Wind power plant synchronous resonance suppression method and system

Abstract

The invention relates to the technical field of synchronous resonance suppression, and discloses a synchronous resonance suppression method and system for a wind power plant. The method comprises the following steps: performing group vibration characteristic decomposition processing on system impedance characteristics and key frequency monitoring points to obtain a fan group disturbance sensitivity index and an oscillation source positioning result; performing impedance reconstruction processing on the disturbance sensitivity index and the grading early warning threshold parameter to obtain a fan group cooperative damping parameter and a power dynamic distribution coefficient; and performing resonance link tracking processing on the oscillation source positioning result, the fan group disturbance sensitivity index and the power dynamic distribution coefficient to obtain a key branch impedance compensation value and a reactive power regulation coefficient, and performing closed-loop correction processing to obtain a suppression control instruction and a dynamic correction parameter. According to the invention, through establishing a complete technical scheme of resonance characteristic identification, risk assessment, group vibration analysis and cooperative control, accurate identification and effective suppression of a resonance problem in a wind power plant range are realized.

Description

Technical Field

[0001] The present application relates to the field of synchronous resonance suppression, and in particular to a method for suppressing synchronous resonance in a wind farm and a system thereof. Background Art

[0002] Wind farms are an important part of the development of clean energy, but with the continuous expansion of wind farm scale and the continuous increase in grid-connected capacity, the problem of synchronous resonance has become increasingly prominent. Existing wind farm synchronous resonance suppression technologies mainly include passive damping method, active damping method and hybrid damping method. The passive damping method increases system damping by adding resistive elements to the system, and has the characteristics of simple structure and high reliability; the active damping method simulates virtual impedance through control strategies to suppress resonance, and has the advantages of strong flexibility and good adaptability; the hybrid damping method combines the characteristics of passive and active damping, and to a certain extent achieves the improvement of resonance suppression effect.

[0003] However, the existing technology has the following deficiencies: First, traditional resonance suppression methods are often designed for a single wind turbine or a local area, lacking systematic consideration of the group vibration characteristics within the entire wind farm, resulting in unsatisfactory suppression effects under complex working conditions; second, existing suppression strategies mostly use fixed parameter control, which is difficult to adapt to the dynamic changes in wind farm operating conditions, and is prone to problems of over-suppression or under-suppression; third, research on resonance source positioning and propagation path analysis is not in-depth enough, making it difficult to accurately locate and effectively control resonance problems; fourth, there is a lack of in-depth analysis of the mutual influence between wind turbine groups, resulting in unsatisfactory results in coordinated control. Summary of the invention

[0004] The present application provides a method and system for suppressing synchronous resonance of a wind farm, which is used to solve the technical problem that the prior art lacks systematic consideration of the group vibration characteristics of wind farms, resulting in unsatisfactory resonance suppression effects. By establishing a complete technical solution for resonance characteristic identification, risk assessment, group vibration analysis, and coordinated control, accurate identification and effective suppression of resonance problems within the wind farm are achieved.

[0005] In a first aspect, the present application provides a method for suppressing synchronous resonance of a wind farm, the method comprising: Perform resonance characteristic identification processing on the electrical quantity data of each node in the wind farm to obtain the resonance frequency interval parameters and system impedance characteristics; Performing risk classification processing on the resonance frequency interval parameters to obtain graded warning threshold parameters and key frequency monitoring points; Performing group vibration characteristic decomposition processing on the system impedance characteristics and the key frequency monitoring points to obtain a wind turbine group disturbance sensitivity index and an oscillation source positioning result; Performing impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain a wind turbine group coordinated damping parameter and a power dynamic allocation coefficient; Performing resonant link tracking processing on the oscillation source positioning result, the wind turbine group disturbance sensitivity index and the power dynamic allocation coefficient to obtain a key branch impedance compensation value and a reactive power adjustment coefficient; The wind turbine group coordinated damping parameters, the key branch impedance compensation value and the reactive power regulation coefficient are subjected to closed-loop correction processing to obtain suppression control instructions and dynamic correction parameters.

[0006] In a second aspect, the present application provides a wind farm synchronous resonance suppression system, the wind farm synchronous resonance suppression system comprising: An identification module is used to perform resonance characteristic identification processing on the electrical quantity data of each node of the wind farm to obtain the resonance frequency interval parameters and system impedance characteristics; A classification module is used to classify the resonant frequency interval parameters into risk levels to obtain graded warning threshold parameters and key frequency monitoring points; A decomposition module, used to perform group vibration characteristic decomposition processing on the system impedance characteristics and the key frequency monitoring points to obtain a wind turbine group disturbance sensitivity index and an oscillation source positioning result; A reconstruction module, used to perform impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain a wind turbine group coordinated damping parameter and a power dynamic allocation coefficient; A tracking module, used to perform resonant link tracking processing on the oscillation source positioning result, the disturbance sensitivity index of the wind turbine group and the power dynamic allocation coefficient, so as to obtain a key branch impedance compensation value and a reactive power adjustment coefficient; The correction module is used to perform closed-loop correction processing on the wind turbine group coordinated damping parameters, the key branch impedance compensation value and the reactive power adjustment coefficient to obtain suppression control instructions and dynamic correction parameters.

[0007] In the technical solution provided by the present application, the resonance characteristics of the electrical quantity data of each node in the wind farm are identified and processed to obtain the resonance frequency interval parameters and the system impedance characteristics, thereby realizing a comprehensive and accurate identification of the resonance characteristics of the wind farm, and laying a data foundation for the subsequent suppression control; secondly, the resonance frequency interval parameters are divided into risk levels to obtain the graded warning threshold parameters and key frequency monitoring points, and a hierarchical risk warning mechanism is established, which can timely discover potential resonance risks; thirdly, the group vibration characteristics of the system impedance characteristics and the key frequency monitoring points are decomposed and processed to obtain the disturbance sensitivity index of the wind turbine group and the oscillation source positioning results, thereby realizing an in-depth analysis of the group vibration characteristics of the wind farm and the precise positioning of the oscillation source; further, The disturbance sensitivity index and the graded warning threshold parameters are processed by impedance reconstruction to obtain the wind turbine group coordinated damping parameters and power dynamic allocation coefficients, and a coordinated control strategy based on group characteristics is constructed to improve the integrity of the suppression effect; then, by performing resonant link tracking processing on the oscillation source positioning results, the wind turbine group disturbance sensitivity index and the power dynamic allocation coefficient, the key branch impedance compensation value and the reactive power adjustment coefficient are obtained, and the accurate tracking and directional suppression of the resonant propagation path are achieved; finally, the wind turbine group coordinated damping parameters, key branch impedance compensation values ​​and reactive power adjustment coefficients are processed by closed-loop correction to obtain suppression control instructions and dynamic correction parameters, and an adaptive closed-loop control mechanism is established to ensure real-time optimization and dynamic adjustment of the suppression strategy. Overall, the present invention realizes the systematic suppression of synchronous resonance in wind farms by constructing a complete technical system of resonance characteristic identification, risk assessment, group vibration analysis and coordinated control, improves the accuracy and reliability of the suppression effect, enhances the adaptability and robustness of the control strategy, and provides effective guarantees for the safe and stable operation of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a method for suppressing synchronous resonance of a wind farm in an embodiment of the present application; Figure 2 It is a schematic diagram of an embodiment of a wind farm synchronous resonance suppression system in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiment of the present application provides a method for suppressing synchronous resonance of a wind farm and a system thereof. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for suppressing synchronous resonance of a wind farm in the embodiment of the present application includes: Step S101, performing resonance characteristic identification processing on the electrical quantity data of each node of the wind farm to obtain resonance frequency interval parameters and system impedance characteristics; Step S102, performing risk level classification processing on the resonance frequency interval parameters to obtain graded warning threshold parameters and key frequency monitoring points; Step S103, performing group vibration characteristic decomposition processing on the system impedance characteristics and key frequency monitoring points to obtain the wind turbine group disturbance sensitivity index and oscillation source positioning result; Step S104, performing impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain the wind turbine group coordinated damping parameter and the power dynamic allocation coefficient; Step S105, performing resonant link tracking processing on the oscillation source positioning result, the disturbance sensitivity index of the wind turbine group and the power dynamic allocation coefficient to obtain the key branch impedance compensation value and the reactive power adjustment coefficient; Step S106: Perform closed-loop correction processing on the wind turbine group coordinated damping parameters, key branch impedance compensation values, and reactive power regulation coefficients to obtain suppression control instructions and dynamic correction parameters.

