the harmonic subset to be suppressed and transmitted to the filter control module.

[0098] The filter control module receives the harmonic subset to be suppressed, dynamically adjusts the filter weights using the least mean square error adaptive filtering algorithm, and generates a compensation current sequence with opposite phase and equal amplitude in real time. This sequence is used in the time domain to suppress the target disturbance.

[0099] The matching calculation module then compares the compensation current sequence and the original current sequence, extracts the phase difference and amplitude ratio of each corresponding frequency, and calculates the cosine similarity value as the matching degree indicator. If the matching degree is greater than 0.95, the system performs signal superposition and noise filtering, merges the compensation current sequence into the main control loop, and outputs the optimized current signal.

[0100] The digital signal processing module calculates the next suppression instruction based on the time domain characteristics of the optimized current signal, and updates the control strategy in real time combined with the current changes in the feedback channel. This module also completes the stability verification logic and generates a directional suppression pulse sequence for adjusting the power converter.

[0101] The remote transmission module is used to convert the above suppression pulse sequence into control data packets, and before sending, it judges whether the network transmission delay is less than 20 milliseconds through the delay evaluation mechanism. If it meets the requirements, the system uses encryption algorithm to encrypt the data packet, and uses the optimization mechanism based on the transmission control protocol to send the data packet to the remote server.

[0102] The parameter update module receives the adjustment parameter set from the remote server, parses the effective feedback value through the feedback response channel, and uses the proportional-integral-derivative control algorithm combined with the error retransmission mechanism to correct the parameters. Finally, the updated control parameters are applied to the filter configuration to generate the final suppression output sequence.

[0103] The stability monitoring module analyzes the final suppression output sequence, extracts the fluctuation rate, frequency offset and continuity indicators of the grid-connected current sequence, and judges whether it meets the set wind farm grid stability requirements. If the evaluation result meets the system set threshold, the system automatically loops back to the sampling module and re-executes the next round of sampling and analysis, thereby realizing continuous monitoring throughout the cycle.

[0104] This system architecture has the characteristics of clear modules, clear function division, and closed-loop execution process, and can effectively meet the high-frequency harmonic detection, identification and compensation control requirements in the wind power grid connection scene, and ensure the quality of grid-connected current and the stability of the grid.

