A safe operation method and system for a wind power grid-connected system against subsynchronous oscillation

Abstract

The present application relates to the technical field of wind power grid connection control, and discloses a method and a system for safe operation of a wind power grid connection system against subsynchronous oscillation. The method includes: performing time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities; performing stability analysis processing on the oscillation mode feature quantities to obtain subsynchronous oscillation risk warning signals; performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signals to obtain mechanical-electrical coordination control instructions; performing task decomposition processing on the coordination control instructions to obtain real-time control quantities of each actuator; evaluating and correcting the execution effects of the real-time control quantities of each actuator to obtain a corrected control parameter set, and performing real-time control on the wind power grid connection system through the corrected control parameter set. The present application realizes the coordinated control of the electrical subsystem and the mechanical subsystem of the wind power grid connection system, effectively suppresses subsynchronous oscillation, and improves the operation stability of the system.

Description

Technical Field

[0001] This application relates to the field of wind power grid connection control, and particularly to a safe operation method and system for a wind power grid connection system against subsynchronous oscillation. Background Art

[0002] With the continuous expansion of the scale of wind power grid connection, the problem of subsynchronous oscillation in large-scale wind farms has become increasingly prominent. Existing wind power grid connection systems mainly suppress subsynchronous oscillation by setting devices such as damping controllers and power oscillation suppressors, and adopt fast detection methods based on state observation and control strategies based on multiple feedback for oscillation suppression. At the same time, the industry has also developed a variety of advanced control methods including adaptive control and model predictive control to improve the anti-oscillation ability of the system, and these methods have alleviated the adverse effects brought by subsynchronous oscillation to a certain extent.

[0003] However, the existing technology still has the following deficiencies: First, traditional oscillation suppression methods often separate the electrical system and the mechanical system for independent control, ignoring the coupling effect between the two subsystems, resulting in unsatisfactory control effects; Second, existing oscillation detection methods mainly analyze based on a single time scale and cannot accurately capture the oscillation characteristics of the system in different frequency bands; Finally, the design of the control strategy lacks consideration of the overall performance of the system and it is difficult to maintain a good oscillation suppression effect under complex working conditions. Summary of the Invention

[0004] This application provides a safe operation method and system for a wind power grid connection system against subsynchronous oscillation, which is used to achieve the coordinated control of the electrical subsystem and the mechanical subsystem of the wind power grid connection system, effectively suppress subsynchronous oscillation, and improve the operation stability of the system.

[0005] In a first aspect, this application provides a safe operation method for a wind power grid connection system against subsynchronous oscillation. The safe operation method for a wind power grid connection system against subsynchronous oscillation includes: performing multi-source data acquisition and processing on the electrical parameters and mechanical parameters of the wind power grid connection system to obtain an original data set; performing time-frequency feature extraction processing on the original data set to obtain oscillation modal feature quantities; performing stability analysis processing on the oscillation modal feature quantities to obtain a subsynchronous oscillation risk warning signal; performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction; performing task decomposition processing on the coordinated control instruction to obtain real-time control quantities of each actuator; evaluating and correcting the execution effects of the real-time control quantities of each actuator to obtain a corrected control parameter set, and performing real-time control on the wind power grid connection system through the corrected control parameter set.

[0006] In a second aspect, the present application provides a wind power grid-connected system safety operation system for subsynchronous oscillations, the wind power grid-connected system safety operation system for subsynchronous oscillations comprising:

[0007] The acquisition module is used to perform multi-source data acquisition and processing on the electrical parameters and mechanical parameters of the wind power grid-connected system to obtain the original data set;

[0008] An extraction module is used to perform time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities;

[0009] An analysis module is used to perform stability analysis on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk warning signal;

[0010] A decoupling module, used for performing multi-time scale decoupling processing on the sub-synchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction;

[0011] The decomposition module is used to perform task decomposition processing on the coordinated control instructions to obtain the real-time control quantity of each actuator;

[0012] The correction module is used to evaluate and correct the execution effect of the real-time control quantity of each actuator to obtain a corrected control parameter set, and to control the wind power grid-connected system in real time through the corrected control parameter set.

[0013] In the technical solution provided by the present application, by performing multi-source data collection and processing on the electrical parameters and mechanical parameters of the wind power grid-connected system, the system operation status information can be fully obtained, laying a data foundation for the subsequent oscillation feature extraction and analysis; the time-frequency feature extraction and processing of the original data set can accurately capture the oscillation characteristics of the system in both the time domain and the frequency domain, and improve the accuracy of oscillation mode recognition; the stability analysis and processing of the oscillation mode feature quantity can timely discover potential unstable factors in the system and realize early warning of subsynchronous oscillations; by performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal, the oscillation components of different frequency bands in the system can be separated. The system can decompose the coordinated control instructions into tasks, reasonably allocate the control tasks to each actuator, and ensure the executability of the control instructions; evaluate and correct the execution effect of the real-time control quantity of each actuator, and dynamically adjust the control parameters to continuously improve the control performance; the entire control process forms a closed-loop adaptive control system, which not only takes into account the coupling characteristics of the electrical system and the mechanical system, but also realizes coordinated control on multiple time scales. At the same time, the real-time evaluation and correction mechanism ensures the continuous optimization of the control effect, thereby effectively improving the operating reliability and stability of the wind power grid-connected system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic diagram of an embodiment of the method for safe operation of a wind power grid-connected system against subsynchronous oscillation in an embodiment of the present application;

[0016] Figure 2 It is a schematic diagram of an embodiment of the system for safe operation of a wind power grid-connected system against subsynchronous oscillation in an embodiment of the present application. Specific embodiments

[0017] The embodiments of the present application provide a method and system for safe operation of a wind power grid-connected system against subsynchronous oscillation. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and accompanying drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the term "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 An embodiment of the method for safe operation of a wind power grid-connected system against subsynchronous oscillation in an embodiment of the present application includes:

[0019] Step S101: Perform multi-source data acquisition and processing on the electrical parameters and mechanical parameters of the wind power grid-connected system to obtain an original data set;

[0020] Step S102: Perform time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities;

[0021] Step S103: Perform stability analysis processing on the oscillation mode feature quantities to obtain a subsynchronous oscillation risk warning signal;

[0022] Step S104: Perform multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction;

[0023] Step S105: Perform task decomposition processing on the coordinated control instruction to obtain the real-time control quantities of each actuator;

[0024] Step S106: Evaluate and correct the execution effects of the real-time control quantities of each actuator to obtain a set of corrected control parameters, and perform real-time control on the wind power grid-connected system through the set of corrected control parameters.

