Arc fan broadband oscillation suppression method and system based on SSC networking control

Abstract

The invention discloses an arc fan broadband oscillation suppression method and system based on SSC networking control, and the method comprises the steps: carrying out the online impedance measurement and characteristic analysis of an arc fan, so as to recognize the impedance characteristic and negative impedance frequency band of the arc fan in a broadband range; performing SSC capacity configuration and SSC control parameter design based on the impedance characteristic and the negative impedance frequency band of the arc fan; on the basis of the control parameters of the SSC, a network construction control strategy of the SSC is executed, and compensation power is injected into a power grid to remodel system impedance of a wind power plant grid-connected point, so that the system impedance presents a positive impedance characteristic in a target frequency range; through on-line monitoring, the effect of impedance remodeling is continuously evaluated, and control parameters of the SSC are dynamically adjusted according to working condition changes. By means of the scheme, efficient targeted inhibition is achieved by accurately positioning the negative impedance frequency band, and blind compensation is avoided. And the active remodeling system impedance is controlled by constructing a network, so that oscillation can be thoroughly suppressed.

Description

Technical Field

[0001] This application generally relates to the field of power system stability control technology. More specifically, this application relates to a method and system for suppressing broadband oscillations in residual wind turbines based on SSC grid control. Background Technology

[0002] In recent years, with the global energy structure transitioning towards clean and low-carbon energy, new energy sources, represented by wind power, have developed rapidly. Wind power installed capacity has continued to climb, and large-scale wind farms connecting to the grid have become commonplace. However, in regions with weak grid structures or insufficient system rotational inertia, the grid connection of wind farms has brought a series of new challenges, among which the broadband oscillation problem caused by the "isolated wind turbine" phenomenon is particularly prominent.

[0003] "Lost wind turbines" refer to wind turbines in large wind farms whose dynamic response characteristics differ significantly from the main turbine group due to unique internal electrical topology (such as excessively long and complex collector lines) or grid conditions at the connection point (such as differences in impedance characteristics). Under specific operating conditions, these "lost wind turbines" are prone to adverse interactions with the power grid, inducing broadband oscillations ranging from tens to thousands of hertz, including subsynchronous oscillations (SSO), supersynchronous oscillations, and high-frequency oscillations. These oscillations pose a serious threat to the safe and stable operation of the power system.

[0004] To suppress oscillations caused by wind power grid connection, existing technologies have explored various approaches. For example, Chinese patent CN110429235A discloses a method for suppressing subsynchronous resonance by adjusting the parameters of a wind turbine controller. However, this method primarily addresses the overall characteristics of the wind farm, and its applicability and suppression effect are limited for complex broadband oscillations caused by the localized factor of "isolated wind turbines." Another approach, as disclosed in Chinese patent CN111986764A, uses a static var compensator (STATCOM / SVG) to suppress subsynchronous oscillations. However, traditional STATCOM / SVG and other compensation devices have limited compensation capabilities and response bandwidth, making it difficult to effectively handle oscillations over a wide frequency range, especially for higher frequency (e.g., hundreds to thousands of hertz) oscillation components, where their suppression capability is significantly insufficient.

[0005] In view of this, there is an urgent need to provide a broadband oscillation suppression scheme for isolated wind turbines based on SSC network control, which can overcome the above defects, accurately identify the characteristics of isolated wind turbines, and effectively and adaptively reshape the impedance of isolated wind turbines, thereby systematically suppressing broadband oscillations. Summary of the Invention

[0006] In order to at least solve one or more of the technical problems mentioned above, this application proposes a broadband oscillation suppression scheme for residual wind turbines based on SSC network control in several aspects.

[0007] In a first aspect, this application provides a broadband oscillation suppression method for isolated wind turbines based on SSC grid control, comprising: performing online impedance measurement and characteristic analysis on the isolated wind turbine to identify its impedance characteristics and negative impedance frequency band over a wide frequency range; configuring the capacity of the SSC and designing the control parameters of the SSC based on the impedance characteristics and negative impedance frequency band of the isolated wind turbine to determine the SSC capacity and control parameters that can compensate for the negative impedance characteristics of the isolated wind turbine; executing the SSC grid control strategy based on the SSC control parameters to inject compensation power into the grid to reshape the system impedance at the wind farm grid connection point so that it exhibits positive impedance characteristics within the target frequency range; continuously evaluating the effect of impedance reshaping through online monitoring, and dynamically adjusting the control parameters of the SSC according to changes in operating conditions.

[0008] In some embodiments, during the online impedance measurement and characteristic analysis of the abandoned wind turbine, the following steps are performed: At the grid connection point of the wind farm and the connection point of the abandoned wind turbine, online impedance measurement is performed using a frequency sweep signal injection method to obtain impedance measurement data; based on the obtained measurement data, a parameter identification algorithm is used to extract the quantized impedance model of the abandoned wind turbine, and the characteristics of this model in a wide frequency band are analyzed to locate the negative impedance frequency band of the abandoned wind turbine; the severity of the negative impedance frequency band of the abandoned wind turbine is quantified, and the impedance characteristics containing this quantified index are stored in the abandoned wind turbine impedance characteristic database; wherein, the severity of the negative impedance frequency band of the abandoned wind turbine is quantified using a first calculation formula, the first calculation formula being: Severity=Σ(|Re[Z_wind(jω) i )]|×Δf),Re[Z_wind(jω i )]<0, Severity represents the severity of the negative impedance frequency band of the orphaned wind turbine, Z_wind(jω i ω is the i-th frequency point in the negative impedance frequency band of the orphan fan. i The complex impedance, Z_wind is the complex impedance of the isolated wind turbine, j is the imaginary unit, ω i For the i-th frequency point, Re[·] represents the operation of taking the real part, and Δf is the frequency resolution.