[0012] It is understandable that the execution subject of the present application may be a wind farm synchronous resonance suppression system, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, the resonance characteristic identification process is performed, and the voltage and current data of each node in the wind farm are collected, and the sampled data is synchronously timestamped to ensure the time sequence correspondence of the data. The voltage and current data here contain amplitude and phase angle information. After the voltage data is standardized, the voltage frequency component and amplitude characteristic quantity are obtained through spectrum analysis. The correlation analysis is performed on the collected voltage frequency components, and the coupling index between nodes is calculated. For example, when the voltage frequency components collected at the outlet of a certain wind turbine are 49.8Hz, 50.2Hz and 50.5Hz, the coupling relationship between nodes is determined by calculating the amplitude ratio and phase difference of adjacent frequency components. Similarly, the current data is also processed in the same way, and the branch impedance characteristic quantity is obtained by analyzing the correlation of the current frequency components. Based on the coupling degree index between nodes and the branch impedance characteristic quantity, the resonance modal analysis is performed to identify the candidate resonance frequency interval. The target resonance frequency interval is determined by segmenting the candidate resonance frequency interval. In the target resonant frequency range, the impedance characteristic curve of each node is calculated by combining the voltage and current frequency component information, and then the system impedance characteristics and resonant frequency range parameters are extracted.

[0014] For the obtained resonant frequency interval parameters, the risk intensity statistics are performed to obtain the frequency band risk distribution sequence. Through the segmented grade calculation processing, the risk level is divided into different levels to obtain the initial value of risk classification. The boundary point of the initial value of risk classification is calibrated to determine the demarcation point of the risk level, and the graded threshold reference group is obtained through threshold processing. For example, the risk level is divided into three levels: low risk (0-30%), medium risk (30%-70%) and high risk (70%-100%), and the corresponding threshold reference values ​​are 0.3, 0.7 and 1.0 respectively. After parameter correction and dynamic correction, the graded warning threshold parameters are finally obtained. At the same time, the sensitivity analysis of the resonant frequency interval parameters is performed to obtain the resonant frequency sensitivity matrix, and the monitoring point evaluation index is determined by characteristic decomposition, and finally the key frequency monitoring points are screened out. After obtaining the system impedance characteristics and key frequency monitoring points, the group vibration characteristics are decomposed. First, the system impedance characteristics are divided into groups, and the wind turbines with similar characteristics are divided into the same group, and the inter-group coupling matrix is ​​calculated. Combined with the information of key frequency monitoring points, the group vibration characteristic vector is extracted, and the group vibration modal parameters are obtained through modal decomposition. The sensitivity analysis and normalization of the group vibration modal parameters are performed to obtain the disturbance sensitivity index of the wind turbine group. At the same time, through time delay calculation and energy flow analysis, the oscillation propagation sequence and oscillation energy distribution matrix are identified, and then the oscillation source positioning result is determined.

[0015] Based on the disturbance sensitivity index and the hierarchical warning threshold parameters, impedance reconstruction processing is performed. The disturbance sensitivity index is sorted to obtain the sensitivity level sequence, which is matched with the hierarchical warning threshold parameters to form a sensitivity threshold matrix. By analyzing the impedance characteristics and parameter decomposition, a damping characteristic set is obtained, and then damping distribution and dynamic coordination are performed to finally obtain the wind turbine group coordinated damping parameters. At the same time, the power characteristics are calculated based on the damping reference sequence, and the power dynamic allocation coefficient is obtained after distributed analysis and dynamic calibration. Resonant link tracking is performed based on the oscillation source positioning results, wind turbine group disturbance sensitivity index and power dynamic allocation coefficient. The oscillation propagation path is determined by link association processing, and hierarchical division is performed to obtain the link hierarchical sequence. Combined with the power dynamic allocation coefficient, feature matching and node identification are performed to determine the key node set. Branch analysis and benchmark calculation are performed on the key node set to obtain the branch impedance benchmark value, and then the compensation amount is calculated and the difference correction is performed to obtain the key branch impedance compensation value. The reactive power regulation coefficient is finally obtained through reactive power characteristic analysis and coefficient calibration.

[0016] Finally, the wind turbine group coordinated damping parameters, key branch impedance compensation values ​​and reactive power regulation coefficients are corrected in a closed loop. First, the parameters are combined and matched to obtain a parameter combination sequence. The control reference interval is determined through stability evaluation and interval division. After parameter calibration and response characteristic analysis, the control reference value and response characteristic parameters are generated. Control instructions are generated based on the response characteristic parameters, and the validity of the control instructions is ensured through closed-loop verification. Finally, the dynamic correction parameters are obtained through parameter correction and dynamic analysis.

[0017] For example, a wind farm contains 50 wind turbines. The voltage frequency components collected at the outlet of wind turbine No. 1 are mainly concentrated at 49.8Hz, 50.2Hz and 50.5Hz, and the voltage amplitudes are 35kV, 0.8kV and 0.5kV respectively. Through spectrum analysis and correlation calculation, the coupling index with the adjacent wind turbine is 0.85. Combined with current data analysis, the impedance characteristic of the branch is calculated to be 0.4Ω. After risk level classification, the risk level of this frequency interval is medium risk, and the corresponding warning threshold is 0.7. Through group vibration characteristic decomposition, it is determined that the disturbance sensitivity index of the wind turbine is 0.65, which belongs to a relatively sensitive wind turbine group. Based on impedance reconstruction, the cooperative damping parameter of the wind turbine is calculated to be 0.3, and the power dynamic allocation coefficient is 0.8. In the resonant link tracking, it is identified that the propagation level between the wind turbine and the oscillation source is 2, the required impedance compensation value is 0.2Ω, and the reactive power adjustment coefficient is 0.4. Finally, through closed-loop correction, the suppression control instruction generated is to reduce the power output by 15%, and the dynamic correction parameter is 0.25.