[0025] It can be understood that the execution subject of this application can be a safety operation system for a wind power grid-connected system against subsynchronous oscillation, or a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0026] Specifically, multi-source data acquisition and processing are performed. In this step, voltage and current signals are collected at a sampling frequency of 10 kHz through sampling devices arranged at the stator end and the rotor end. The collected data includes stator voltage, stator current, rotor voltage, and rotor current. After being processed by a digital low-pass filter, high-frequency noise above 1 kHz is filtered out to obtain electrical parameter data; at the same time, the wind turbine speed and the generator rotor speed are collected, and the sampling frequency is set to 1 kHz. The speed spectrum characteristics in the range of 0.1 - 50 Hz are obtained through spectrum analysis; vibration signals are collected on the drive shaft, and the sampling frequency is 5 kHz. Measurement noise is eliminated through wavelet transform, and vibration characteristics in the range of 0.1 - 100 Hz are extracted. Finally, these data are time-aligned and data-fused to form a complete original data set. Time-frequency feature extraction is performed on the original data set. The voltage and current signals are converted to the frequency domain through Fourier transform, and the spectrum data in the range of 0.1 - 50 Hz are extracted to identify the main harmonic components in the electrical system; wavelet decomposition is performed on the speed signal, and the db4 wavelet basis function is selected to decompose the signal into 4 layers, corresponding to the speed characteristics in different frequency bands respectively; empirical mode decomposition is performed on the vibration signal to decompose the complex vibration signal into multiple intrinsic mode functions, and each intrinsic mode function represents an inherent vibration mode; then the correlation relationship between electrical characteristics and mechanical characteristics is determined through cross-correlation analysis, and finally the oscillation mode characteristic quantity of the system is obtained.

[0027] After obtaining the oscillation mode characteristic quantities, stability analysis is carried out, the system eigenvalues are calculated, the real and imaginary parts of the eigenvalues are extracted, and the damping ratios of each oscillation mode are calculated. The modes with damping ratios less than 5% are marked as potentially unstable modes; energy distribution analysis is performed on these modes, the oscillation intensities of each frequency band are calculated, and the risk levels are divided into three levels: low, medium, and high according to the magnitudes of the oscillation intensities. The risk probabilities are updated through Bayesian inference to generate a subsynchronous oscillation risk warning signal. Based on the warning signal, multi-time scale decoupling is carried out, and the oscillation signal is divided into a fast oscillation component (greater than 5 Hz) and a slow oscillation component (0.1 - 5 Hz) according to the frequency characteristics, and the control requirements of the electrical subsystem and the mechanical subsystem are extracted respectively; the system coupling characteristics are analyzed, control target constraint conditions are established, a reference control sequence is generated and dynamically compensated to form a mechanical-electrical coordinated control command.

[0028] The coordinated control command is decomposed, the control tasks are assigned to actuators such as converter control, pitch control, and excitation control, the control parameters required by each actuator are calculated, and timing coordination is carried out to ensure the coordination of control actions; finally, the control effect is evaluated in real time, the deviation of the control parameters is calculated, and the corrected control parameter set is obtained through amplitude limiting and parameter update to achieve closed-loop control of the wind power grid-connected system.

[0029] For example, when the operating power of the unit is 1.5 MW, the original data of the stator voltage of 220 V (rms), stator current of 1.2 kA, rotor speed of 1500 rpm, and drive shaft vibration amplitude of 0.5 mm are obtained through sampling. After time-frequency characteristic extraction, an oscillation component with an amplitude of 0.3 pu is identified near 15 Hz, and the damping ratio of this oscillation mode is calculated to be 3% through damping ratio calculation, triggering a medium-level warning signal. The system decomposes this oscillation into an electrical oscillation component of 12 Hz and a mechanical oscillation component of 3 Hz, and generates a current control command (1.0 pu) for the rotor-side converter and an angle control command (2 degrees) for the pitch system respectively. After the control is executed, the oscillation amplitude of the system is reduced to below 0.1 pu, and the damping ratio is increased to 8%, achieving effective suppression of subsynchronous oscillation.

[0030] In the implementation of this application, by collecting and processing multi-source data of the electrical parameters and mechanical parameters of the wind power grid-connected system, the operation state information of the system can be comprehensively obtained, laying a data foundation for subsequent oscillation feature extraction and analysis; by performing time-frequency feature extraction processing on the original data set, the oscillation characteristics of the system can be accurately captured in both the time domain and the frequency domain, improving the accuracy of oscillation mode identification; by performing stability analysis processing on the oscillation mode feature quantities, potential unstable factors in the system can be detected in a timely manner, realizing early warning of subsynchronous oscillation; by performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal, the oscillation components in different frequency bands of the system can be separated, and control strategies can be formulated in a targeted manner to improve the accuracy of control; by performing task decomposition processing on the coordinated control instructions, the control tasks can be reasonably allocated to each actuator to ensure the executability of the control instructions; by evaluating and correcting the execution effects of the real-time control quantities of each actuator, the control parameters can be dynamically adjusted to continuously improve the control performance; the entire control process forms a closed-loop adaptive control system, which not only considers the coupling characteristics of the electrical system and the mechanical system, but also realizes coordinated control on multiple time scales, and at the same time ensures the continuous optimization of the control effect through a real-time evaluation and correction mechanism, thereby effectively improving the operation reliability and stability of the wind power grid-connected system.

