Wind turbine control parameter identification method, device and equipment and readable storage medium

Abstract

The application discloses a wind turbine control parameter identification method, device and equipment and a readable storage medium. The method comprises the following steps: determining the measured power of a target wind turbine, the feasible value and search range of each parameter to be identified; calculating the active sensitivity and reactive sensitivity of each parameter to be identified; performing simulation on the target wind turbine according to the identification priority and in combination with the feasible value and search range of each parameter to be identified to obtain a simulation result; based on the simulation result and the measured power, the feasible value and search range are updated for the first time, and each parameter to be identified is disturbed and checked; if the check fails, the feasible value and search range of each parameter to be identified are updated for the second time, and the calculation of the active sensitivity and reactive sensitivity is performed again; if the check passes, the calculation of the active sensitivity and reactive sensitivity is performed again; and the final identification value of each parameter to be identified is obtained. It can be seen that the parameter identification of the wind turbine can be completed through the closed-loop correction mode.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and more specifically, to a method, apparatus, equipment, and readable storage medium for identifying control parameters of wind turbine generators. Background Technology

[0002] In power systems, building simulation models of wind turbines is essential for analyzing wind farm operation characteristics, assessing grid connection safety, and studying transient responses. The dynamic response, fault ride-through, and active power recovery characteristics of wind turbines are primarily determined by control parameters; therefore, these control parameters directly impact the model fitting accuracy. However, wind turbine manufacturers are reluctant to disclose their control parameters, making it difficult to obtain these parameters for current modeling work. Summary of the Invention

[0003] In view of this, this application provides a method, apparatus, device and readable storage medium for identifying control parameters of wind turbine generators, in order to solve the shortcomings of the prior art in that it is difficult to model direct-drive wind turbine generators.

[0004] To achieve the above objectives, the following solution is proposed:

[0005] A method for identifying control parameters of a wind turbine generator set includes:

[0006] Determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range for each parameter to be identified;

[0007] Calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0008] Based on the active and reactive sensitivity of each parameter to be identified, the identification priority of each parameter to be identified is determined.

[0009] According to the identification priority of each parameter to be identified, the target wind turbine is assigned parameter values ​​and simulated in turn, combining the feasible value and search range of each parameter to be identified, to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0010] After updating the feasible values ​​and search range of all parameters to be identified in this round, a perturbation verification is performed on each parameter to be identified.

[0011] If any of the parameters to be identified fails the verification in this round, the feasible values ​​and search range of each parameter to be identified will be updated a second time, and the process will return to the step of calculating the active and reactive sensitivity of each parameter to be identified.

[0012] If each parameter to be identified passes the verification in this round, the process will directly return to the step of calculating the active and reactive sensitivity of each parameter to be identified until the preset stopping condition is met; the feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

[0013] Optionally, determining the identification priority of each parameter to be identified based on its active power sensitivity and reactive power sensitivity includes:

[0014] Calculate the weighted value of the active sensitivity and reactive sensitivity for each parameter to be identified;

[0015] The parameters to be identified are sorted in descending order of weighted value to determine the identification priority of each parameter.

[0016] Optionally, by combining the feasible values ​​and search ranges of each parameter to be identified, parameter assignment and simulation are performed on the target wind turbine to obtain simulation results, including:

[0017] For each parameter to be identified, the current search step size of the parameter is determined based on the search range, active power sensitivity, and reactive power sensitivity of the parameter to be identified.

[0018] Based on the current search step size and the search range, determine multiple currently feasible solutions for the parameter to be identified;

[0019] Each current feasible solution is used as the simulation value of the parameter to be identified in turn, and the simulation result of each current feasible solution is determined.

[0020] Optionally, determining the current search step size of the parameter to be identified based on the search range, active power sensitivity, and reactive power sensitivity includes:

[0021] The search range, active sensitivity, and reactive sensitivity of the parameter to be identified are substituted into the preset step size calculation function to calculate the current search step size of the parameter to be identified.