[0018] In the embodiment of the present application, the electrical quantity data of each node of the wind farm is processed for resonance characteristic identification to obtain the resonance frequency interval parameters and system impedance characteristics, thereby realizing comprehensive and accurate identification of the resonance characteristics of the wind farm, and laying a data foundation for subsequent suppression control; secondly, the resonance frequency interval parameters are processed for risk level classification to obtain graded warning threshold parameters and key frequency monitoring points, and a hierarchical risk warning mechanism is established, which can timely discover potential resonance risks; thirdly, the group vibration characteristics of the system impedance characteristics and key frequency monitoring points are processed for group vibration characteristic decomposition to obtain the wind turbine group disturbance sensitivity index and oscillation source positioning results, thereby realizing in-depth analysis of the wind farm group vibration characteristics and precise positioning of the oscillation source; further, the disturbance The sensitivity index and the graded warning threshold parameters are processed by impedance reconstruction to obtain the wind turbine group coordinated damping parameters and power dynamic allocation coefficients, and a coordinated control strategy based on group characteristics is constructed to improve the integrity of the suppression effect; then, by performing resonant link tracking processing on the oscillation source positioning results, wind turbine group disturbance sensitivity index and power dynamic allocation coefficient, the key branch impedance compensation value and reactive power adjustment coefficient are obtained, and accurate tracking and directional suppression of the resonant propagation path are achieved; finally, the wind turbine group coordinated damping parameters, key branch impedance compensation values ​​and reactive power adjustment coefficients are processed by closed-loop correction to obtain suppression control instructions and dynamic correction parameters, and an adaptive closed-loop control mechanism is established to ensure real-time optimization and dynamic adjustment of the suppression strategy. Overall, the present invention realizes the systematic suppression of synchronous resonance in wind farms by constructing a complete technical system of resonance characteristic identification, risk assessment, group vibration analysis and coordinated control, improves the accuracy and reliability of the suppression effect, enhances the adaptability and robustness of the control strategy, and provides effective guarantees for the safe and stable operation of wind farms.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Sampling the voltage data of each node in the wind farm to obtain a voltage amplitude sequence and a phase angle sequence, and performing synchronous time stamp marking on the voltage amplitude sequence and the phase angle sequence to obtain a standardized voltage data group; (2) Perform spectrum analysis on the standardized voltage data group to obtain the voltage frequency components and amplitude characteristics of each node, and perform correlation analysis on the voltage frequency components to obtain the node-to-node coupling index; (3) Sampling the current data of each node in the wind farm to obtain a current amplitude sequence and a phase angle sequence, and performing synchronous time stamp marking on the current amplitude sequence and the phase angle sequence to obtain a standardized current data group; (4) Performing spectrum analysis on the standardized current data group to obtain the current frequency components and amplitude characteristics of each node, and performing correlation analysis on the current frequency components to obtain the branch impedance characteristics; (5) Perform resonant modal analysis on the node coupling index and branch impedance characteristic quantity to obtain the candidate resonant frequency interval, and perform segmented screening on the candidate resonant frequency interval to obtain the target resonant frequency interval; (6) Perform impedance calculation processing on the voltage frequency component and the current frequency component within the target resonant frequency range to obtain the impedance characteristic curve of each node, and perform feature extraction processing on the impedance characteristic curve of each node to obtain the system impedance characteristic; (7) Perform threshold analysis on the system impedance characteristics to obtain the resonant frequency range parameters.

[0020] Specifically, the voltage data of each key node in the wind farm is collected, including the wind turbine outlet, the busbar of the booster station, the grid connection point and other locations. Through the high-precision synchronous sampling device, the acquisition frequency is set to 10kHz, and the sampling time is 10 seconds, and the voltage amplitude sequence and phase angle sequence are obtained. For the collected raw data, synchronous timestamp marking is performed, that is, a time label accurate to the microsecond level is added to each sampling point to ensure the timing of the data. Then the data is normalized, and the voltage amplitude is converted to the range of 0-1 to obtain a standardized voltage data group. Fast Fourier transform (FFT) analysis is performed on the standardized voltage data to calculate the spectrum distribution. In the spectrum analysis, the frequency resolution is set to 0.1Hz, and the analysis range is 0-1000Hz, and the voltage frequency components and corresponding amplitude characteristics of each node are obtained. The obtained voltage frequency components are subjected to correlation analysis, and the coupling index between nodes is obtained by calculating the amplitude ratio and phase difference of the same frequency components between different nodes. This index reflects the degree of correlation between voltage fluctuations between different nodes. The same sampling method is used to obtain the current data of each node. Similarly, 10 seconds of data are collected at a sampling frequency of 10kHz to record the current amplitude sequence and phase angle sequence. A synchronous timestamp is also added to the current data, and normalization is performed to obtain a standardized current data group. The standardization process ensures the consistency of voltage and current data, which is convenient for subsequent analysis. FFT analysis is also performed on the standardized current data group to obtain the frequency components and amplitude characteristics of the current. By analyzing the correlation of the current frequency components between different nodes and combining the voltage data analysis results, the branch impedance characteristic is calculated. The branch impedance characteristic reflects the impedance characteristics of each branch in the wind farm network and provides basic data for subsequent resonance analysis.

[0021] Based on the coupling index between nodes and the characteristic quantity of branch impedance, the resonance modal analysis is carried out. First, the state space model of the system is established, and the natural frequency and oscillation mode of the system are obtained by eigenvalue decomposition. By analyzing the damping characteristics and participation factors of the mode, the candidate interval of the resonance frequency is identified. Then, the candidate interval is segmented and screened, and the target resonance frequency interval with actual resonance risk is screened according to the characteristics such as damping ratio and energy distribution. In the determined target resonance frequency interval, the impedance of the voltage frequency component and the current frequency component is calculated. By calculating the ratio of voltage to current at each frequency point, the impedance characteristic curve of each node is obtained. The feature extraction of the impedance characteristic curve includes analyzing the peak point, valley point, inflection point and other characteristics of the curve, and considering the change trend of impedance amplitude and phase angle, the impedance characteristics of the system are finally obtained. Finally, the threshold analysis of the impedance characteristics of the system is carried out. By setting the thresholds of parameters such as impedance amplitude and phase angle change rate, the resonance risk level of different frequency intervals is divided, and the resonance frequency interval parameters are obtained.

[0022] For example: At the outlet of wind turbine No. 1 of a certain wind farm, the sampled voltage data was standardized and FFT analysis was performed to find that there were obvious frequency components at 49.8Hz, 50.2Hz and 50.5Hz, with amplitudes of 0.95, 0.03 and 0.02 (normalized values), respectively. Through correlation analysis, the coupling index between this node and the adjacent wind turbine No. 2 at 50.2Hz was calculated to be 0.82, indicating that the two nodes have a strong coupling relationship at this frequency point. At the same time, current data analysis shows that the amplitudes of the current components at the same frequency points are 0.88, 0.04 and 0.03, and the impedance characteristic value of this branch at 50.2Hz is calculated to be 0.75. Through resonant modal analysis, 48-52Hz is identified as the candidate resonant frequency interval, of which 50.2Hz±0.3Hz is the target resonant frequency interval. In this range, the impedance characteristic curve shows that the impedance amplitude reaches a peak value of 3.5 at 50.2Hz, and the phase angle changes by -180 degrees. After extracting the features, the system impedance characteristic value of this node is 2.8. Finally, through threshold analysis, the 50.2Hz±0.3Hz frequency range is divided into a high-risk resonance range, and the resonance frequency range parameters are set to [49.9Hz, 50.5Hz].