[0009] In some embodiments, the capacity configuration of the SSC is performed by a second calculation formula, wherein the second calculation formula is: S_ssc≥α×max{|S_negative|,|S_peak|}×(1+margin), where S_ssc is the capacity of the SSC, S_negative is the compensation capacity required for the negative impedance frequency band, S_peak is the peak power requirement, α is the compensation coefficient, and margin is the safety margin.

[0010] In some embodiments, the following steps are performed during the design of the control parameters of the SSC: establishing an SSC impedance model, wherein the SSC impedance model correlates the control parameters of the SSC with the equivalent output impedance of the SSC; determining the frequency point with the most severe negative impedance in the negative impedance frequency band of the isolated wind turbine as the critical compensation frequency point, and setting the compensation target as follows: at the critical compensation frequency point, the real part of the equivalent output impedance of the SSC is positive and greater than the absolute value of the real part of the negative impedance of the isolated wind turbine; based on the SSC impedance model, determining the control parameters of the SSC through iterative optimization until the set compensation target is met; wherein the control parameters of the SSC include proportional gain, integral gain, and derivative gain.

[0011] In some embodiments, a hierarchical control architecture is adopted during the execution of the SSC network control strategy. The hierarchical control architecture includes: a basic control layer for providing power and voltage support for upper-level control; an impedance reshaping layer for receiving control parameters from the SSC to perform impedance compensation; and a coordination control layer for enabling the SSC and corresponding devices to work together.

[0012] In some embodiments, the impedance reshaping layer performs impedance compensation by implementing a frequency-dependent virtual impedance, the expression of which is: Z_virtual(s)=K1 / (s) 2 +2ζ1ω1s+ω12)+K2 / (s 2 +2ζ2ω2s+ω22)+...+K n / (s 2 +2ζnω n s+ω n 2), where s is the complex frequency variable in the Laplace transform, Z_virtual(s) is the frequency-dependent virtual impedance, n is the total number of target frequency points in the target frequency range, and K i K2 and K n All are gain coefficients, ω1, ω2 and ω n All are target frequency points, ζ1, ζ2 and ζ n All are damping ratios.

[0013] In some embodiments, the frequency-dependent virtual impedance is achieved by designing the instantaneous power reference command of the SSC, which includes an active power command and a reactive power command.

[0014] The expression for the active power command is: P_ssc = P_ref - K_p × BPF(Δf) - K_v × HPF(ΔV), where P_ssc is the instantaneous active power of the SSC, P_ref is the reference value of the instantaneous active power of the SSC, K_p is the frequency droop coefficient, BPF(·) is the bandpass filter, Δf is the difference between the actual frequency of the power grid and the rated frequency, K_v is the high-frequency voltage gain, HPF(·) is the high-pass filter, and ΔV is the difference between the actual voltage value and the reference value. The expression for the reactive power command is: Q_ssc=Q_ref-K_q×BPF(ΔV)-K_i×HPF(ΔI), where Q_ssc is the instantaneous reactive power of SSC, Q_ref is the reference value of instantaneous reactive power of SSC, K_q is the voltage droop coefficient, BPF(·) is the bandpass filter, ΔV is the difference between the actual voltage value and the reference value, K_i is the high-frequency current gain, HPF(·) is the high-pass filter, and ΔI is the difference between the actual current value and the expected current value.

[0015] In some embodiments, the online monitoring includes: using the system impedance exhibiting positive impedance characteristics within the target frequency range as the target impedance; monitoring the system impedance in real time, comparing it with the target impedance, and determining whether there is a deviation; in response to the absence of a deviation, not performing any action; in response to the presence of a deviation, correcting the control parameters of the SSC based on the deviation and a preset parameter sensitivity matrix, so that the deviation is 0; wherein, the control parameters of the SSC are corrected using a third calculation formula, the third calculation formula being: Params_new=Params_old+ΔParams, ΔParams=f(Z_difference,Sensitivity_matrix), where Params_new is the corrected control parameter of the SSC, Params_old is the original control parameter of the SSC, f(·) is the adaptive mapping function, Z_difference is the impedance deviation, and Sensitivity_matrix is ​​the preset parameter sensitivity matrix.

[0016] In some embodiments, the online monitoring further includes: establishing an assessment model composed of multiple weighted risk indicators to perform risk calculation on the system's online monitoring data; determining whether the risk calculation result exceeds a preset threshold; not performing any action if the risk calculation result does not exceed the preset threshold; and triggering a risk warning if the risk calculation result exceeds the preset threshold; wherein, the expression of the assessment model is: Risk_index = w1×Ind1 + w2×Ind2 + ... + wn ×Ind n w1, w2 and w n All are weighting coefficients, Ind1, Ind2, and Ind n All of these are risk indicators.

[0017] In a second aspect, this application provides a broadband oscillation suppression system for isolated wind turbines based on SSC grid control. The system employs the broadband oscillation suppression method for isolated wind turbines based on SSC grid control as described in any embodiment of the first aspect. The system includes: a characteristic analysis module for performing online impedance measurement and characteristic analysis on the isolated wind turbine to identify its impedance characteristics and negative impedance frequency band over a wide frequency range; a compensation parameter acquisition module for configuring the capacity of the SSC and designing the control parameters of the SSC based on the impedance characteristics and negative impedance frequency band of the isolated wind turbine to determine the SSC capacity and control parameters capable of compensating for the negative impedance characteristics of the isolated wind turbine; a grid control strategy execution module for executing the SSC grid control strategy based on the SSC control parameters, injecting compensation power into the grid to reshape the system impedance at the wind farm grid connection point, making it exhibit positive impedance characteristics within the target frequency range; and an adaptive optimization module for continuously evaluating the effect of impedance reshaping through online monitoring and dynamically adjusting the control parameters of the SSC according to changes in operating conditions.