[0031] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0032] (1) Synchronously sample the voltage signal and current signal at the stator end of the wind power grid-connected system to obtain stator electrical parameter data, and synchronously sample the voltage signal and current signal at the rotor end of the wind power grid-connected system to obtain rotor electrical parameter data;

[0033] (2) Perform digital filtering processing on the stator electrical parameter data and rotor electrical parameter data to obtain filtered electrical signal data, and perform power parameter calculation processing on the filtered electrical signal data to obtain real-time system power data;

[0034] (3) Collect the wind turbine speed and generator rotor speed of the wind power grid-connected system to obtain mechanical speed data, and perform spectral analysis processing on the mechanical speed data to obtain speed spectral feature data;

[0035] (4) Collect vibration characteristics of the drive shaft vibration signal of the wind power grid-connected system to obtain original vibration data, and perform noise elimination processing on the original vibration data through wavelet transform to obtain vibration feature data after noise reduction;

[0036] (5) Perform data fusion processing on the real-time system power data, speed spectral feature data, and vibration feature data after noise reduction to obtain the original data set.

[0037] Specifically, high-precision voltage sensors and current sensors are installed at the stator end of the wind turbine. The synchronous sampling technique is used to collect the stator three-phase voltage and current signals, and the sampling frequency is set to 10 kHz, which ensures the sampling accuracy and meets the Nyquist sampling theorem at the same time. The range of the collected original voltage signal is 0 - 1000 V, and the range of the current signal is 0 - 5000 A. The signals are normalized to the range of ±10 V through the signal conditioning circuit and then digitally processed by a 16-bit AD converter to obtain the stator electrical parameter data. At the same time, the same sampling scheme is adopted at the rotor end to synchronously sample the rotor three-phase voltage and current. The range of the rotor voltage signal is 0 - 690 V, and the range of the current signal is 0 - 2000 A. After the same signal conditioning and AD conversion process, the rotor electrical parameter data is obtained. Then, digital filtering is performed on the collected stator and rotor electrical parameter data. A Butterworth low-pass filter is used, and the cut-off frequency is set to 1 kHz to filter out high-frequency noise and interference components. The filtered data is used to calculate the real-time power parameters of the system, including the active power and reactive power on the stator side, the active power and reactive power on the rotor side, etc. The power calculation uses the three-phase instantaneous power theory to calculate the corresponding power values at each sampling point, and a set of average power data is output every 100 ms to obtain the system real-time power data.

[0038] In terms of mechanical parameter acquisition, the rotational speed signals are collected by rotational speed sensors installed on the wind turbine main shaft and the generator shaft, and the sampling frequency is 1 kHz. The rotational speed signals are digitally processed after photoelectric conversion and signal conditioning to obtain the mechanical rotational speed data. Fast Fourier transform is performed on the mechanical rotational speed data to analyze the rotational speed fluctuation characteristics in the frequency range of 0.1 - 50 Hz, with a focus on the subsynchronous frequency components in the range of 1 - 5 Hz to obtain the rotational speed spectrum characteristic data. For the vibration monitoring of the transmission system, acceleration sensors are installed at key positions of the transmission shaft, and the sampling frequency is set to 5 kHz. The collected vibration acceleration signals are processed through a charge amplifier and signal conditioning to obtain the original vibration data. The db4 wavelet basis function is used to perform 4-layer wavelet decomposition on the original vibration data to remove the noise components in the highest frequency band and retain the mid-low frequency signals containing the system vibration characteristics. The vibration characteristic data after noise reduction is obtained through wavelet reconstruction.

[0039] Finally, data fusion processing is performed on the system real-time power data, the rotational speed spectrum characteristic data, and the vibration characteristic data after noise reduction. The data fusion adopts the time stamp alignment method to unify the data with different sampling frequencies to a time interval of 100 ms, establish a multi-dimensional data matrix containing electrical parameters and mechanical parameters, and form a complete original data set.

[0040] Taking a 1.5MW doubly-fed wind turbine as an example, when the wind speed is 10m / s, the effective value of the phase-A voltage sampled at the stator end is 220V, the current is 1200A, the phase-A voltage at the rotor end is 150V, and the current is 500A. After digital filtering and power calculation, the active power on the stator side is 1.2MW, the reactive power is 0.3MVar, the active power on the rotor side is 0.3MW, and the reactive power is 0.1MVar. At the same time, the wind turbine speed is measured to be 15rpm, and the generator rotor speed is 1500rpm. Through FFT analysis, it is found that there is a speed fluctuation with an amplitude of 0.1rpm at 2.5Hz. The original amplitude of the drive shaft vibration signal is 2g, and after wavelet denoising, it is reduced to 1.5g. The main vibration frequency is concentrated around 3Hz. After time alignment of these data, a data vector containing 22 parameters is formed every 100ms, constituting the original data set for subsequent analysis.

[0041] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0042] (1) Perform Fourier transform processing on the voltage signal and current signal in the original data set to obtain electrical signal spectrum data, and perform harmonic component extraction processing on the electrical signal spectrum data to obtain electrical harmonic feature data;

[0043] (2) Perform wavelet decomposition processing on the speed signal in the original data set to obtain multi-scale speed feature data, and perform principal component analysis processing on the multi-scale speed feature data through singular value decomposition to obtain the main oscillation component of the speed;

[0044] (3) Perform empirical mode decomposition processing on the vibration signal in the original data set to obtain an intrinsic mode function set, and perform envelope analysis processing on the intrinsic mode function set to obtain vibration amplitude feature data;

[0045] (4) Perform cross-correlation analysis processing on the electrical harmonic feature data and the main oscillation component of the speed to obtain electro-mechanical coupling feature data, and perform feature fusion processing on the electro-mechanical coupling feature data and the vibration amplitude feature data to obtain multi-source oscillation feature data;

[0046] (5) Perform modal separation processing on the multi-source oscillation feature data to obtain oscillation modal feature quantities.