[0023] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Perform risk intensity statistical processing on the resonance frequency interval parameters to obtain the frequency band risk distribution sequence, and perform segmented grade calculation processing on the frequency band risk distribution sequence to obtain the initial value of risk grading; (2) Perform boundary point calibration on the initial value of risk classification to obtain the risk level cutoff point, and perform threshold processing on the risk level cutoff point to obtain the classification threshold benchmark group; (3) Performing parameter correction processing on the graded threshold reference group to obtain the warning threshold reference value, and dynamically correcting the warning threshold reference value to obtain the graded warning threshold parameter; (4) Perform frequency sensitivity analysis on the resonant frequency interval parameters to obtain a resonant frequency sensitivity matrix, and perform characteristic decomposition on the resonant frequency sensitivity matrix to obtain monitoring point evaluation indicators; (5) Prioritize the monitoring point evaluation indicators to obtain a candidate sequence of monitoring points, and screen the candidate sequence of monitoring points to obtain key frequency monitoring points.

[0024] Specifically, the risk intensity of the resonance frequency interval parameters is statistically processed, and the risk intensity value of each frequency point is calculated by statistically analyzing the characteristics of the resonance amplitude, duration and frequency of occurrence at each frequency point. For continuous frequency intervals, they are divided according to the frequency interval of 0.1Hz, and the risk characteristic parameters in each small interval are statistically calculated to form a frequency band risk distribution sequence. The obtained frequency band risk distribution sequence is segmented and graded. Based on the distribution characteristics of the risk intensity value, the cluster analysis method is used to preliminarily divide the risk level into three levels: low risk, medium risk and high risk, and the initial value of risk classification is obtained. For the initial value of risk classification, the boundary point calibration processing is performed. First, the transition area between adjacent risk levels is identified, and the best demarcation point is found in the transition area. By calculating the gradient change of the risk intensity value, the point with the largest gradient change is selected as the risk level demarcation point. After determining the demarcation point, it is thresholded, the risk intensity value is mapped to the 0-1 interval, and the classification threshold reference group is set. Among them, the demarcation threshold from low risk to medium risk is set to 0.3, and the demarcation threshold from medium risk to high risk is set to 0.7.

[0025] The parameters of the graded threshold benchmark group are corrected, and the benchmark threshold is adjusted considering the actual operating status and historical resonance data of the wind farm. By analyzing the risk level distribution in the historical resonance events, the statistical characteristics of each level, including the mean, variance, etc., are calculated, and the warning threshold reference value is obtained accordingly. At the same time, combined with the real-time operation data of the wind farm, the warning threshold reference value is dynamically corrected, and the threshold parameters are adjusted in real time according to factors such as load level and wind condition changes, and finally the graded warning threshold parameters are obtained. While determining the warning threshold, the frequency sensitivity analysis of the resonance frequency interval parameters is performed. By calculating the influence of each frequency point on the system resonance characteristics, a frequency sensitivity matrix is ​​established. The specific analysis method includes: calculating the resonance amplification factor of each frequency point, the influence on the system damping, and the coupling strength with other frequency points. These features are organized into a matrix form, in which the matrix elements represent the sensitivity relationship between different frequency points. The sensitivity matrix is ​​subjected to eigenvalue decomposition, the main eigenvectors are extracted, and the monitoring point evaluation index reflecting the importance of each frequency point is obtained.

[0026] Prioritize the monitoring point evaluation indicators. First, sort the evaluation indicators by numerical value to establish a preliminary candidate sequence of monitoring points. Considering the spatial distribution and coverage of the monitoring points, set the screening rules: the frequency interval between adjacent monitoring points is not less than 0.5Hz, and the evaluation indicator value of the monitoring point is not less than the threshold. Filter the candidate sequence through the screening rules and finally determine the key frequency monitoring points.

[0027] For example: the resonant frequency interval of a wind farm is [49.5Hz, 50.5Hz]. Through risk intensity statistics, it is found that the resonance amplitude at 50.2Hz is an average of 15% of the rated value, the probability of duration exceeding 1 minute is 30%, and the frequency of occurrence is 2 times per hour. The interval is divided into 0.1Hz intervals to obtain the frequency band risk distribution sequence. After segmented level calculation, the risk intensity value below 0.4 is divided into low risk, 0.4-0.7 is medium risk, and above 0.7 is high risk. In the boundary point calibration, by calculating the gradient of the risk intensity value, it is determined that there is an obvious gradient change at the risk intensity values ​​of 0.45 and 0.75, and these two points are set as the dividing points. Considering the historical operation data, the low risk threshold is adjusted to 0.35 and the high risk threshold is adjusted to 0.8 to form a graded threshold benchmark group. In the frequency sensitivity analysis, it was calculated that the resonance magnification of the 50.2Hz frequency point was 2.5 times, the influence on the system damping was 0.6, the coupling strength with the adjacent frequency point was 0.4, and the comprehensive evaluation index value of this point was 0.85. After sorting the evaluation indicators of all frequency points, points with evaluation index values ​​greater than 0.7 and intervals of not less than 0.5Hz were selected as monitoring points, and 49.8Hz, 50.2Hz and 50.5Hz were finally determined as key frequency monitoring points.

[0028] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) The impedance characteristics of the system are divided into groups to obtain a set of impedance characteristic groups, and the mutual coupling calculation is performed on the set of impedance characteristic groups to obtain an inter-group coupling matrix; (2) Perform feature extraction on the inter-group coupling matrix and key frequency monitoring points to obtain the group vibration feature vector, and perform modal decomposition on the group vibration feature vector to obtain the group vibration modal parameters; (3) Perform sensitivity analysis on the group vibration modal parameters to obtain the node sensitivity sequence, and normalize the node sensitivity sequence to obtain the wind turbine group disturbance sensitivity index; (4) Perform time delay calculation on the group vibration modal parameters to obtain the oscillation propagation sequence, and perform energy flow analysis on the oscillation propagation sequence to obtain the oscillation energy distribution matrix; (5) Perform source point identification processing on the oscillation energy distribution matrix to obtain a candidate set of oscillation sources, and perform positioning analysis processing on the candidate set of oscillation sources to obtain the oscillation source positioning result.