[0018] The broadband oscillation suppression scheme for isolated wind turbines based on SSC grid control, as described above, achieves efficient targeted suppression by precisely locating the negative impedance frequency band, avoiding blind compensation. Secondly, it actively reshapes the system impedance using grid control to completely suppress oscillations, rather than passively responding. Thirdly, through online monitoring and dynamic parameter adjustment mechanisms, the entire system is endowed with strong adaptability and robustness, ensuring that the suppression effect can be reliably maintained at its optimal state for a long period under complex and changing power grid conditions.

[0019] Furthermore, in some embodiments, by injecting frequency sweep signals and using parameter identification algorithms, the complex characteristics of the electrical system are transformed into a precise, wide-bandwidth quantized impedance model, providing a reliable data foundation for subsequent analysis. Secondly, it not only accurately locates negative impedance frequency bands with potential stability risks but also innovatively introduces a quantification formula for severity, thus concretizing the abstract oscillation risk into a measurable indicator, providing a direct, non-empirical design basis for the capacity configuration and parameter design of subsequent compensation devices. Thirdly, by constructing an impedance characteristic database, these key quantified indicators are stored in a structured manner, laying a solid foundation for the systematic management, long-term performance tracking, and automated control strategies of this technical solution.

[0020] Furthermore, in some embodiments, a hierarchical control architecture decouples complex control tasks, ensuring the stability of the underlying power support, the precise execution of core impedance compensation, and the collaborative work between top-level devices, greatly improving the modularity, logic, and reliability of the control system. Secondly, the impedance reshaping layer employs frequency-dependent virtual impedance, which can flexibly and accurately construct the required positive damping characteristics at any target frequency point, achieving precise compensation for the negative impedance of isolated wind turbines, exhibiting extremely high targeting and efficiency. Thirdly, it clarifies the physical implementation path of the virtual impedance, namely, by designing an instantaneous power reference command including bandpass and high-pass filtering stages, successfully mapping the theoretical complex impedance to the instantaneous control quantities of active and reactive power in the SSC, thus providing a clear and effective technical solution for this advanced control strategy.

[0021] Furthermore, in some embodiments, by real-time monitoring of the deviation between the system impedance and the target impedance, and by automatically correcting the control parameters based on a preset parameter sensitivity matrix, a precise and efficient adaptive control closed loop is constructed. This ensures that the impedance reshaping effect remains optimal under dynamic operating conditions, greatly improving the system's robustness and adaptability. Secondly, based on this, a comprehensive evaluation model composed of multiple weighted risk indicators is introduced, enabling quantitative calculation and threshold judgment of the overall system operational risk. This achieves an upgrade from passive correction to proactive early warning, providing a more forward-looking technical guarantee for ensuring the safe and stable operation of the power grid. Attached Figure Description

[0022] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein:

[0023] Figure 1 An exemplary flowchart of a broadband oscillation suppression method for residual solitary wind turbines based on SSC network control, according to an embodiment of this application, is shown.

[0024] Figure 2 An exemplary flowchart illustrating online impedance measurement and characteristic analysis of a lost-orbit fan according to an embodiment of this application is shown;

[0025] Figure 3 An exemplary flowchart for designing control parameters for an SSC according to an embodiment of this application is shown;

[0026] Figure 4 An exemplary flowchart of system impedance monitoring according to an embodiment of this application is shown;

[0027] Figure 5An exemplary flowchart of risk detection according to an embodiment of this application is shown;

[0028] Figure 6 An exemplary structural block diagram of a broadband oscillation suppression system for residual solitary wind turbines based on SSC network control, according to an embodiment of this application, is shown. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0032] Figure 1 An exemplary flowchart of a broadband oscillation suppression method 100 for residual isolated wind turbines based on SSC network control, according to an embodiment of this application, is shown.

[0033] like Figure 1 As shown, in step S110, online impedance measurement and characteristic analysis are performed on the isolated wind turbine to identify its impedance characteristics and negative impedance frequency band in a wide frequency range.

[0034] In the embodiments of this application, the wideband range includes a low-frequency band, a subsynchronous band, and a high-frequency band. Specifically, the low-frequency band has a frequency range of 0.1Hz-10Hz, the subsynchronous band has a frequency range of 10Hz-50Hz, and the high-frequency band has a frequency range >50Hz.

[0035] In the embodiments of this application, the specific process involved in step S110 can be found in [reference needed]. Figure 2 .

[0036] Figure 2 An exemplary flowchart illustrating online impedance measurement and characteristic analysis of a lost-solar fan according to an embodiment of this application is shown.

[0037] like Figure 2 As shown, in step S210, online impedance measurement is performed at the connection point between the wind farm grid and the isolated wind turbine to obtain impedance measurement data using a frequency sweep signal injection method. In step S220, based on the acquired measurement data, a parameter identification algorithm is used to extract the quantized impedance model of the isolated wind turbine, and the characteristics of this model over a wide frequency band are analyzed to locate the negative impedance frequency band of the isolated wind turbine. In step S230, the severity of the negative impedance frequency band of the isolated wind turbine is quantified, and the impedance characteristics containing this quantified index are stored in the isolated wind turbine impedance characteristic database.

[0038] In the embodiments of this application, a set of small-amplitude current signals in a wide frequency range is injected into the power grid by a power electronic converter (such as the inverter of a wind turbine) at preset steps and frequency points, and the voltage and current signals are recorded in real time at the injection points.