[0022] The step size calculation function is as follows:

[0023] ;

[0024] In the formula, Let be the current search step size for the i-th parameter to be identified; This is the step size adjustment coefficient; This represents the upper limit of the search range for the i-th parameter to be identified; This is the lower limit of the search range for the i-th parameter to be identified; This is the weighted value of the active and reactive sensitivity of the i-th parameter to be identified; The maximum value among the weighted values ​​of active and reactive sensitivity for each parameter to be identified; To prevent zero coefficient.

[0025] Optionally, the perturbation verification of each parameter to be identified includes:

[0026] Simulation calculations are performed based on the updated feasible values ​​of each parameter to be identified to determine the results of this round of simulation.

[0027] Select multiple verification parameters from each parameter to be identified;

[0028] Centered on the feasible value of each verification parameter, generate multiple discrete perturbation points corresponding to the verification parameter;

[0029] By combining and matching discrete disturbance points with different verification parameters, multiple sets of parameter combinations are obtained;

[0030] Determine the verification simulation power for each parameter combination, and select the parameter combination with the smallest difference between the corresponding verification simulation power and the measured power as the optimal parameter combination;

[0031] If the difference between the verification simulation power of the optimal parameter combination and the simulation result of this round is less than a preset comparison threshold, then it is determined that each parameter to be identified in this round has passed the verification; otherwise, it is determined that each parameter to be identified in this round has failed the verification.

[0032] Optionally, the step of selecting multiple verification parameters from the various parameters to be identified includes:

[0033] Based on the identification priority of each parameter to be identified, the first N parameters to be identified are selected as each verification parameter.

[0034] Optionally, the secondary update of the feasible values ​​and search range of each parameter to be identified includes:

[0035] The feasible values ​​of each parameter to be identified are updated according to the optimal parameter combination.

[0036] Based on the optimal parameter combination and the current search step size of each parameter to be identified, the search range of the corresponding parameter to be identified is updated.

[0037] A wind turbine control parameter identification device, comprising:

[0038] The determination module is used to determine the measured power of the target wind turbine and multiple parameters to be identified, and to determine the feasible value and search range of each parameter to be identified;

[0039] The calculation module is used to calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0040] The sorting module is used to determine the identification priority of each parameter based on its active and reactive power sensitivity.

[0041] The simulation module is used to assign parameters and simulate the target wind turbine according to the identification priority of each parameter to be identified, and in turn combine the feasible value and search range of each parameter to be identified to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0042] The verification module is used to perform perturbation verification on each parameter to be identified after updating the feasible values ​​and search range of all parameters to be identified in this round. If each parameter to be identified fails the verification in this round, the update module is called. If each parameter to be identified passes the verification in this round, the calculation module is called until the preset stopping condition is met. The final feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

[0043] The update module is used to update the feasible values ​​and search range of each parameter to be identified, and call the calculation module until the preset stopping condition is met.

[0044] A wind turbine control parameter identification device includes a memory and a processor;

[0045] The memory is used to store programs;

[0046] The processor is used to execute the program to implement each step of the wind turbine control parameter identification method described above.

[0047] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of the wind turbine control parameter identification method described above.