[0029] Specifically, the impedance characteristics of the system are divided into groups, and cluster analysis is performed based on the similarity of the impedance characteristic curves. By calculating the Euclidean distance of the impedance amplitude and phase angle, wind turbines with similar characteristics are divided into the same group to form a set of impedance characteristic groups. The mutual coupling calculation is performed on the group set, the coupling strength between different groups is analyzed, the impedance mutual inductance coefficient between each group is calculated, and the inter-group coupling matrix is ​​constructed. Each element of the matrix represents the strength of the coupling relationship between different groups. Feature extraction processing is performed on the inter-group coupling matrix and the key frequency monitoring point data. The eigenvalues ​​and eigenvectors of the inter-group coupling matrix are calculated at each key frequency point to obtain the group vibration eigenvector reflecting the group oscillation characteristics. The group vibration eigenvector is modally decomposed, and the singular value decomposition method is used to extract the main oscillation mode, and the frequency, damping ratio and amplitude of each mode are calculated to form the group vibration modal parameters.

[0030] The sensitivity analysis of group vibration modal parameters is performed, and the node sensitivity sequence is obtained by calculating the participation factor of each node in the main vibration mode. This sequence reflects the influence of each node in the group vibration process. The node sensitivity sequence is normalized, and the sensitivity value is mapped to the range of 0-1 to obtain the standardized wind turbine group disturbance sensitivity index.

[0031] Delay calculation and feature association are the key steps of this solution, and their mathematical description is as follows: in, represents the oscillation propagation delay between nodes i and j; is the angular frequency of the kth oscillation mode; and are the phase angles of nodes i and j in the kth mode respectively; is the coherence coefficient of nodes i and j in the kth mode; is the weight coefficient of the kth mode; is the attenuation coefficient of the kth mode; t is the observation time; N is the total number of modes considered.

[0032] The oscillation propagation sequence is obtained through time delay calculation, and then the energy flow direction of the sequence is analyzed, the energy transmission direction and intensity between each node are calculated, and the oscillation energy distribution matrix is ​​formed. This matrix describes the propagation law of oscillation energy in the wind farm. The oscillation energy distribution matrix is ​​processed for source point identification, and nodes with positive net energy output exceeding the threshold are screened and included in the oscillation source candidate set. By calculating the energy contribution rate and influence range of each candidate point, positioning analysis is performed, and the oscillation source positioning result is finally determined.

[0033] For example: A wind farm contains 30 wind turbines, which are divided into 3 groups based on impedance characteristic cluster analysis, each group contains 10 wind turbines. By calculating the mutual inductance coefficient of the impedance between groups, a 3×3 inter-group coupling matrix is ​​obtained, and the matrix element value range is between 0.2-0.8. Feature extraction is performed at the key frequency point 50.2Hz, and the group vibration eigenvector with eigenvalues ​​of [2.5, 1.8, 0.7] is obtained. After modal decomposition, three main oscillation modes are obtained, with frequencies of 50.2Hz, 50.4Hz and 50.1Hz, and the corresponding damping ratios are 0.03, 0.05 and 0.08. Through sensitivity analysis, it is calculated that the participation factor of wind turbine No. 1 to the dominant mode is 0.85, and its disturbance sensitivity index is 0.92 after normalization, indicating that the wind turbine plays an important role in the group vibration process. Delay calculation shows that the oscillation propagation delay between wind turbines 1 and 2 is 15ms, and energy flow analysis shows that the energy output ratio of wind turbine 1 to surrounding wind turbines is 1.5, exceeding the threshold of 1.2, so it is included in the candidate set of oscillation sources. After positioning analysis, wind turbine 1 is confirmed to be the oscillation source, and its influence range covers 5 adjacent wind turbines.

[0034] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) The disturbance sensitivity index is sorted to obtain a sensitivity level sequence, and the sensitivity level sequence is matched with the graded warning threshold parameters to obtain a sensitivity threshold matrix; (2) Perform impedance characteristic analysis on the sensitivity threshold matrix to obtain impedance characteristic parameters, and perform parameter decomposition on the impedance characteristic parameters to obtain a damping characteristic set; (3) Perform damping distribution processing on the damping characteristic set to obtain a damping reference sequence, and perform dynamic coordination processing on the damping reference sequence to obtain the coordinated damping parameters of the wind turbine group; (4) Calculate the power characteristics of the damping reference sequence to obtain a power regulation reference value, and perform distributed analysis on the power regulation reference value to obtain a power allocation vector; (5) The power allocation vector is calibrated to obtain the allocation reference coefficient, and the allocation reference coefficient is dynamically calibrated to obtain the power dynamic allocation coefficient.

[0035] Specifically, the disturbance sensitivity index is sorted by sensitivity, and arranged in descending order based on the size of the disturbance sensitivity value to form a sensitivity level sequence. The sequence is matched with the graded warning threshold parameters to establish a two-dimensional sensitivity threshold matrix. The rows of the matrix represent different sensitivity levels, the columns represent different warning levels, and the matrix element values ​​reflect the control reference values ​​under the corresponding conditions. For the sensitivity threshold matrix, the impedance characteristic analysis is performed to extract the impedance amplitude and phase angle characteristics of each fan at different frequency points to obtain the impedance characteristic parameters. These parameters are decomposed to decompose the impedance characteristics into resistive components and inductive components, and the damping contribution capacity of each fan is comprehensively evaluated to form a damping characteristic set.

[0036] The damping distribution process is the core step of this scheme, and its mathematical description is as follows: in, is the damping distribution coefficient of the i-th fan; is the reference damping coefficient of the fan; is the weight factor of the jth resonance mode; is the participation of wind turbine i in mode j; is the attenuation coefficient of mode j; is the electrical distance from fan i to the resonance center j; is the rated capacity of fan i; is the energy density of mode j; M is the number of modes considered.

[0037] The damping distribution processing is performed on the damping characteristic set, and the damping reference sequence is calculated according to the above formula. This sequence reflects the damping responsibility that each wind turbine should bear. Through dynamic coordination processing, the mutual influence between wind turbines is considered, and the damping parameter configuration of each wind turbine is adjusted to finally obtain the coordinated damping parameters of the wind turbine group. Based on the damping reference sequence, the power characteristic calculation is performed, and the active power regulation capability and reactive power support capability of each wind turbine are analyzed to obtain the power regulation reference value. The power regulation reference value is subjected to distributed analysis, and the power sharing ratio of each wind turbine is calculated considering the spatial distribution and electrical distance of the wind turbine to form a power allocation vector.

[0038] The power distribution vector is calibrated, and the reference coefficient of power distribution is determined according to factors such as the operating status of the wind turbine and power generation efficiency. The reference coefficient is dynamically calibrated, and the power distribution ratio of each wind turbine is adjusted in real time to obtain the dynamic power distribution coefficient.

[0039] For example, a wind farm contains 20 wind turbines. Through sensitivity sorting, the disturbance sensitivity index is sorted from large to small. Among them, the sensitivity of wind turbine No. 1 is 0.95, ranking first. Combined with the warning threshold parameters (low risk 0.3, medium risk 0.7), a 4×3 sensitivity threshold matrix is ​​constructed. According to the impedance characteristic analysis, the impedance amplitude of wind turbine No. 1 at 50.2Hz is 2.5Ω, the phase angle is -45 degrees, and the equivalent resistive component is decomposed into 1.77Ω. Through damping distribution calculation, considering that the rated capacity of wind turbine No. 1 is 2MW, the electrical distance to the resonance center is 2 busbar sections, the weight factor of the dominant mode is 0.8, and the participation is 0.9, its damping distribution coefficient is calculated to be 0.85. After dynamic coordination, the coordinated damping parameter of the wind turbine is determined to be 0.75. The power characteristic calculation shows that the active regulation capacity of the wind turbine is 15% of the rated power, and the reactive support capacity is 30% of the rated capacity.