[0047] Specifically, the second step of the safe operation method for a wind power grid-connected system against subsynchronous oscillation is to extract time-frequency characteristics. First, perform Fourier transform processing on the voltage and current signals in the original dataset to convert the time-domain signals to the frequency domain. The Fourier transform uses a 1024-point FFT algorithm and performs a transformation on every 10 ms of data to obtain spectral data in the range of 0 - 500 Hz. Extract the fundamental wave component (50 Hz) and harmonic components in the subsynchronous frequency range (0.1 - 50 Hz) from the spectral data, and record the amplitude and phase information of each frequency point. Sort the extracted harmonic components by amplitude size, and select the components with an amplitude exceeding 1% of the fundamental wave as the main harmonic characteristics to form electrical harmonic characteristic data. Perform wavelet decomposition on the rotational speed signal in the original dataset, and use the db4 wavelet basis function for 4-layer decomposition. The wavelet decomposition divides the rotational speed signal into different frequency bands: the first layer corresponds to 25 - 50 Hz, the second layer corresponds to 12.5 - 25 Hz, the third layer corresponds to 6.25 - 12.5 Hz, the fourth layer corresponds to 3.125 - 6.25 Hz, and the approximation component corresponds to 0 - 3.125 Hz. Construct a Hankel matrix for the obtained multi-scale rotational speed characteristic data and perform singular value decomposition. The singular value decomposition decomposes the data matrix into a left singular matrix, a singular value diagonal matrix, and a right singular matrix. Select the eigenvectors corresponding to the largest 3 singular values and reconstruct to obtain the main oscillation component of the rotational speed.

[0048] Perform empirical mode decomposition processing on the vibration signal, and decompose the complex signal into several intrinsic mode functions through an iterative screening process. The empirical mode decomposition first finds all local extreme points of the signal, forms the upper and lower envelope lines through cubic spline interpolation, calculates the mean envelope line, subtracts the mean envelope from the original signal to obtain the first intrinsic mode function, and repeats this process until the remaining signal becomes a monotonic function. Perform Hilbert transform on each intrinsic mode function to obtain the instantaneous amplitude and instantaneous frequency, and form vibration amplitude characteristic data. Perform cross-correlation analysis on the electrical harmonic characteristic data and the main oscillation component of the rotational speed, calculate the correlation coefficients at different time delays, find the time delay corresponding to the maximum correlation coefficient, and determine the correlation relationship between electrical oscillation and mechanical oscillation. Fuse the cross-correlation analysis results and the vibration amplitude characteristic data at the feature level, and use the weighted average method to normalize and combine the three types of characteristic data to obtain multi-source oscillation characteristic data. Finally, perform modal separation on the multi-source oscillation characteristic data, and use the independent component analysis method to separate the mixed oscillation characteristics into independent oscillation modes. The independent component analysis is based on the statistical independence of the signals, and estimates the separation matrix by maximizing the non-Gaussianity to obtain the characteristic quantities of each independent oscillation mode.

[0049] Taking a 1.5MW doubly-fed wind turbine as an example to illustrate the data processing process: The FFT analysis is performed on the collected stator voltage signal. Besides the fundamental wave of 50Hz, three main harmonic components of 15Hz, 30Hz and 45Hz are detected, and the amplitudes are 5%, 3% and 2% of the fundamental wave respectively. The wavelet decomposition is performed on the generator speed signal of 1500rpm. A fluctuation with an amplitude of 5rpm is detected in the third layer (6.25 - 12.5Hz), and it is confirmed as the main oscillation component through singular value decomposition. The empirical mode decomposition of the drive shaft vibration signal obtains 5 intrinsic mode functions. Among them, the second intrinsic mode function shows significant vibration characteristics near 7.5Hz, and the amplitude is 0.8g. Through cross-correlation analysis, it is found that there is a time delay of 0.1s between the 15Hz electrical harmonic and the 7.5Hz mechanical vibration, and the correlation coefficient reaches 0.85, indicating an obvious coupling relationship between these two oscillations. Finally, 3 main oscillation modes are obtained through mode separation: the 15Hz electrical oscillation mode, the 7.5Hz mechanical oscillation mode, and the subsynchronous oscillation mode generated by the coupling of the two.

[0050] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0051] (1) Perform eigenvalue calculation processing on the oscillation mode characteristic quantities to obtain a set of system eigenvalues, and perform damping ratio calculation processing on the set of system eigenvalues to obtain modal damping ratio data;

[0052] (2) Perform critical stability judgment processing on the modal damping ratio data to obtain oscillation instability degree data, and perform energy distribution analysis processing on the oscillation instability degree data to obtain oscillation energy distribution characteristics;

[0053] (3) Perform frequency band decomposition processing on the oscillation energy distribution characteristics to obtain oscillation intensity data for each frequency band, and perform threshold comparison processing on the oscillation intensity data for each frequency band to obtain frequency band warning level data;

[0054] (4) Perform risk assessment processing on the frequency band warning level data to obtain oscillation risk level data, and perform probability update processing on the oscillation risk level data through Bayesian inference to obtain risk probability distribution data;

[0055] (5) Perform risk comprehensive analysis processing on the risk probability distribution data to obtain a subsynchronous oscillation risk warning signal.

[0056] Specifically, eigenvalue calculations are performed on the oscillation mode characteristic quantities. A characteristic equation is constructed through the state space matrix, and the set of system eigenvalues is obtained by solving. Each eigenvalue contains a real part and an imaginary part. The real part represents the decay rate of the oscillation, and the imaginary part represents the angular frequency of the oscillation. Based on the eigenvalues, the damping ratio of each oscillation mode is calculated. The damping ratio is equal to the absolute value of the real part of the eigenvalue divided by the modulus of the eigenvalue, obtaining the modal damping ratio data matrix. A critical stability judgment is made on the modal damping ratio data. The critical value of the damping ratio is set at 5%, and the oscillation modes with damping ratios lower than the critical value are marked as potentially unstable modes. According to the difference between the value of the damping ratio and the critical value, the degree of oscillation instability is calculated, and an energy distribution analysis is performed on the unstable oscillation. The energy distribution analysis is calculated using the following formula:

[0057]

[0058] Where:

[0059] is the oscillation energy distribution at frequency f; N is the number of oscillation modes; is the weight coefficient of the i-th mode; is the power spectral density of the i-th mode at frequency f; is the attenuation coefficient; is the damping ratio of the i-th mode at frequency f.