[0105] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and variations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for suppressing harmonics in grid-connected current of a virtual synchronous generator, characterized in that, include: S1. Using the grid-connected current signal of the wind farm in the power grid, the intermittent high-frequency disturbance is digitally sampled by an analog-to-digital converter to obtain the original digital current sequence; S2. Based on the original digital current sequence, the wind load frequency components are analyzed using the Fast Fourier Transform algorithm to determine the amplitude and phase characteristics of the high-frequency harmonic components. S3. If the amplitude of the high-frequency harmonic component exceeds the preset threshold, extract the intermittent perturbation harmonic sequence from the frequency components to obtain the harmonic subset to be suppressed. S4. Using the subset of harmonics to be suppressed, the filter parameters are dynamically adjusted using an adaptive filtering algorithm to generate a compensation current sequence; S5. Obtain the phase matching adjustment and amplitude scaling parameters from the compensation current sequence, determine the matching degree between the compensation current sequence and the original digital current sequence, and if the matching degree is higher than the threshold, fuse the compensation current sequence into the main control loop through signal superposition operation and noise suppression filtering to obtain the optimized current signal. S6. Based on the optimized current signal, a digital signal processor is used to calculate the suppression command in real time, while loop feedback integration and stability verification are performed to generate a directional suppression pulse sequence. Through the directional suppression pulse sequence, network transmission data packets are obtained, and delay threshold judgment and network bandwidth assessment are performed. If the delay is lower than the threshold, data packet encryption transmission and transmission protocol optimization are used to send the data to the remote server to obtain a remote adjustment parameter set. S7. Extract feedback values ​​from the remote adjustment parameter set, update the local filter parameters through server response parsing and parameter set download, and use a proportional-integral-derivative control algorithm combined with an error retransmission mechanism and feedback value extraction processing to generate the final suppressed output sequence. Specifically, this includes: Feedback values ​​are extracted from the remote adjustment parameter set, and effective feedback data are separated using a data parsing algorithm to obtain the feedback dataset; Based on the feedback dataset, update parameters are extracted by parsing the server response and an update parameter set is generated. If the updated parameter set meets the preset threshold, the proportional-integral-derivative control algorithm is used to adjust the feedback dataset to obtain the adjusted dataset. Based on the adjusted dataset, and combined with the error retransmission mechanism to check data integrity, a retransmission correction dataset is generated. Local filter parameters are extracted from the retransmission correction dataset, and the smoothness of the parameters is optimized using a moving average algorithm to obtain smoothed filter parameters; The output sequence is suppressed by adjusting the smoothing filter parameters, and the final output sequence is formed by using a sequence generation algorithm. Based on the final output sequence, a verification algorithm is used to verify the sequence integrity, and a verification output sequence is obtained. S8. Based on the final suppressed output sequence, determine whether the intermittent disturbance stability index of the wind farm grid meets the requirements. If it does, loop back to the sampling process of the original digital current sequence to maintain continuous monitoring. Specifically, this includes: Power grid stability features are extracted from the suppressed output sequence, and intermittent disturbance data are separated using a feature extraction algorithm to obtain a disturbance feature set; If the perturbation feature set deviates from the preset threshold within a certain range, the threshold comparison algorithm is used to verify the stability index and generate a verification result set. Based on the verification result set, a cyclic sampling mechanism is used to re-acquire data from the digital current sequence to obtain an updated sampling dataset; Time series features are extracted from the updated sampled dataset, and the continuity of the sequence is analyzed using the sliding window algorithm to obtain the continuity analysis results; If the continuity analysis results meet the preset continuity conditions, the sampled dataset and the disturbance feature set are merged and updated through the data integration algorithm to generate an integrated monitoring dataset. Based on the integrated monitoring dataset, a condition monitoring algorithm is used to determine the power grid operating status and obtain the condition assessment results. Abnormal fluctuation features are extracted from the state assessment results, and anomaly detection algorithms are used to generate anomaly marker sequences.

2. The method for suppressing harmonics in the grid-connected current of a virtual synchronous generator according to claim 1, characterized in that: S1 includes: The grid-connected current signal of the wind farm is digitally sampled by an analog-to-digital converter to generate an original digital current sequence. The frequency domain analysis of the original digital current sequence is performed using the Fast Fourier Transform algorithm to obtain the frequency components of the current signal; If there are abnormal high-frequency components in the frequency components that are higher than the preset threshold, the original digital current sequence is filtered by a bandpass filter to obtain the filtered current sequence. Based on the filtered current sequence, the time-frequency features are extracted using the short-time Fourier transform algorithm to generate the time-frequency distribution of the current signal; If the duration of intermittent high-frequency disturbances detected in the time-frequency distribution exceeds a preset threshold, the filtered current sequence is decomposed using a wavelet transform algorithm to obtain the high-frequency disturbance components. Based on the high-frequency disturbance components, calculate their energy distribution characteristics to determine the disturbance intensity and location. The power grid's operating status is determined by analyzing the intensity and location of disturbances using a pre-defined classification model.

3. The method for suppressing harmonics in the grid-connected current of a virtual synchronous generator according to claim 1, characterized in that: S2 includes: The Fast Fourier Transform algorithm is used to perform frequency domain analysis on the original digital current sequence to obtain the amplitude and phase characteristics of the high-frequency harmonic components. Based on the amplitude and phase characteristics of the high-frequency harmonic components, the original digital current sequence is processed by a bandpass filter to obtain the filtered current sequence. The time-frequency distribution characteristics of the filtered current sequence are obtained by using the short-time Fourier transform algorithm. If the energy concentration of the frequency component of the high-frequency disturbance is detected in the time-frequency distribution characteristics, the filtered current sequence is decomposed by the wavelet transform algorithm to obtain the high-frequency component sequence; Based on the high-frequency component sequence, the temporal energy distribution is calculated to determine the temporal characteristics of the disturbance; If the duration of the disturbance time feature exceeds the preset threshold, the high-frequency component sequence is analyzed by the preset classification model to determine the power grid operating status. Based on the grid operation status, status assessment data is generated to determine the grid connection stability of the wind farm.