[0039] In the embodiments of this application, the following calculation formula is used in the process of obtaining impedance measurement data:

[0040] Z(jω h )=V(jω h ) / I(jω h ), Z(jω h V(jω) is the complex impedance at the h-th measurement frequency point. h ) represents the voltage spectrum at the h-th measurement frequency point, I(jω) h The current spectrum at the h-th measurement frequency point.

[0041] In the embodiments of this application, during the extraction of the quantized impedance model for the abandoned wind turbine, the acquired impedance measurement data is first divided into low-frequency impedance measurement data, sub-synchronous frequency impedance measurement data, and high-frequency impedance measurement data. Next, in the low-frequency band, equivalent resistance and equivalent time constant are used as key parameters to address voltage stability issues. In the sub-synchronous frequency band, resonant frequency, damping ratio, and phase margin are used as key parameters to address sub-synchronous oscillation risks. In the high-frequency band, minimum impedance magnitude, resonant peak value, and phase jump are used as key parameters to address high-frequency oscillation risks. Then, a quantized impedance model is constructed based on the segmentation structure and key parameters of the impedance measurement data.

[0042] By extracting differentiated parameters in different frequency bands (such as focusing on oscillation risk in the subsynchronous band and focusing on resonance peak in the high-frequency band), the limitations of a single model are avoided, and the weak links in stability of different frequency bands are accurately located.

[0043] In the embodiments of this application, it is determined whether the real part of the impedance of the quantized impedance model of the abandoned wind turbine is negative in a wide frequency range, and the frequency range in which the real part of the impedance is less than zero is obtained and taken as the negative impedance frequency band.

[0044] In the embodiments of this application, a first calculation formula is used to quantify the severity of the negative impedance frequency band of the abandoned wind turbine. The first calculation formula is: Severity=Σ(|Re[Z_wind(jω)) i )]|×Δf),Re[Z_wind(jω i )]<0, where Severity is the severity of the negative impedance frequency band of the isolated wind turbine, Z_wind(jω i ω is the i-th frequency point in the negative impedance frequency band of the orphan fan. i The complex impedance, Z_wind is the complex impedance of the isolated wind turbine, j is the imaginary unit, ω i For the i-th frequency point, Re[·] represents the operation of taking the real part, and Δf is the frequency resolution.

[0045] By using the severity index of negative impedance, the abstract oscillation risk is transformed into a comparable value, which can directly guide the subsequent SSC capacity configuration and control parameter design.

[0046] After completing step S110, in step S120, based on the impedance characteristics and negative impedance frequency band of the isolated wind turbine, the capacity configuration and control parameters of the SSC are designed to determine the SSC capacity and control parameters that can compensate for the negative impedance characteristics of the isolated wind turbine.

[0047] In the embodiments of this application, SSC is an overcapacitance static switching camera.

[0048] In the embodiments of this application, the capacity configuration of the SSC is performed using a second calculation formula.

[0049] Specifically, the second calculation formula is: S_ssc≥α×max{|S_negative|, where |S_peak|}×(1+margin), S_ssc is the capacity of SSC, S_negative is the compensation capacity required for the negative impedance frequency band, S_peak is the peak power requirement, α is the compensation coefficient, and margin is the safety margin.

[0050] By configuring capacity through SSC, capacity allocation is based on quantitative system analysis, improving the accuracy and relevance of the design and effectively avoiding insufficient or wasted investment. Introducing compensation coefficients and safety margins not only enhances the flexibility of the configuration scheme to adapt to different system requirements but also reserves necessary redundancy, significantly improving the reliability and security of the final solution.

[0051] The specific process of designing the control parameters for the SSC in the embodiments of this application can be found in [reference needed]. Figure 3 .

[0052] Figure 3 An exemplary flowchart for designing control parameters for an SSC according to an embodiment of this application is shown.

[0053] like Figure 3 As shown, in step S310, an SSC impedance model is established, which correlates the control parameters of the SSC with its equivalent output impedance. In step S320, the frequency point with the most severe negative impedance in the negative impedance frequency band of the isolated wind turbine is determined as the critical compensation frequency point, and the compensation target is set as follows: at the critical compensation frequency point, the real part of the equivalent output impedance of the SSC is positive and greater than the absolute value of the real part of the negative impedance of the isolated wind turbine. In step S330, based on the SSC impedance model, the control parameters of the SSC are determined through iterative optimization until the set compensation target is met.

[0054] Specifically, the control parameters of SSC include proportional gain, integral gain, and derivative gain.

[0055] In the embodiments of this application, the SSC impedance model can be expressed as: Z_ssc(jω)=f(K p K i K d , ω_c, ...), where K p For proportional gain, K i For integral gain, K d Z_ssc(jω) is the differential gain, ω_c is the critical compensation frequency, and Z_ssc(jω) is the impedance of the SSC at the compensation frequency ω.

[0056] By precisely pinpointing the frequency point of maximum negative impedance (i.e., the system's most vulnerable point), the parameter design objective becomes highly focused, significantly simplifying the optimization problem and improving design efficiency and success rate. By using the objective of ensuring the real part of the SSC's equivalent output impedance is positive and greater than the absolute value of the real part of the isolated fan's negative impedance, the abstract concept of system stability is quantified into a calculable and verifiable inequality, ensuring design reliability. Through iterative optimization, the optimal combination of control parameters can be found while satisfying stability margins. This not only guarantees stability but also considers other performance indicators such as the system's dynamic response speed, avoiding sacrificing system performance due to conservative parameter selection.