[0048] As can be seen from the above technical solution, the wind turbine control parameter identification method provided in this application can determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range of each parameter to be identified; calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified, whereby each active power sensitivity characterizes the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity characterizes the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity; and based on the active power sensitivity and reactive power sensitivity of each parameter to be identified, determine the optimal identification parameters for each parameter to be identified. Priority; based on this, this application quantifies the influence of different parameters on the unit's output response from both active and reactive power dimensions, and processes each parameter to be identified sequentially according to the magnitude of its influence, avoiding blind synchronous identification that would slow down the identification process; then, by combining the feasible values ​​and search ranges of each parameter to be identified in sequence according to their identification priority, the target wind turbine is subjected to parameter assignment and simulation to obtain simulation results; based on the simulation results and the measured power, the feasible values ​​and search ranges of each parameter to be identified are initially updated; this application can be performed according to priority. The simulation process involves sequentially assigning values ​​and using feedback from measured power as a benchmark to complete the initial iterative correction of the feasible values ​​and search range of the parameters, gradually narrowing the parameter error range. To avoid numerical update deviations, after updating the feasible values ​​and search ranges of all parameters to be identified in this round, a perturbation check can be performed on each parameter. Therefore, after the initial parameter update in a single round, this application uniformly conducts multi-parameter perturbation checks to detect the stability and adaptability of the current parameter combination, avoiding the local optimum problem caused by a single correction. If any parameter to be identified fails the check in this round, the feasible values ​​and search ranges of each parameter are updated a second time. The process returns to the step of calculating the active and reactive power sensitivity of each parameter to be identified. If each parameter to be identified passes the verification in this round, the process returns directly to the step of calculating the active and reactive power sensitivity of each parameter to be identified until the preset stopping condition is met. The feasible value of each parameter to be identified is the final identified value of the corresponding parameter. Based on this, this application can perform secondary range updates or direct iterative loops based on the verification results, continuously optimizing the parameter range in a closed loop, and continuously narrowing the matching degree between the simulation response and the measured operating conditions until a converged and stable feasible value is output, thus completing the multi-parameter identification. It can be seen that this application can complete the parameter identification of wind turbines through a full-process multi-parameter identification method of gradual boundary shrinkage, dual-sensitivity quantitative evaluation, priority hierarchical identification, hierarchical iterative update, and disturbance verification closed-loop correction. By relying on the dual observation sensitivity of active and reactive power to determine the priority, and simulating step by step according to the priority to reduce computational redundancy, combined with the disturbance verification mechanism, the problems of identification divergence, local optima, and poor parameter adaptability are effectively avoided, thus accelerating the modeling efficiency and reliability of wind turbines. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a flowchart of a wind turbine control parameter identification method disclosed in an embodiment of this application;

[0051] Figure 2 This is a structural block diagram of a wind turbine control parameter identification device disclosed in an embodiment of this application;

[0052] Figure 3 This is a hardware structure block diagram of a wind turbine control parameter identification device disclosed in an embodiment of this application. Detailed Implementation

[0053] 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 embodiments of this application, and not all embodiments. 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.

[0054] This application provides a method for identifying control parameters of a wind turbine generator. This method can be applied to various power grid simulation systems or wind farm simulation systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0055] Next, combine Figure 1 The wind turbine control parameter identification method of this application is described in detail, including the following steps:

[0056] Step S1: Determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range of each parameter to be identified.

[0057] Specifically, the measured reactive power curve and measured active power curve of the target wind turbine can be obtained during actual operation.

[0058] Each parameter to be identified may include the proportional coefficient k of the current inner-loop proportional-integral (PI) controller. pc Integral coefficient k icThe proportional coefficient k of the DC voltage outer loop PI controller pudc Integral coefficient k iudc The proportional coefficient k of the phase-locked loop PI controller ppll Integral coefficient k ipll .

[0059] Based on relevant data about wind turbine units, initial values ​​and upper and lower limits can be assigned to each parameter to be identified.

[0060] Step S2: Calculate the active sensitivity and reactive sensitivity of each parameter to be identified.

[0061] Specifically, each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0062] The active sensitivity calculation formula for each parameter to be identified is as follows:

[0063] ;

[0064] The reactive power sensitivity calculation formula for each parameter to be identified is as follows:

[0065] ;

[0066] Where, Δθ i For the parameter to be identified θ i The change in θ i Let i be the i-th parameter to be identified; The active power corresponding to the current feasible value of each parameter to be identified; The reactive power corresponding to the current feasible value of each parameter to be identified; n is the total number of observation points.

[0067] Step S3: Determine the identification priority of each parameter based on its active and reactive power sensitivity.

[0068] Specifically, the active and reactive sensitivity of each parameter to be identified can be numerically calculated, and the identification priority of each parameter to be identified can be determined based on the numerical calculation results.