[0060] The calculated oscillation energy distribution characteristics are divided into multiple frequency bands according to frequency, with each frequency band having a width of 5 Hz, and the oscillation intensity within each frequency band is calculated. The oscillation intensity is determined according to the energy integral value within the frequency band. The oscillation intensity of the frequency band is compared with a preset threshold value. The threshold values are divided into three levels: low risk (less than 30%), medium risk (30% - 70%), and high risk (greater than 70%), generating frequency band warning level data. A risk assessment is performed on the frequency band warning level data, comprehensively considering factors such as oscillation frequency, energy magnitude, and duration, obtaining oscillation risk level data. The risk level is dynamically updated through Bayesian inference. Bayesian inference establishes a prior probability based on historical data and updates the posterior probability in combination with real-time monitoring data, obtaining risk probability distribution data.

[0061] Finally, a comprehensive analysis is performed on the risk probability distribution data. According to the risk probability and energy distribution characteristics of each frequency band, a sub-synchronous oscillation risk warning signal is generated. The warning signal includes information such as oscillation frequency, risk level, and development trend.

[0062] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0063] (1)Perform time-scale hierarchical processing on the subsynchronous oscillation risk warning signal to obtain the fast oscillation component and the slow oscillation component, and perform electrical feature extraction processing on the fast oscillation component to obtain the control requirements of the electrical subsystem;

[0064] (2)Perform mechanical feature extraction processing on the slow oscillation component to obtain the control requirements of the mechanical subsystem, and perform coupling analysis processing on the control requirements of the electrical subsystem and the mechanical subsystem to obtain the system coupling characteristic data;

[0065] (3)Perform control target decomposition processing on the system coupling characteristic data to obtain the control target sets of each subsystem, and perform coordination constraint processing on the control target sets of each subsystem to obtain the coordinated control constraint conditions;

[0066] (4)Perform control sequence generation processing on the coordinated control constraint conditions to obtain the reference control sequence, and perform dynamic compensation processing on the reference control sequence to obtain the compensation control quantity;

[0067] (5)Perform coordinated control synthesis processing on the compensation control quantity to obtain the mechanical-electrical coordinated control instruction.

[0068] Specifically, perform time-scale hierarchical processing on the subsynchronous oscillation risk warning signal, and use the wavelet packet decomposition method to divide the signal into different frequency bands. Define the frequency above 5 Hz as the fast oscillation component, which is mainly related to the electrical subsystem; define the frequency range of 0.1 - 5 Hz as the slow oscillation component, which is mainly related to the mechanical subsystem. Perform electrical feature extraction on the fast oscillation component, analyze parameters including voltage volatility, current harmonic content, power fluctuation amplitude, etc., set the allowable voltage fluctuation range of ±5%, the current harmonic content limit of 5%, and the power fluctuation limit of 10%, and generate the control requirement data of the electrical subsystem. Perform mechanical feature extraction on the slow oscillation component, analyze parameters including rotational speed fluctuation amplitude, drive shaft torsional amplitude, blade vibration frequency, etc., set the rotational speed fluctuation limit of ±2%, the torsional amplitude limit of 0.5°, and the vibration acceleration limit of 2g, and form the control requirement data of the mechanical subsystem. Perform coupling analysis on the electrical and mechanical control requirements, establish an electro-mechanical coupling matrix, where the matrix elements represent the coupling strength between different parameters, calculate the coupling coefficient using the correlation analysis method, and generate the system coupling characteristic data.

[0069] Decompose the control objectives for the system coupling characteristic data, and decompose the overall control objective into an electrical control objective and a mechanical control objective. The electrical control objectives include: stabilizing the bus voltage, suppressing power fluctuations, and reducing harmonic content; the mechanical control objectives include: smoothing the rotational speed fluctuations, reducing the stress on the drive shaft, and suppressing structural vibrations. Perform coordination constraint processing on the control objectives of each subsystem, establish a set of constraint conditions, including control quantity range constraints, rate of change constraints, stability constraints, etc., and generate coordinated control constraint conditions. Based on the coordinated control constraint conditions, use the rolling horizon optimization method to generate a reference control sequence. The control sequence includes: converter control instructions (such as the reference values of d-q axis currents), pitch control instructions (such as the given values of pitch angles), excitation control instructions (such as the regulated values of excitation voltages), etc. Perform dynamic compensation on the reference control sequence, adjust the control parameters according to the real-time operating state, calculate the compensation value, and obtain the compensated control quantity.

[0070] Finally, perform coordinated control synthesis processing on the compensated control quantity to ensure the cooperation and timing between the control quantities, and form a complete mechanical-electrical coordinated control instruction. The control instructions include: converter PWM control signals, pitch actuator control signals, excitation system control signals, etc.

[0071] Taking a 1.5MW doubly-fed generator set as an example: a sub-synchronous oscillation warning signal of 15Hz is detected, and the oscillation amplitude is 8% of the rated power. Through time-scale decomposition, it is separated into an electrical oscillation of 12Hz (amplitude 6%) and a mechanical oscillation of 3Hz (amplitude 4%). Electrical feature extraction shows: voltage fluctuation of 3.5%, current harmonics of 4.2%, power fluctuation of 8%; mechanical feature extraction shows: rotational speed fluctuation of 1.8%, torsional amplitude of 0.4°, vibration acceleration of 1.5g. Coupling analysis shows that the coupling coefficient between the electrical oscillation and the mechanical oscillation is 0.72. After control objective decomposition and constraint processing, a reference control sequence is generated: converter d-axis current regulation -50A, q-axis current regulation +30A, pitch angle regulation -1.5°. Dynamic compensation, according to the real-time power fluctuation situation, compensates the d-axis current by +10A, and finally forms a coordinated control instruction: converter PWM control duty cycle adjustment of 2%, pitch rate control of 0.5° / s.