[0040] After distributed analysis, it was determined that the power sharing ratio of wind turbine No. 1 was 0.12. Considering its 90% power generation efficiency, the power allocation benchmark coefficient was calibrated to 0.108. Finally, through dynamic calibration, according to the real-time wind conditions, its power dynamic allocation coefficient was adjusted to 0.115 to guide the real-time power regulation of the wind turbine.

[0041] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Perform link association processing on the oscillation source positioning results and the disturbance sensitivity index of the wind turbine group to obtain the oscillation propagation path, and then perform hierarchical division processing on the oscillation propagation path to obtain the link hierarchy sequence; (2) Perform feature matching processing on the link level sequence and the power dynamic allocation coefficient to obtain the link impedance feature quantity, and perform node identification processing on the link impedance feature quantity to obtain the key node set; (3) Perform branch analysis on the key node set to obtain a branch impedance sequence, and perform benchmark calculation on the branch impedance sequence to obtain a branch impedance benchmark value; (4) Calculating the compensation amount of the branch impedance reference value to obtain the impedance compensation reference amount, and performing difference correction processing on the impedance compensation reference amount to obtain the key branch impedance compensation value; (5) Perform reactive characteristic analysis on the key branch impedance compensation value to obtain the reactive compensation demand, and perform coefficient calibration on the reactive compensation demand to obtain the reactive regulation coefficient.

[0042] Specifically, the oscillation source positioning results and the disturbance sensitivity index of the wind turbine group are processed by link association. Starting from the identified oscillation source node, the propagation path of the oscillation energy is tracked based on the electrical connection relationship between nodes and the disturbance propagation characteristics. According to the electrical distance and impedance characteristics between nodes, the priority path of oscillation propagation is determined to form a complete oscillation propagation path diagram. The oscillation propagation path is hierarchically divided, with the oscillation source as the first layer, and the nodes along the way are divided into different levels according to the propagation distance and attenuation characteristics to obtain a link level sequence. Feature matching processing is performed on the link level sequence and the power dynamic allocation coefficient. The power allocation characteristics of nodes at each level are analyzed, the impedance relationship between nodes is calculated, and the link impedance characteristic quantity is obtained. This characteristic quantity reflects the impedance coupling relationship between nodes on the oscillation propagation path. Node identification is performed on the link impedance characteristic quantity, and the key nodes with a greater impact on oscillation propagation are screened out according to the significance of the impedance characteristics and the importance of the node position to form a key node set.

[0043] Perform branch analysis on the key node set and calculate the branch impedance values ​​between nodes. Consider the electrical characteristics of the branch, including resistance, inductance and capacitance parameters, to obtain a complete branch impedance sequence. Perform a benchmark calculation on the branch impedance sequence, and determine the benchmark impedance value of each branch based on the impedance characteristics at the resonant frequency point. In the benchmark calculation process, consider the transmission capacity and stability margin requirements of the branch to obtain the branch impedance benchmark value. Perform compensation calculation on the branch impedance benchmark value, analyze the impedance deviation of each branch at the resonant frequency point, and calculate the required compensation amount. Normalize the compensation amount to obtain the impedance compensation benchmark amount. Perform difference correction on the impedance compensation benchmark amount, adjust the distribution ratio of the compensation amount according to the actual operating status and carrying capacity of the branch, and finally obtain the key branch impedance compensation value.

[0044] Reactive power characteristics analysis is performed for the impedance compensation values ​​of key branches. The reactive power injection required to achieve impedance compensation is calculated, and the reactive power compensation demand is obtained by considering the reactive power regulation capability of the wind turbine and the grid operation constraints. The reactive power compensation demand is calibrated, and the reactive power regulation coefficient is determined according to the compensation priority of each branch and the principle of nearest compensation.

[0045] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Perform parameter combination processing on the wind turbine group coordinated damping parameters and the key branch impedance compensation values ​​to obtain an initial set of control parameters, and perform matching processing on the initial set of control parameters and the reactive power regulation coefficient to obtain a parameter combination sequence; (2) Perform stability assessment on the parameter combination sequence to obtain a stability margin index, and divide the stability margin index into intervals to obtain a control reference interval; (3) Perform parameter calibration on the control reference interval to obtain the control reference value, and perform response characteristic analysis on the control reference value to obtain the response characteristic parameter; (4) performing instruction generation processing on the response characteristic parameter to obtain a control instruction reference value, and performing closed-loop verification processing on the control instruction reference value to obtain a suppression control instruction; (5) Performing parameter correction processing on the suppression control instruction to obtain a correction reference value, and performing dynamic analysis processing on the correction reference value to obtain a dynamic correction parameter.

[0046] Specifically, the wind turbine group coordinated damping parameters and key branch impedance compensation values ​​are processed by parameter combination, and multiple parameters of the same control object are integrated together. For each wind turbine, its coordinated damping parameters and the impedance compensation values ​​of the connected branches are weighted and combined according to the parameter weights to form an initial set of control parameters. The initial set is matched with the reactive power regulation coefficient, and the control parameters are corrected according to the reactive power regulation capability to obtain a parameter combination sequence. The parameter combination sequence is evaluated for stability, and the influence of various parameters on the system stability is analyzed. By calculating the small disturbance stability margin, the influence of the change of control parameters on the system characteristic value is evaluated to obtain the stability margin index. The index includes characteristic quantities such as damping ratio and oscillation frequency deviation. The stability margin index is divided into intervals. According to the stability requirements of the system, the control interval is divided into a safe interval, a warning interval and a dangerous interval to obtain a control reference interval.

[0047] Parameter calibration is performed for the control reference interval, and the control parameters of each interval are corrected in combination with the real-time operation data of the wind farm. The control reference value is obtained by calculating the damping characteristics and impedance characteristics under actual operating conditions. The response characteristics of the control reference value are analyzed, including calculating indicators such as response time, overshoot, and steady-state error to form response characteristic parameters. Instruction generation processing is performed based on the response characteristic parameters, and a step-by-step control strategy is generated according to the dynamic response characteristics of the system. The control strategy is quantified into specific numerical instructions to obtain the control instruction reference value. A closed-loop check is performed on the control instruction reference value, and the control effect is verified through simulation analysis to ensure the effectiveness of the control instruction, and finally the suppression control instruction is obtained.

[0048] The parameters of the suppression control instructions are corrected, and the control parameters are fine-tuned according to the real-time operation data. The correction reference value is obtained by calculating the control deviation and the correction coefficient. The correction reference value is dynamically analyzed, and the dynamic adjustment strategy of the parameters is determined considering the time-varying characteristics of the control parameters, and finally the dynamic correction parameters are obtained.