[0057] After step S120 is completed, in step S130, based on the control parameters of SSC, the grid-connection control strategy of SSC is executed to inject compensation power into the grid to reshape the system impedance of the wind farm grid connection point so that it exhibits positive impedance characteristics in the target frequency range.

[0058] In the embodiments of this application, a layered control architecture is adopted during the execution of the SSC network control strategy. This layered control architecture includes a basic control layer, an impedance reshaping layer, and a coordination control layer. The basic control layer provides power and voltage support for the upper-level control. The impedance reshaping layer receives the control parameters of the SSC to perform impedance compensation. The coordination control layer enables the SSC to work collaboratively with the corresponding devices.

[0059] In the embodiments of this application, the impedance reshaping layer performs impedance compensation by implementing a frequency-dependent virtual impedance, the expression of which is:

[0060] Z_virtual(s) = K1 / (s) 2 +2ζ1ω1s+ω12)+K2 / (s 2 +2ζ2ω2s+ω22)+...+K n / (s 2 +2ζnω n s+ω n 2), where s is the pull

[0061] In the Plas transform, the complex frequency variable Z_virtual(s) is the frequency-dependent virtual impedance, n is the total number of target frequency points in the target frequency range, and K... i K2 and K n All are gain coefficients, ω1, ω2 and ω n All are target frequency points, ζ1, ζ2 and ζ n All are damping ratios. Each term in this expression corresponds to compensation at a target frequency point.

[0062] The impedance reshaping layer performs impedance compensation by implementing frequency-dependent virtual impedance, which can accurately provide the required compensation impedance at multiple target frequency points simultaneously, with minimal impact on the rest of the system's frequency bands. This not only greatly improves the efficiency and performance of oscillation suppression but also gives the system extremely high flexibility.

[0063] In the embodiments of this application, the frequency-dependent virtual impedance is implemented by designing the instantaneous power reference command of the SSC, which includes active power command and reactive power command.

[0064] Specifically, the expression for the active power command is: P_ssc=P_ref-K_p×BPF(Δf)-K_v×HPF(ΔV), where P_ssc is the instantaneous active power of the SSC, P_ref is the reference value of the instantaneous active power of the SSC, K_p is the frequency droop coefficient, BPF(·) is the bandpass filter, Δf is the difference between the actual frequency of the power grid and the rated frequency, K_v is the high-frequency voltage gain, HPF(·) is the high-pass filter, and ΔV is the difference between the actual voltage value and the reference value.

[0065] K_p×BPF(Δf) is specifically responsible for monitoring the fluctuation of the grid frequency in specific low-to-medium frequency bands (such as the subsynchronous oscillation band). Once such frequency oscillation occurs, this term will immediately calculate a reverse active power adjustment, dynamically instructing the SSC to generate or absorb active power to suppress the oscillation frequency, thereby providing precise active power damping.

[0066] The algorithm uses K_v×HPF(ΔV) to specifically scan for high-frequency noise or harmonics in the grid voltage caused by switching of power electronic devices. Once detected, it suppresses these noises by fine-tuning the active power.

[0067] Specifically, the expression for the reactive power command is: Q_ssc=Q_ref-K_q×BPF(ΔV)-K_i×HPF(ΔI), where Q_ssc is the instantaneous reactive power of SSC, Q_ref is the instantaneous reactive power reference value of SSC, K_q is the voltage droop coefficient, BPF(·) is the bandpass filter, ΔV is the difference between the actual voltage value and the reference value, K_i is the high-frequency current gain, HPF(·) is the high-pass filter, and ΔI is the difference between the actual current value and the expected current value.

[0068] The system uses K_q×BPF(ΔV) to specifically monitor voltage fluctuations in the same frequency band. When voltage oscillates as a result, it immediately commands the SSC to dynamically adjust reactive power, smoothing out peaks and valleys in the fluctuating voltage, thereby providing effective reactive power damping.

[0069] K_i×HPF(ΔI) is used to monitor the high-frequency components in the SSC's output current. Upon detecting abnormal high-frequency current, it filters it out by adjusting the reactive power, thereby improving the power quality of the SSC's output.

[0070] By designing the instantaneous power reference command of the SSC and introducing specific filters (BPF and HPF), different processing strategies are implemented for disturbances of different frequencies. Specifically, it uses a bandpass filter (BPF) to accurately capture and suppress low- and mid-frequency power oscillations that cause system instability. Simultaneously, a high-pass filter (HPF) actively identifies and eliminates high-frequency harmonics generated by the equipment itself or the grid background. The advantages of this design are its high degree of specialization and precision: it can use a unified command framework to simultaneously solve multiple stability problems of completely different natures (such as subsynchronous oscillations and high-frequency harmonics), and the various functions are separated by frequency to prevent interference. Therefore, while ensuring system stability, it further improves power quality and optimizes control efficiency and effectiveness.

[0071] After completing step S130, in step S140, the effect of impedance reshaping is continuously evaluated through online monitoring, and the control parameters of SSC are dynamically adjusted according to changes in operating conditions.

[0072] In the embodiments of this application, online monitoring includes system impedance monitoring and risk detection.

[0073] The specific process involved in system impedance monitoring in the embodiments of this application can be found in [reference needed]. Figure 4 .

[0074] Figure 4 An exemplary flowchart of system impedance monitoring according to an embodiment of this application is shown.

[0075] like Figure 4 As shown, in step S410, the system impedance exhibiting positive impedance characteristics within the target frequency range is used as the target impedance. In step S420, the system impedance is monitored in real time and compared with the target impedance to determine if there is a deviation. If no deviation is found, no action is taken in step S430. If a deviation exists, in step S440, the control parameters of the SSC are corrected based on the deviation and a preset parameter sensitivity matrix to make the deviation zero.