[0072] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0073] (1) Perform functional level division processing on the mechanical-electrical coordinated control instruction to obtain a master control layer instruction and a device layer instruction, and perform control task parsing processing on the master control layer instruction to obtain a key control parameter set;

[0074] (2)Perform execution unit allocation processing on the device layer instructions to obtain control sequences for each execution unit, and perform timing coordination processing on the control sequences of each execution unit to obtain control timing data;

[0075] (3)Perform control quantity calculation processing on the key control parameter set to obtain basic control data, and perform matching processing on the basic control data and the control timing data to obtain the actuator control scheme;

[0076] (4)Perform control component extraction processing on the actuator control scheme to obtain a set of alternative control quantities, and perform constraint verification processing on the set of alternative control quantities to obtain the real-time control quantities of each actuator.

[0077] Specifically, perform functional level division on the mechanical-electrical coordinated control instructions, and divide the control instructions into two levels: the main control layer and the device layer. The main control layer instructions are responsible for formulating the overall control strategy, including oscillation suppression objectives, control priorities, coordination strategies, etc.; the device layer instructions are responsible for the control of specific execution units, including converter control, pitch control, excitation control, etc. Perform task parsing on the main control layer instructions to extract key control parameters, such as oscillation suppression target values, control bandwidths, response times, and other parameters, to form a key control parameter set. Perform execution unit allocation on the device layer instructions, and allocate the instructions to the corresponding actuators according to the characteristics of the control tasks. The converter control unit is responsible for electrical oscillation suppression, the pitch control unit is responsible for mechanical oscillation suppression, and the excitation control unit is responsible for voltage regulation. Perform timing coordination on the control sequences of each execution unit to determine the sequence and execution time of control actions, and generate control timing data including execution time, duration, and switching conditions.

[0078] Perform control quantity calculation on the key control parameter set, and the calculation process uses the following formula:

[0079]

[0080] Among them, is the control quantity at time t; M is the number of control parameters; is the weight factor of the kth parameter; is the parameter adjustment gain; is the reference value; is the damping coefficient; is the deviation value; is the time attenuation factor. The calculated basic control data is matched with the control timing data, and the control quantity is organized according to the execution timing to form a complete actuator control scheme. The control scheme is extracted, and the control quantity is decomposed into specific control components required by each actuator, such as the modulation ratio of the converter, the angle increment of the pitch, the voltage regulation of the excitation, etc., to form a set of alternative control quantities. The set of alternative control quantities is constrained and checked to check whether each control quantity meets the physical constraints (such as the pitch angle is limited to 0-90 degrees), dynamic constraints (such as the pitch rate is limited to within 8 degrees / second) and stability constraints (such as maintaining a certain stability margin), and the real-time control quantity of each actuator that meets the constraint conditions is obtained. Take a 1.5MW doubly fed generator set to handle subsynchronous oscillation as an example: after detecting a 15Hz oscillation, the main control layer instruction sets the oscillation suppression target to reduce the oscillation amplitude to within 3% of the rated value, and the response time requirement is 100ms. Equipment layer instruction allocation: converter control time resolution 5ms, pitch control time resolution 50ms. Through the control quantity calculation, the converter d-axis current reference value is set to -50A, the q-axis current reference value is set to +30A; the pitch angle reference adjustment is -1.5 degrees. Considering the converter current limit (±1.2 times the rated value) and the pitch rate limit (8 degrees / second), it is finally determined that the converter d-axis current real-time control quantity is -45A, the q-axis current real-time control quantity is +25A, the pitch angle real-time adjustment rate is 6 degrees / second, and the adjustment direction is to decrease.

[0081] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0082] (1) Perform deviation calculation on the response data of the real-time control quantity of each actuator to obtain control deviation data, and perform evaluation index calculation on the control deviation data to obtain performance evaluation results;

[0083] (2) Calculating the control parameter correction amount on the performance evaluation results to obtain the parameter correction amount, and limiting the parameter correction amount to obtain the effective correction amount;

[0084] (3) performing control parameter updating processing on the effective correction amount to obtain a progressive correction parameter, and performing comprehensive processing on the progressive correction parameter to obtain a corrected control parameter set;

[0085] (4) The modified control parameter set is distributed and processed to obtain the modified instructions of each controller, and the modified instructions of each controller are synchronously issued to complete the real-time control of the wind power grid-connected system.

[0086] Specifically, the deviation calculation is performed on the real-time control quantity responses of each actuator to calculate the difference between the actual response value and the target value. The converter control deviation includes the deviation between the actual d-q axis current and the given value, and the sampling period is 1 ms; the pitch control deviation includes the deviation between the actual pitch angle and the given value, and the sampling period is 10 ms; the excitation control deviation includes the deviation between the actual excitation voltage and the given value, and the sampling period is 5 ms. The performance index evaluation is carried out on the calculated control deviation data, and the evaluation indexes include: steady-state deviation (required to be less than 1%), overshoot (required to be less than 10%), adjustment time (required to be less than 200 ms), oscillation decay rate (required to be greater than 0.1 / s), etc., and the comprehensive performance evaluation result is obtained through weighted calculation. The control parameter correction amount is calculated according to the performance evaluation result, and the calculation of the correction amount takes into account the magnitude, change trend and duration of the control deviation. For converter control, when the d-axis current deviation exceeds 5%, the proportional gain correction amount is 0.2 times the deviation value; for pitch control, when the pitch angle deviation exceeds 0.5 degrees, the response gain correction amount is 0.3 times the deviation value; for excitation control, when the excitation voltage deviation exceeds 3%, the integral time correction amount is 0.1 times the original value. The calculated parameter correction amount is limited to ensure that the changed amount of the corrected parameter is within the safe range: the converter control parameter correction amount is limited within ±20%, the pitch control parameter correction amount is limited within ±15%, and the excitation control parameter correction amount is limited within ±10% to obtain the effective correction amount.

[0087] The control parameters are updated with the effective correction amount, and the progressive correction method is adopted, and the update amplitude each time does not exceed 5% of the current parameter value. The converter control parameters are updated every 5 ms, the pitch control parameters are updated every 50 ms, and the excitation control parameters are updated every 20 ms. The stability of the updated parameters is verified to ensure that the parameter update does not cause new oscillations. The progressive correction parameters are comprehensively processed to ensure the coordination between the parameters of each controller and form a complete set of corrected control parameters. The set of corrected control parameters is distributed to each execution controller through a distributed control network, using CAN bus communication with a communication rate of 1 Mbps. After each controller receives the correction instruction, the parameter update is carried out in a synchronous trigger mode, and the trigger signal is uniformly distributed by the main controller to ensure that the parameter update actions of each controller are executed synchronously, and the real-time control process of the entire wind power grid-connected system is completed.