[0049] For example, the No. 1 wind turbine of a certain wind farm has a coordinated damping parameter of 0.75, an impedance compensation value of the connecting branch of 0.25Ω, and a reactive power regulation coefficient of 0.4. These parameters are combined according to the weight of 0.5:0.3:0.2 to obtain the initial control parameter of 0.53. After stability evaluation, it is calculated that the damping ratio of the system under this parameter is 0.05, the oscillation frequency deviation is 0.02, and the comprehensive stability margin index is 0.85. According to the stability margin index, the control interval is divided into three sections: safe interval (0.8-1.0), warning interval (0.6-0.8) and dangerous interval (0-0.6). Since the current stability margin of 0.85 is in the safe interval, the control reference value is initially determined to be 0.50. Through response characteristic analysis, it is calculated that under this control value, the system response time is 100ms, the overshoot is 5%, and the steady-state error is 2%. Based on the response characteristics, the generated control instructions include: adjusting the damping parameter to 0.70, the impedance compensation value to 0.22Ω, and the reactive power adjustment coefficient to 0.35. After closed-loop verification, this set of control instructions can reduce the system resonance amplitude by 80%. In the actual implementation process, it was found that the system response was slightly delayed, and the calculated correction coefficient was 1.1. The control parameters were adjusted to: damping parameter 0.77, impedance compensation value 0.24Ω, reactive power adjustment coefficient 0.38. The final dynamic correction parameter settings are: when the voltage fluctuation exceeds 5%, the damping parameter increases by 0.05, the impedance compensation value increases by 0.02Ω, and the reactive power adjustment coefficient increases by 0.03.

[0050] The above describes the method for suppressing synchronous resonance of a wind farm in the embodiment of the present application. The following describes the system for suppressing synchronous resonance of a wind farm in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the wind farm synchronous resonance suppression system includes: An identification module is used to perform resonance characteristic identification processing on the electrical quantity data of each node of the wind farm to obtain the resonance frequency interval parameters and system impedance characteristics; A classification module is used to classify the resonant frequency interval parameters into risk levels to obtain graded warning threshold parameters and key frequency monitoring points; A decomposition module, used to perform group vibration characteristic decomposition processing on the system impedance characteristics and the key frequency monitoring points to obtain a wind turbine group disturbance sensitivity index and an oscillation source positioning result; A reconstruction module, used to perform impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain a wind turbine group coordinated damping parameter and a power dynamic allocation coefficient; A tracking module, used to perform resonant link tracking processing on the oscillation source positioning result, the disturbance sensitivity index of the wind turbine group and the power dynamic allocation coefficient, so as to obtain a key branch impedance compensation value and a reactive power adjustment coefficient; The correction module is used to perform closed-loop correction processing on the wind turbine group coordinated damping parameters, the key branch impedance compensation value and the reactive power adjustment coefficient to obtain suppression control instructions and dynamic correction parameters.

[0051] Through the coordinated cooperation of the above-mentioned components, the resonance characteristics of the electrical quantity data of each node in the wind farm are identified and processed, and the resonance frequency range parameters and system impedance characteristics are obtained, which realizes the comprehensive and accurate identification of the resonance characteristics of the wind farm, and lays a data foundation for the subsequent suppression control; secondly, the resonance frequency range parameters are divided into risk levels, and the graded warning threshold parameters and key frequency monitoring points are obtained, and a hierarchical risk warning mechanism is established, which can timely discover potential resonance risks; thirdly, by decomposing the group vibration characteristics of the system impedance characteristics and key frequency monitoring points, the disturbance sensitivity index of the wind turbine group and the oscillation source positioning results are obtained, which realizes the in-depth analysis of the group vibration characteristics of the wind farm and the precise positioning of the oscillation source; further , the disturbance sensitivity index and the graded warning threshold parameters are processed by impedance reconstruction, and the wind turbine group coordinated damping parameters and power dynamic allocation coefficients are obtained, and a coordinated control strategy based on group characteristics is constructed to improve the integrity of the suppression effect; then, the resonance link tracking process is performed on the oscillation source positioning results, the wind turbine group disturbance sensitivity index and the power dynamic allocation coefficient, and the key branch impedance compensation value and reactive power adjustment coefficient are obtained, so as to achieve accurate tracking and directional suppression of the resonant propagation path; finally, the wind turbine group coordinated damping parameters, key branch impedance compensation values ​​and reactive power adjustment coefficients are processed by closed-loop correction, and suppression control instructions and dynamic correction parameters are obtained, and an adaptive closed-loop control mechanism is established to ensure real-time optimization and dynamic adjustment of the suppression strategy. Overall, the present invention realizes the systematic suppression of synchronous resonance of wind farms by constructing a complete technical system of resonance characteristic identification, risk assessment, group vibration analysis and coordinated control, improves the accuracy and reliability of the suppression effect, enhances the adaptability and robustness of the control strategy, and provides effective guarantee for the safe and stable operation of wind farms.

[0052] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for suppressing synchronous resonance of a wind farm, characterized in that: The method for suppressing synchronous resonance of a wind farm comprises: Perform resonance characteristic identification processing on the electrical quantity data of each node in the wind farm to obtain the resonance frequency interval parameters and system impedance characteristics; Performing risk classification processing on the resonance frequency interval parameters to obtain graded warning threshold parameters and key frequency monitoring points; Performing group vibration characteristic decomposition processing on the system impedance characteristics and the key frequency monitoring points to obtain a wind turbine group disturbance sensitivity index and an oscillation source positioning result; Performing impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain a wind turbine group coordinated damping parameter and a power dynamic allocation coefficient; Performing resonant link tracking processing on the oscillation source positioning result, the wind turbine group disturbance sensitivity index and the power dynamic allocation coefficient to obtain a key branch impedance compensation value and a reactive power adjustment coefficient; The wind turbine group coordinated damping parameters, the key branch impedance compensation value and the reactive power regulation coefficient are subjected to closed-loop correction processing to obtain suppression control instructions and dynamic correction parameters.

2. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The resonant characteristic identification processing is performed on the electrical quantity data of each node of the wind farm to obtain the resonant frequency interval parameters and the system impedance characteristics, including: Sampling the voltage data of each node of the wind farm to obtain a voltage amplitude sequence and a phase angle sequence, and performing synchronous time stamp marking on the voltage amplitude sequence and the phase angle sequence to obtain a standardized voltage data group; Performing spectrum analysis on the standardized voltage data group to obtain voltage frequency components and amplitude characteristic quantities of each node, and performing correlation analysis on the voltage frequency components to obtain an index of coupling degree between nodes; Sampling the current data of each node of the wind farm to obtain a current amplitude sequence and a phase angle sequence, and performing synchronous time stamp marking on the current amplitude sequence and the phase angle sequence to obtain a standardized current data group; Performing spectrum analysis on the standardized current data group to obtain the current frequency components and amplitude characteristic quantities of each node, and performing correlation analysis on the current frequency components to obtain the branch impedance characteristic quantities; Performing resonance modal analysis on the inter-node coupling index and the branch impedance characteristic quantity to obtain a candidate resonance frequency interval, and performing segmented screening on the candidate resonance frequency interval to obtain a target resonance frequency interval; Performing impedance calculation processing on the voltage frequency component and the current frequency component within the target resonant frequency interval to obtain an impedance characteristic curve of each node, and performing feature extraction processing on the impedance characteristic curve of each node to obtain a system impedance characteristic; Threshold analysis is performed on the impedance characteristics of the system to obtain resonance frequency interval parameters.

3. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The risk level classification process of the resonant frequency interval parameter to obtain the graded warning threshold parameter and the key frequency monitoring point includes: Performing risk intensity statistical processing on the resonance frequency interval parameter to obtain a frequency band risk distribution sequence, and performing segmented grade calculation processing on the frequency band risk distribution sequence to obtain a risk grading initial value; Performing boundary point calibration processing on the risk grading initial value to obtain risk grade demarcation points, and performing threshold processing on the risk grade demarcation points to obtain a grading threshold reference group; Performing parameter correction processing on the graded threshold reference group to obtain a warning threshold reference value, and dynamically correcting the warning threshold reference value to obtain a graded warning threshold parameter; Performing frequency sensitivity analysis on the resonance frequency interval parameters to obtain a resonance frequency sensitivity matrix, and performing characteristic decomposition on the resonance frequency sensitivity matrix to obtain a monitoring point evaluation index; Prioritizing the monitoring point evaluation indicators to obtain a monitoring point candidate sequence, and screening the monitoring point candidate sequence to obtain key frequency monitoring points.

4. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The group vibration characteristic decomposition processing is performed on the system impedance characteristic and the key frequency monitoring point to obtain the wind turbine group disturbance sensitivity index and the oscillation source positioning result, including: Performing group division processing on the impedance characteristics of the system to obtain an impedance characteristic grouping set, and performing mutual coupling calculation processing on the impedance characteristic grouping set to obtain an inter-group coupling matrix; Performing feature extraction processing on the inter-group coupling matrix and the key frequency monitoring point to obtain a group vibration feature vector, and performing modal decomposition processing on the group vibration feature vector to obtain a group vibration modal parameter; Performing sensitivity analysis on the group vibration modal parameters to obtain a node sensitivity sequence, and normalizing the node sensitivity sequence to obtain a wind turbine group disturbance sensitivity index; Performing time delay calculation processing on the group vibration modal parameters to obtain an oscillation propagation sequence, and performing energy flow analysis processing on the oscillation propagation sequence to obtain an oscillation energy distribution matrix; The oscillation energy distribution matrix is ​​subjected to source point identification processing to obtain an oscillation source candidate set, and the oscillation source candidate set is subjected to positioning analysis processing to obtain an oscillation source positioning result.

5. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The impedance reconstruction processing is performed on the disturbance sensitivity index and the graded warning threshold parameter to obtain the wind turbine group coordinated damping parameter and the power dynamic allocation coefficient, including: Performing sensitivity sorting processing on the disturbance sensitivity index to obtain a sensitivity level sequence, and performing matching processing on the sensitivity level sequence and the graded warning threshold parameter to obtain a sensitivity threshold matrix; Performing impedance characteristic analysis processing on the sensitivity threshold matrix to obtain impedance characteristic parameters, and performing parameter decomposition processing on the impedance characteristic parameters to obtain a damping characteristic set; Performing damping distribution processing on the damping characteristic set to obtain a damping reference sequence, and performing dynamic coordination processing on the damping reference sequence to obtain wind turbine group coordinated damping parameters; Performing power characteristic calculation processing on the damping reference sequence to obtain a power adjustment reference value, and performing distributed analysis processing on the power adjustment reference value to obtain a power allocation vector; The power allocation vector is subjected to coefficient calibration processing to obtain allocation reference coefficients, and the allocation reference coefficients are subjected to dynamic calibration processing to obtain power dynamic allocation coefficients.

6. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The resonant link tracking process is performed on the oscillation source positioning result, the wind turbine group disturbance sensitivity index and the power dynamic allocation coefficient to obtain the key branch impedance compensation value and the reactive power adjustment coefficient, including: Performing link association processing on the oscillation source positioning result and the disturbance sensitivity index of the wind turbine group to obtain an oscillation propagation path, and performing hierarchical division processing on the oscillation propagation path to obtain a link hierarchical sequence; Performing feature matching processing on the link level sequence and the power dynamic allocation coefficient to obtain a link impedance feature quantity, and performing node identification processing on the link impedance feature quantity to obtain a key node set; Performing branch analysis processing on the key node set to obtain a branch impedance sequence, and performing benchmark calculation processing on the branch impedance sequence to obtain a branch impedance benchmark value; Performing compensation amount calculation processing on the branch impedance reference value to obtain an impedance compensation reference amount, and performing difference correction processing on the impedance compensation reference amount to obtain a key branch impedance compensation value; The reactive characteristic analysis process is performed on the critical branch impedance compensation value to obtain the reactive compensation demand, and the reactive compensation demand is subjected to coefficient calibration process to obtain the reactive adjustment coefficient.

7. The method for suppressing synchronous resonance of a wind farm according to claim 1, characterized in that: The closed-loop correction process is performed on the wind turbine group coordinated damping parameter, the key branch impedance compensation value and the reactive power adjustment coefficient to obtain the suppression control instruction and the dynamic correction parameter, including: Performing parameter combination processing on the wind turbine group coordinated damping parameter and the key branch impedance compensation value to obtain an initial set of control parameters, and performing matching processing on the initial set of control parameters and the reactive power adjustment coefficient to obtain a parameter combination sequence; Performing stability evaluation processing on the parameter combination sequence to obtain a stability margin index, and performing interval division processing on the stability margin index to obtain a control reference interval; Performing parameter calibration processing on the control reference interval to obtain a control reference value, and performing response characteristic analysis processing on the control reference value to obtain a response characteristic parameter; Performing instruction generation processing on the response characteristic parameter to obtain a control instruction reference value, and performing closed-loop verification processing on the control instruction reference value to obtain a suppression control instruction; The suppression control instruction is subjected to parameter correction processing to obtain a correction reference value, and the correction reference value is subjected to dynamic analysis processing to obtain a dynamic correction parameter.

8. A wind farm synchronous resonance suppression system, used to implement the wind farm synchronous resonance suppression method according to any one of claims 1 to 7, characterized in that: The wind farm synchronous resonance suppression system comprises: An identification module is used to perform resonance characteristic identification processing on the electrical quantity data of each node of the wind farm to obtain the resonance frequency interval parameters and system impedance characteristics; A classification module is used to classify the resonant frequency interval parameters into risk levels to obtain graded warning threshold parameters and key frequency monitoring points; A decomposition module, used to perform group vibration characteristic decomposition processing on the system impedance characteristics and the key frequency monitoring points to obtain a wind turbine group disturbance sensitivity index and an oscillation source positioning result; A reconstruction module, used to perform impedance reconstruction processing on the disturbance sensitivity index and the graded warning threshold parameter to obtain a wind turbine group coordinated damping parameter and a power dynamic allocation coefficient; A tracking module, used to perform resonant link tracking processing on the oscillation source positioning result, the disturbance sensitivity index of the wind turbine group and the power dynamic allocation coefficient, so as to obtain a key branch impedance compensation value and a reactive power adjustment coefficient; The correction module is used to perform closed-loop correction processing on the wind turbine group coordinated damping parameters, the key branch impedance compensation value and the reactive power adjustment coefficient to obtain suppression control instructions and dynamic correction parameters.

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