[0076] In the embodiments of this application, the expression for the target impedance is: Z_system(jω) m )=Z_wind(jω m )||Z_ssc(jω m )>0, where ω m ∈[ω_min,ω_max],Z_system(jω m ω is the m-th target frequency point of the system within the target frequency range. m Target impedance, Z_wind(jω) m ) represents the m-th target frequency point ω within the target frequency range for the abandoned wind turbine. mTarget impedance, Z_ssc(jω m ω is the m-th target frequency point of SSC in the target frequency range. m The target impedance is ||, where || represents the parallel operation of impedances, ω_min is the lowest frequency in the target frequency range, and ω_max is the highest frequency in the target frequency range.

[0077] In the embodiments of this application, when monitoring system impedance, online impedance measurement is performed at the grid connection point of the wind farm and the connection point of the isolated wind turbine to obtain impedance measurement data by means of frequency sweep signal injection. The calculation formula used in the process of obtaining impedance measurement data is the same as that described above, and will not be repeated here.

[0078] In the embodiments of this application, the control parameters of the SSC are corrected using a third calculation formula. The third calculation formula is: Params_new=Params_old+ΔParams, where ΔParams=f(Z_difference,Sensitivity_matrix), Params_new is the control parameter of the SSC after correction, Params_old is the control parameter of the SSC before correction, f(·) is the adaptive mapping function, Z_difference is the impedance deviation, and Sensitivity_matrix is ​​the preset parameter sensitivity matrix.

[0079] By monitoring the total system impedance in real time, once a deviation from the stable target due to changes in external operating conditions is detected, the system can automatically and accurately calculate the correction amount and update its own control parameters using a preset parameter sensitivity matrix as a knowledge base, thereby pulling the system back to the optimal stable state in real time. This method ensures that the system maintains robust stability under various complex and changing operating conditions, achieving a high degree of automation in operation and maintenance and continuous optimization of control performance.

[0080] The specific process involved in risk detection in the embodiments of this application can be found in [reference needed]. Figure 5 .

[0081] Figure 5 An exemplary flowchart of risk detection according to an embodiment of this application is shown.

[0082] like Figure 5 As shown, in step S510, an assessment model consisting of multiple weighted risk indicators is established to calculate the risk of the system's online monitoring data. In step S520, it is determined whether the risk calculation result exceeds a preset threshold. If the risk calculation result does not exceed the preset threshold, no action is taken in step S530. If the risk calculation result exceeds the preset threshold, a risk warning is triggered in step S540.

[0083] In the embodiments of this application, the aforementioned preset threshold is set according to actual needs and historical experience, and this application does not impose any restrictions on it.

[0084] Specifically, the expression for the aforementioned evaluation model is: Risk_index = w1 × Ind1 + w2 × Ind2 + ... + w n ×Ind n w1, w2 and w n All are weighting coefficients, Ind1, Ind2, and Ind n All of these are risk indicators.

[0085] By constructing a comprehensive evaluation model composed of multiple weighted indicators, various dispersed factors affecting system stability are integrated into an intuitive risk index, which triggers an early warning when it exceeds a safety threshold. This not only clarifies and quantifies the vague concept of risk, providing a more comprehensive and reliable assessment of system status, but more importantly, it can identify the trend of risk accumulation before oscillations actually occur, buying valuable time for manual intervention or system self-protection, thereby greatly improving the safety and operational reliability of the entire system.

[0086] In summary, through the broadband oscillation suppression scheme for isolated wind turbines based on SSC grid control provided above, this application embodiment achieves efficient targeted suppression by precisely locating the negative impedance frequency band, avoiding blind compensation. Secondly, it actively reshapes the system impedance using grid control to completely suppress oscillations, rather than passively responding. Thirdly, through online monitoring and dynamic parameter adjustment mechanisms, the entire system is endowed with strong adaptive capabilities and robustness, ensuring that the suppression effect can be reliably maintained at its optimal state for a long time under complex and ever-changing power grid conditions.

[0087] Furthermore, in some embodiments, by injecting frequency sweep signals and using parameter identification algorithms, the complex characteristics of the electrical system are transformed into a precise, wide-bandwidth quantized impedance model, providing a reliable data foundation for subsequent analysis. Secondly, it not only accurately locates negative impedance frequency bands with potential stability risks but also innovatively introduces a quantification formula for severity, thus concretizing the abstract oscillation risk into a measurable indicator, providing a direct, non-empirical design basis for the capacity configuration and parameter design of subsequent compensation devices. Thirdly, by constructing an impedance characteristic database, these key quantified indicators are stored in a structured manner, laying a solid foundation for the systematic management, long-term performance tracking, and automated control strategies of this technical solution.

[0088] Furthermore, in some embodiments, a hierarchical control architecture decouples complex control tasks, ensuring the stability of the underlying power support, the precise execution of core impedance compensation, and the collaborative work between top-level devices, greatly improving the modularity, logic, and reliability of the control system. Secondly, the impedance reshaping layer employs frequency-dependent virtual impedance, which can flexibly and accurately construct the required positive damping characteristics at any target frequency point, achieving precise compensation for the negative impedance of isolated wind turbines, exhibiting extremely high targeting and efficiency. Thirdly, it clarifies the physical implementation path of the virtual impedance, namely, by designing an instantaneous power reference command including bandpass and high-pass filtering stages, successfully mapping the theoretical complex impedance to the instantaneous control quantities of active and reactive power in the SSC, thus providing a clear and effective technical solution for this advanced control strategy.