4. The method for suppressing harmonics in the grid-connected current of a virtual synchronous generator according to claim 1, characterized in that: S3 includes: If the amplitude of the high-frequency harmonic components exceeds the preset threshold, the original current sequence is decomposed in the frequency domain using the fast Fourier transform algorithm to obtain the set of frequency components. Based on the frequency component set, the intermittent perturbation sequence is extracted using spectral analysis to generate the harmonic sequence to be suppressed; The harmonic sequence to be suppressed is processed by a bandpass filter to obtain the filtered harmonic sequence. If the energy of the filtered harmonic sequence is concentrated in the frequency range of high-frequency disturbances, then a short-time Fourier transform algorithm is used for time-frequency analysis to obtain the time-frequency distribution characteristics. Based on the time-frequency distribution characteristics, the energy concentration sequence is extracted to generate a high-frequency perturbation sequence; The high-frequency disturbance sequence is analyzed by a pre-set classification model to determine the power grid operating status and obtain status assessment data. Based on the state assessment data, an adaptive filtering algorithm is used to suppress the high-frequency disturbance sequence and generate a stable current sequence.

5. The method for suppressing harmonics in the grid-connected current of a virtual synchronous generator according to claim 1, characterized in that: S4 includes: By using the subset of harmonics to be suppressed, the filter parameters are dynamically adjusted using the least mean square adaptive filtering algorithm to generate a compensation current sequence. Based on the compensation current sequence, the signal is decomposed using discrete Fourier transform to obtain the set of frequency components; If there are components in the frequency component set that are in the high-frequency disturbance frequency range, then the harmonic sequence of that frequency range is extracted by a bandpass filter to obtain the filtered harmonic sequence. Based on the filtered harmonic sequence, the time-frequency distribution is analyzed using short-time Fourier transform to obtain the time-frequency characteristic sequence; The power grid status is determined by using a pre-defined classification model based on time-frequency feature sequences, thus obtaining status assessment data. If the state assessment data indicates an abnormal power grid state, the filtered harmonic sequence is processed a second time using the least mean square adaptive filtering algorithm to generate a stable current sequence. Based on the stable current sequence, the time-domain signal is reconstructed through inverse Fourier transform to obtain the optimized current sequence.

6. The method for suppressing harmonics in the grid-connected current of a virtual synchronous generator according to claim 1, characterized in that: S5 includes: Phase adjustment parameters and amplitude scaling parameters are extracted from the compensation current sequence. The signal is decomposed using Fast Fourier Transform to obtain phase and amplitude features, thus yielding the phase adjustment parameters and amplitude scaling parameters. Based on the phase adjustment parameters and amplitude scaling parameters, the phase difference and amplitude ratio between the compensated current sequence and the original digital current sequence are calculated. The cosine similarity algorithm is used to evaluate the matching degree between the two, and the matching degree value is obtained. If the matching degree value is higher than the preset threshold, the compensation current sequence is fused into the main control loop through weighted signal superposition to obtain the fused current signal; For the fused current signal, a filter is used to remove noise components to obtain a denoised current signal; Based on the denoised current signal, the time-domain statistical characteristics of the signal are calculated, and the signal stability is judged using a support vector machine classification model to obtain the stability evaluation result; If the stability assessment result indicates that the signal is unstable, the denoised current signal is adjusted a second time using the least mean square adaptive filtering algorithm to obtain a stable current signal; Based on the stable current signal, the time domain signal is reconstructed using inverse fast Fourier transform to obtain the optimized current signal.