[0088] Taking a 1.5MW doubly-fed generator set as an example: When 15Hz subsynchronous oscillation is detected, the d-axis current set value of the converter controller is -45A, the actual response value is -40A, and the deviation is 11.1%, exceeding the allowable range of 5%; at the same time, the q-axis current set value is 25A, the actual response value is 22A, and the deviation is 12%. The performance evaluation shows that the adjustment time is 180ms and the overshoot is 15%, both of which do not meet the control index requirements. According to the evaluation results, the correction amount of the converter proportional gain is calculated to be +0.15 (15% of the original value), and the actual correction amount is determined to be +0.12 after amplitude limiting processing. Using the progressive correction method, the proportional gain is increased by 0.02 each time, and the correction is completed in 6 times. The corrected control parameters are sent to the converter controller through the CAN bus at a rate of 1Mbps. At the same time, the pitch controller and the excitation controller also synchronously complete the update of their respective parameters. After the parameter correction, the d-axis current response deviation is reduced to 3%, the q-axis current response deviation is reduced to 4%, the adjustment time is shortened to 150ms, and the overshoot is reduced to 8%, meeting the control performance requirements.

[0089] The above describes the method for safe operation of a wind power grid-connected system against subsynchronous oscillation in the embodiments of the present application. Next, the system for safe operation of a wind power grid-connected system against subsynchronous oscillation in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the system for safe operation of a wind power grid-connected system against subsynchronous oscillation in the embodiments of the present application includes:

[0090] An acquisition module, configured to perform multi-source data acquisition and processing on the electrical parameters and mechanical parameters of the wind power grid-connected system to obtain an original data set;

[0091] An extraction module, configured to perform time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities;

[0092] An analysis module, configured to perform stability analysis processing on the oscillation mode feature quantities to obtain a subsynchronous oscillation risk warning signal;

[0093] A decoupling module, configured to perform multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal to obtain a mechanical-electrical coordination control instruction;

[0094] A decomposition module, configured to perform task decomposition processing on the coordination control instruction to obtain real-time control quantities of each actuator;

[0095] A correction module, configured to evaluate and correct the execution effect of the real-time control quantities of each actuator to obtain a corrected control parameter set, and perform real-time control on the wind power grid-connected system through the corrected control parameter set.

[0096] Through the collaborative cooperation of the above-mentioned various components, by collecting and processing multi-source data on the electrical parameters and mechanical parameters of the wind power grid-connected system, the operating state information of the system can be comprehensively obtained, laying a data foundation for subsequent oscillation feature extraction and analysis; by performing time-frequency feature extraction and processing on the original data set, the oscillation characteristics of the system can be accurately captured in both the time domain and the frequency domain, improving the accuracy of oscillation mode identification; by performing stability analysis and processing on the oscillation mode characteristic quantities, potential unstable factors in the system can be detected in a timely manner, realizing early warning of subsynchronous oscillation; by performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal, the oscillation components in different frequency bands of the system can be separated, and control strategies can be formulated in a targeted manner, improving the accuracy of control; by performing task decomposition processing on the coordinated control instructions, the control tasks can be reasonably allocated to each actuator, ensuring the executability of the control instructions; by evaluating and correcting the execution effects of the real-time control quantities of each actuator, the control parameters can be dynamically adjusted to continuously improve the control performance; the entire control process forms a closed-loop adaptive control system, which not only considers the coupling characteristics of the electrical system and the mechanical system, but also realizes coordinated control on multiple time scales, and at the same time ensures the continuous optimization of the control effect through a real-time evaluation and correction mechanism, thereby effectively improving the operation reliability and stability of the wind power grid-connected system.

[0097] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for safe operation of a wind power grid-connected system for subsynchronous oscillation, characterized in that: The wind power grid-connected system safe operation method for subsynchronous oscillation includes: Perform multi-source data collection and processing on the electrical and mechanical parameters of the wind power grid-connected system to obtain the original data set; Performing time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities; Performing stability analysis on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk warning signal; Performing multi-time scale decoupling processing on the subsynchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction; Decompose the coordinated control instructions to obtain the real-time control quantity of each actuator; Evaluate and correct the execution effect of the real-time control quantity of each actuator to obtain a corrected control parameter set, and use the corrected control parameter set to control the wind power grid-connected system in real time; The multi-time scale decoupling process is performed on the sub-synchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction, including: Performing time-scale layered processing on the subsynchronous oscillation risk warning signal to obtain a fast oscillation component and a slow oscillation component, and performing electrical feature extraction processing on the fast oscillation component to obtain an electrical subsystem control demand; Performing mechanical feature extraction processing on the slow oscillation component to obtain a mechanical subsystem control requirement, and performing coupling analysis processing on the electrical subsystem control requirement and the mechanical subsystem control requirement to obtain system coupling feature data; Performing control target decomposition processing on the system coupling characteristic data to obtain a control target set of each subsystem, and performing coordination constraint processing on the control target set of each subsystem to obtain a coordinated control constraint condition; Performing control sequence generation processing on the coordinated control constraint condition to obtain a reference control sequence, and performing dynamic compensation processing on the reference control sequence to obtain a compensation control amount; The compensation control amount is subjected to coordinated control comprehensive processing to obtain the mechanical-electrical coordinated control instruction.