[0089] Furthermore, in some embodiments, by real-time monitoring of the deviation between the system impedance and the target impedance, and by automatically correcting the control parameters based on a preset parameter sensitivity matrix, a precise and efficient adaptive control closed loop is constructed. This ensures that the impedance reshaping effect remains optimal under dynamic operating conditions, greatly improving the system's robustness and adaptability. Secondly, based on this, a comprehensive evaluation model composed of multiple weighted risk indicators is introduced, enabling quantitative calculation and threshold judgment of the overall system operational risk. This achieves an upgrade from passive correction to proactive early warning, providing a more forward-looking technical guarantee for ensuring the safe and stable operation of the power grid.

[0090] This application also provides a broadband oscillation suppression system for isolated wind turbines based on SSC network control. It can use the aforementioned broadband oscillation suppression method 100 for isolated wind turbines based on SSC network control to suppress broadband oscillations of isolated wind turbines, or other methods can be used to suppress broadband oscillations of isolated wind turbines. This application does not limit the methods.

[0091] Figure 6 An exemplary structural block diagram of a broadband oscillation suppression system for residual solitary wind turbines based on SSC network control, according to an embodiment of this application, is shown.

[0092] like Figure 6 As shown, the system 600 includes a feature analysis module 610, a compensation parameter acquisition module 620, a network construction control strategy execution module 630, and an adaptive optimization module 640. In the embodiments of this application, the feature analysis module 610, the compensation parameter acquisition module 620, the network construction control strategy execution module 630, and the adaptive optimization module 640 may be separate units or integrated into the same controller; this application does not impose any restrictions here.

[0093] Specifically, the characteristic analysis module 610 is used to perform online impedance measurement and characteristic analysis on the abandoned solenoid fan to identify its impedance characteristics and negative impedance frequency band over a wide frequency range.

[0094] Specifically, the compensation parameter acquisition module 620 is used to configure the capacity of the SSC and design the control parameters of the SSC based on the impedance characteristics and negative impedance frequency band of the isolated wind turbine, so as to determine the SSC capacity and control parameters that can compensate for the negative impedance characteristics of the isolated wind turbine.

[0095] Specifically, the grid control strategy execution module 630 is used to execute the grid control strategy of SSC based on the control parameters of SSC, and inject compensation power into the grid to reshape the system impedance of the wind farm grid connection point so that it exhibits positive impedance characteristics in the target frequency range.

[0096] Specifically, the adaptive optimization module 640 is used to continuously evaluate the effect of impedance reshaping through online monitoring and dynamically adjust the control parameters of the SSC according to changes in operating conditions.

[0097] When system 600 employs the aforementioned method 100 for suppressing broadband oscillations in isolated wind turbines based on SSC network control, the aforementioned step S110 is executed through the characteristic analysis module 610, the aforementioned step S120 is executed through the compensation parameter acquisition module 620, the aforementioned step S130 is executed through the network control strategy execution module 630, and the aforementioned step S140 is executed through the adaptive optimization module 640. The specific execution process can be found above and will not be repeated here.

[0098] While numerous embodiments of this application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will arise for those skilled in the art without departing from the spirit and intent of this application. It should be understood that various alternatives to the embodiments of this application described herein may be employed in the practice of this application. The appended claims are intended to define the scope of protection of this application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for suppressing broadband oscillations in residual wind turbines based on SSC network control, characterized in that, include: Online impedance measurement and characteristic analysis were performed on abandoned wind turbines to identify their impedance characteristics and negative impedance frequency bands over a wide frequency range. Based on the impedance characteristics and negative impedance frequency band of the abandoned wind turbine, the capacity configuration and control parameters of the SSC are designed to determine the SSC capacity and control parameters that can compensate for the negative impedance characteristics of the abandoned wind turbine. Based on the control parameters of SSC, the grid-connection control strategy of SSC is executed to inject compensation power into the grid to reshape the system impedance of the wind farm grid connection point so that it exhibits positive impedance characteristics in the target frequency range. The effect of impedance reshaping is continuously evaluated through online monitoring, and the control parameters of the SSC are dynamically adjusted according to changes in operating conditions.

2. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 1, characterized in that, The following steps are performed during the online impedance measurement and characteristic analysis of the abandoned wind turbine: At the grid connection point of the wind farm and the connection point of the isolated wind turbine, online impedance measurement was performed by the frequency sweep signal injection method to obtain impedance measurement data. Based on the acquired measurement data, a parameter identification algorithm is used to extract the quantized impedance model of the isolated wind turbine, and the characteristics of the model in a wide frequency range are analyzed to locate the negative impedance frequency band of the isolated wind turbine. The severity of the negative impedance frequency band of the abandoned wind turbine is quantitatively calculated, and the impedance characteristics containing the quantitative index are stored in the abandoned wind turbine impedance characteristic database. The severity of the negative impedance frequency band of the abandoned wind turbine is quantified using the first calculation formula, which is: Severity=Σ(|Re[Z_wind(jω i )]|×Δf),Re[Z_wind(jω i )]<0, Severity represents the severity of the negative impedance frequency band of the orphaned wind turbine, Z_wind(jω i ω is the i-th frequency point in the negative impedance frequency band of the orphan fan. i The complex impedance, Z_wind is the complex impedance of the isolated wind turbine, j is the imaginary unit, ω i For the i-th frequency point, Re[·] represents the operation of taking the real part, and Δf is the frequency resolution.

3. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 1, characterized in that, SSC capacity is configured using a second calculation formula, which is: S_ssc≥α×max{|S_negative|,|S_peak|}×(1+margin), where S_ssc is the capacity of SSC, S_negative is the compensation capacity required for the negative impedance frequency band, S_peak is the peak power requirement, α is the compensation coefficient, and margin is the safety margin.