7. The method for suppressing grid-connected current harmonics of a virtual synchronous generator according to claim 1, characterized in that: S6 includes: The time-domain characteristics of the optimized current signal are obtained, and a digital signal processor is used for real-time calculation to obtain the suppression instruction sequence. By suppressing the command sequence, the deviation from the main control loop is calculated using a feedback loop, and a deviation correction sequence is obtained. If the amplitude of the deviation correction sequence exceeds the preset threshold, the Kalman filter algorithm is used to optimize the suppression command sequence to obtain an optimized pulse sequence. Based on the optimized pulse sequence, the statistical characteristics of the time series are calculated, and the stability of the sequence is judged by the support vector machine classification model to obtain the stability judgment result. Based on the stability assessment results, unstable sequence segments are extracted, and a second adjustment is performed using an adaptive filtering algorithm to obtain a stable pulse sequence. Based on the stable pulse sequence, a directional suppression pulse sequence is generated, and a digital signal processor is used for real-time output to obtain the final pulse sequence. The frequency distribution characteristics of the final pulse sequence are calculated, and the frequency consistency is verified by using Fast Fourier Transform to obtain the verification pulse sequence. Network transmission data packets are extracted from directional pulse sequences, and the data packet delay is calculated using timestamp analysis to obtain the delay judgment result; If the delay judgment result is lower than the preset threshold, the network transmission data packet is encrypted using the encryption standard algorithm to obtain an encrypted data packet; Based on the encrypted data packets, the transmission control protocol optimization strategy is used to adjust the data packet sending order and generate an optimized data stream. By optimizing the data flow and using the sliding window protocol to analyze network bandwidth usage, bandwidth allocation parameters can be obtained. Based on the bandwidth allocation parameters, a flow control algorithm is used to adjust the data packet transmission rate and generate an adjusted data stream. Remote adjustment parameters are extracted from the adjusted data stream, and a compressed parameter set is generated using a data compression algorithm to obtain the remote adjustment parameter set. By remotely adjusting the parameter set and using a verification algorithm to verify the integrity of the parameter set, a verification parameter set is obtained.

8. A virtual synchronous generator grid-connected current harmonic suppression system, used to implement the steps of the virtual synchronous generator grid-connected current harmonic suppression method according to any one of claims 1-7, characterized in that, The system includes: The sampling module is used to digitally sample intermittent high-frequency disturbances from the grid-connected current signal of the wind farm in the power grid using an analog-to-digital converter to obtain the original digital current sequence. The spectrum analysis module is used to analyze the wind load frequency components based on the original digital current sequence using the Fast Fourier Transform algorithm, and to determine the amplitude and phase characteristics of the high-frequency harmonic components. The perturbation extraction module is used to extract intermittent perturbation harmonic sequences from frequency components when the amplitude of high-frequency harmonic components exceeds a preset threshold, thereby obtaining a subset of harmonics to be suppressed. The filter control module is used to dynamically adjust the filter parameters using an adaptive filtering algorithm based on the subset of harmonics to be suppressed, and generate a compensation current sequence. The matching calculation module is used to obtain phase matching adjustment and amplitude scaling parameters from the compensation current sequence, and to determine the matching degree between the compensation current sequence and the original digital current sequence. If the matching degree is higher than the threshold, the compensation current sequence is fused into the main control loop through signal superposition operation and noise suppression filtering to obtain the optimized current signal. The digital signal processing module is used to calculate the suppression command in real time based on the optimized current signal using a digital signal processor, while performing loop feedback integration and stability verification checks to generate a directional suppression pulse sequence. The remote transmission module is used to acquire network transmission data packets, determine the latency threshold, and evaluate the network bandwidth. If the latency is lower than the threshold, the data packets are encrypted and the transmission protocol is optimized before being sent to the remote server to obtain a set of remote adjustment parameters. The parameter update module is used to extract feedback values ​​from the remote adjustment parameter set, update the local filter parameters by parsing the server response and downloading the parameter set, and use the proportional-integral-derivative control algorithm combined with the error retransmission mechanism and feedback value extraction processing to generate the final suppressed output sequence. The stability monitoring module is used to determine whether the stability index of intermittent disturbances in the grid of remote wind farms meets the requirements based on the final suppressed output sequence. If it does, it will loop back to the sampling process of the original digital current sequence to maintain continuous monitoring.

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