2. The method for safe operation of a wind power grid-connected system against subsynchronous oscillation according to claim 1, characterized in that: The electrical parameters and mechanical parameters of the wind power grid-connected system are collected and processed from multiple sources to obtain an original data set, including: The voltage signal and the current signal of the stator end of the wind power grid-connected system are synchronously sampled and processed to obtain the stator electrical parameter data, and the voltage signal and the current signal of the rotor end of the wind power grid-connected system are synchronously sampled and processed to obtain the rotor electrical parameter data; Performing digital filtering processing on the stator electrical parameter data and the rotor electrical parameter data to obtain filtered electrical signal data, and performing power parameter calculation processing on the filtered electrical signal data to obtain system real-time power data; Performing speed collection and processing on the wind turbine rotor speed and the generator rotor speed of the wind power grid-connected system to obtain mechanical speed data, and performing spectrum analysis and processing on the mechanical speed data to obtain speed spectrum characteristic data; Performing vibration characteristic acquisition processing on the transmission shaft vibration signal of the wind power grid-connected system to obtain original vibration data, and performing noise elimination processing on the original vibration data through wavelet transform to obtain vibration characteristic data after noise reduction; The system real-time power data, the speed spectrum characteristic data and the vibration characteristic data after noise reduction are subjected to data fusion processing to obtain the original data set.

3. The method for safe operation of a wind power grid-connected system against subsynchronous oscillation according to claim 1, characterized in that: The step of performing time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities includes: Performing Fourier transform processing on the voltage signal and the current signal in the original data set to obtain electrical signal spectrum data, and performing harmonic component extraction processing on the electrical signal spectrum data to obtain electrical harmonic characteristic data; Performing wavelet decomposition processing on the speed signal in the original data set to obtain multi-scale speed characteristic data, and performing principal component analysis processing on the multi-scale speed characteristic data through singular value decomposition to obtain the main oscillation component of the speed; Performing empirical mode decomposition processing on the vibration signal in the original data set to obtain an intrinsic mode function set, and performing envelope analysis processing on the intrinsic mode function set to obtain vibration amplitude characteristic data; Performing cross-correlation analysis on the electrical harmonic characteristic data and the main oscillation component of the speed to obtain electro-mechanical coupling characteristic data, and performing feature fusion processing on the electro-mechanical coupling characteristic data and the vibration amplitude characteristic data to obtain multi-source oscillation characteristic data; The multi-source oscillation characteristic data is subjected to modal separation processing to obtain the oscillation modal characteristic quantity.

4. The method for safe operation of a wind power grid-connected system against subsynchronous oscillation according to claim 1, characterized in that: The performing stability analysis on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk warning signal includes: Performing characteristic root calculation processing on the oscillation modal characteristic quantity to obtain a system characteristic value set, and performing damping ratio calculation processing on the system characteristic value set to obtain modal damping ratio data; Performing critical stability judgment processing on the modal damping ratio data to obtain oscillation instability degree data, and performing energy distribution analysis processing on the oscillation instability degree data to obtain oscillation energy distribution characteristics; Performing frequency band decomposition processing on the oscillation energy distribution characteristics to obtain oscillation intensity data of each frequency band, and performing threshold comparison processing on the oscillation intensity data of each frequency band to obtain frequency band warning level data; Performing risk assessment processing on the frequency band warning level data to obtain oscillation risk level data, and performing probability update processing on the oscillation risk level data through Bayesian reasoning to obtain risk probability distribution data; A comprehensive risk analysis is performed on the risk probability distribution data to obtain the sub-synchronous oscillation risk warning signal.

5. The method for safe operation of a wind power grid-connected system against subsynchronous oscillation according to claim 1, characterized in that: The coordinated control instructions are task-decomposed to obtain the real-time control quantity of each actuator, including: Performing functional hierarchical division processing on the mechanical-electrical coordinated control instructions to obtain main control layer instructions and device layer instructions, and performing control task parsing processing on the main control layer instructions to obtain a key control parameter set; Performing execution unit allocation processing on the device layer instruction to obtain control sequences of each execution unit, and performing timing coordination processing on the control sequences of each execution unit to obtain control timing data; Performing control amount calculation processing on the key control parameter set to obtain basic control data, and performing matching processing on the basic control data and the control timing data to obtain an actuator control scheme; The control scheme of the actuator is subjected to control component extraction processing to obtain a set of candidate control quantities, and the set of candidate control quantities is subjected to constraint verification processing to obtain the real-time control quantities of each actuator.

6. The method for safe operation of a wind power grid-connected system against subsynchronous oscillation according to claim 1, characterized in that: The evaluating and correcting the execution effect of the real-time control amount of each actuator to obtain a corrected control parameter set, and controlling the wind power grid-connected system in real time by using the corrected control parameter set, includes: Performing deviation calculation processing on the response data of the real-time control amount of each actuator to obtain control deviation data, and performing evaluation index calculation processing on the control deviation data to obtain a performance evaluation result; Performing control parameter correction amount calculation processing on the performance evaluation result to obtain a parameter correction amount, and performing amplitude limiting processing on the parameter correction amount to obtain an effective correction amount; Performing control parameter update processing on the effective correction amount to obtain a progressive correction parameter, and performing comprehensive processing on the progressive correction parameter to obtain the correction control parameter set; The modified control parameter set is distributedly distributed to obtain the modified instructions of each controller, and the modified instructions of each controller are synchronously issued to complete the real-time control of the wind power grid-connected system.

7. A wind power grid-connected system safety operation system for subsynchronous oscillation, used to implement the wind power grid-connected system safety operation method for subsynchronous oscillation according to any one of claims 1 to 6, characterized in that: The wind power grid-connected system safety operation system for subsynchronous oscillation includes: The acquisition module is used to perform multi-source data acquisition and processing on the electrical parameters and mechanical parameters of the wind power grid-connected system to obtain the original data set; An extraction module is used to perform time-frequency feature extraction processing on the original data set to obtain oscillation mode feature quantities; An analysis module is used to perform stability analysis on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk warning signal; A decoupling module, used for performing multi-time scale decoupling processing on the sub-synchronous oscillation risk warning signal to obtain a mechanical-electrical coordinated control instruction; The decomposition module is used to perform task decomposition processing on the coordinated control instructions to obtain the real-time control quantity of each actuator; The correction module is used to evaluate and correct the execution effect of the real-time control quantity of each actuator to obtain a corrected control parameter set, and to control the wind power grid-connected system in real time through the corrected control parameter set.

Images