4. The method for suppressing broadband oscillations of residual-arc wind turbines based on SSC network control according to claim 1, characterized in that, The following steps are performed during the design of SSC control parameters: Establish an SSC impedance model, in which the SSC control parameters are related to the SSC's equivalent output impedance. The frequency point with the most severe negative impedance in the negative impedance band of the abandoned wind turbine is determined as the critical compensation frequency point, and the compensation target is set as follows: at the critical compensation frequency point, the real part of the equivalent output impedance of the SSC is positive and greater than the absolute value of the real part of the negative impedance of the abandoned wind turbine. Based on the SSC impedance model, the control parameters of the SSC are determined through iterative optimization until the set compensation target is met. The control parameters of SSC include proportional gain, integral gain, and derivative gain.

5. The method for suppressing broadband oscillations of residual-arc wind turbines based on SSC network control according to claim 1, characterized in that, In executing the SSC network control strategy, a layered control architecture is adopted, which includes: The basic control layer is used to provide power and voltage support for the upper-level control. An impedance reshaping layer is used to receive control parameters from the SSC to perform impedance compensation; and The coordination and control layer is used to enable the SSC to work collaboratively with the corresponding equipment.

6. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 5, characterized in that, The impedance reshaping layer performs impedance compensation by implementing a frequency-dependent virtual impedance, the expression of which is: Z_virtual(s) = K1 / (s 2 + 2ζ1ω1s + ω1²) + K2 / (s 2 + 2ζ2ω2s + ω2²) +... + K n / (s 2 + 2ζnω n s + ω n ²), where s is the Laplace In the Plas transform, the complex frequency variable Z_virtual(s) is the frequency-dependent virtual impedance, n is the total number of target frequency points in the target frequency range, and K... i K2 and K n All are gain coefficients, ω1, ω2 and ω n All are target frequency points, ζ1, ζ2 and ζ n All are damping ratios.

7. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 6, characterized in that, The frequency-dependent virtual impedance is achieved by designing the instantaneous power reference command of the SSC, which includes active power command and reactive power command. The expression for the active power command is: P_ssc=P_ref-K_p×BPF(Δf)-K_v×HPF(ΔV), where P_ssc is the instantaneous active power of SSC, P_ref is the reference value of the instantaneous active power of SSC, K_p is the frequency droop coefficient, BPF(·) is the bandpass filter, Δf is the difference between the actual frequency of the power grid and the rated frequency, K_v is the high-frequency voltage gain, HPF(·) is the high-pass filter, and ΔV is the difference between the actual voltage value and the reference value. The expression for the reactive power command is: Q_ssc=Q_ref-K_q×BPF(ΔV)-K_i×HPF(ΔI), where Q_ssc is the instantaneous reactive power of SSC, Q_ref is the reference value of instantaneous reactive power of SSC, K_q is the voltage droop coefficient, BPF(·) is the bandpass filter, ΔV is the difference between the actual voltage value and the reference value, K_i is the high-frequency current gain, HPF(·) is the high-pass filter, and ΔI is the difference between the actual current value and the expected current value.

8. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 1, characterized in that, The online monitoring includes: The system impedance that exhibits positive impedance characteristics within the target frequency range will be used as the target impedance. Monitor the system impedance in real time and compare it with the target impedance to determine if there is a deviation. If there is no deviation, no action is taken; In response to the existence of a deviation, the control parameters of the SSC are corrected according to the deviation and the preset parameter sensitivity matrix so that the deviation is 0. The control parameters of the SSC are corrected using a third calculation formula, which is as follows: Params_new = Params_old + ΔParams, ΔParams = f(Z_difference, Sensitivity_matrix), where Params_new are the control parameters of the corrected SSC, Params_old are the control parameters of the original SSC, f(·) is the adaptive mapping function, Z_difference is the impedance deviation, and Sensitivity_matrix is ​​the preset parameter sensitivity matrix.

9. The method for suppressing broadband oscillations of residual wind turbines based on SSC network control according to claim 8, characterized in that, The online monitoring also includes: An assessment model consisting of multiple weighted risk indicators is established to calculate the risk of online monitoring data of the system. Determine whether the risk calculation result exceeds a preset threshold; If the risk calculation result does not exceed the preset threshold, no action will be taken; A risk warning is triggered when the risk calculation result exceeds a preset threshold. The expression for the evaluation model is: Risk_index = w1 × Ind1 + w2 × Ind2 + ... + w n ×Ind n w1, w2 and w n All are weighting coefficients, Ind1, Ind2, and Ind n All of these are risk indicators.

10. A broadband oscillation suppression system for residual solitary wind turbines based on SSC network control, characterized in that, The broadband oscillation suppression method for residual-isolator wind turbines based on SSC network control as described in any one of claims 1-9 is used to suppress the broadband oscillation of residual-isolator wind turbines, wherein the system comprises: The characteristic analysis module is used to perform online impedance measurement and characteristic analysis on abandoned solenoid fans to identify their impedance characteristics and negative impedance frequency bands over a wide frequency range. The compensation parameter acquisition module is used to configure the capacity of the SSC and design the control parameters of the SSC based on the impedance characteristics and negative impedance frequency band of the isolated wind turbine, so as to determine the SSC capacity and control parameters that can compensate for the negative impedance characteristics of the isolated wind turbine. The grid construction control strategy execution module is used to execute the grid construction control strategy of SSC based on the control parameters of SSC, and inject compensation power into the grid to reshape the system impedance of the wind farm grid connection point so that it exhibits positive impedance characteristics in the target frequency range. The adaptive optimization module is used to continuously evaluate the effect of impedance reshaping through online monitoring and dynamically adjust the control parameters of the SSC according to changes in operating conditions.

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