[0069] Step S4: According to the identification priority of each parameter to be identified, the target wind turbine is assigned parameter values ​​and simulated in turn, combining the feasible value and search range of each parameter to be identified, to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0070] Specifically, according to the priority of each parameter to be identified, the current feasible value of each parameter to be identified, the search range of this round, and the search step size of this round can be combined in turn to assign values ​​to the parameters to be identified of the target wind turbine and carry out simulation to obtain the simulated power of each parameter to be identified under different values.

[0071] The simulated power and measured power obtained from each simulation can be compared. The feasible value of the corresponding parameter to be identified is updated according to the difference between the two. The search range of the parameter to be identified is updated based on the feasible value. After the initial update of a single parameter to be identified is completed, the next parameter to be identified with the next identification priority is processed in order until all parameters to be identified in this round have completed the initial update. Then, the perturbation verification of each parameter to be identified is performed in step S5.

[0072] Step S5: After updating the feasible values ​​and search ranges of all parameters to be identified in this round, perform perturbation verification on each parameter to be identified. If any parameter to be identified fails the verification in this round, proceed to step S6. If any parameter to be identified passes the verification in this round, return directly to step S2 if the preset stopping condition is not met; the final feasible value of each parameter to be identified is the final identified value of the corresponding parameter.

[0073] Specifically, perturbation verification can be performed on each parameter to be identified based on the updated feasible value of each parameter to be identified.

[0074] If any of the parameters to be identified fails the verification, step S6 can be executed.

[0075] When each parameter to be identified passes the verification, the process can return to step S2 until any of the following stopping conditions are met: the error between the latest simulation result and the measured power is less than the preset error threshold; the error between the simulation results and the measured power for two or more consecutive rounds is less than the preset convergence threshold; and the search range width of each parameter to be identified is less than the preset width threshold.

[0076] The feasible values ​​of each parameter to be identified can be used as the final identification result.

[0077] Step S6: Update the feasible values ​​and search range of each parameter to be identified for the second time, and return to execute step S2.

[0078] Specifically, when any parameter to be identified fails the verification, the feasible value and search range of each parameter to be identified can be updated a second time according to the verification process and the search step size of this round, and then the process returns to step S2.

[0079] As can be seen from the above technical solution, the wind turbine control parameter identification method provided in this application can determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range of each parameter to be identified; calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified, whereby each active power sensitivity characterizes the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity characterizes the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity; and based on the active power sensitivity and reactive power sensitivity of each parameter to be identified, determine the optimal identification parameters for each parameter to be identified. Priority; based on this, this application quantifies the influence of different parameters on the unit's output response from both active and reactive power dimensions, and processes each parameter to be identified sequentially according to the magnitude of its influence, avoiding blind synchronous identification that would slow down the identification process; then, by combining the feasible values ​​and search ranges of each parameter to be identified in sequence according to their identification priority, the target wind turbine is subjected to parameter assignment and simulation to obtain simulation results; based on the simulation results and the measured power, the feasible values ​​and search ranges of each parameter to be identified are initially updated; this application can be performed according to priority. The simulation process involves sequentially assigning values ​​and using feedback from measured power as a benchmark to complete the initial iterative correction of the feasible values ​​and search range of the parameters, gradually narrowing the parameter error range. To avoid numerical update deviations, after updating the feasible values ​​and search ranges of all parameters to be identified in this round, a perturbation check can be performed on each parameter. Therefore, after the initial parameter update in a single round, this application uniformly conducts multi-parameter perturbation checks to detect the stability and adaptability of the current parameter combination, avoiding the local optimum problem caused by a single correction. If any parameter to be identified fails the check in this round, the feasible values ​​and search ranges of each parameter are updated a second time. The process returns to the step of calculating the active and reactive power sensitivity of each parameter to be identified. If each parameter to be identified passes the verification in this round, the process returns directly to the step of calculating the active and reactive power sensitivity of each parameter to be identified until the preset stopping condition is met. The feasible value of each parameter to be identified is the final identified value of the corresponding parameter. Based on this, this application can perform secondary range updates or direct iterative loops based on the verification results, continuously optimizing the parameter range in a closed loop, and continuously narrowing the matching degree between the simulation response and the measured operating conditions until a converged and stable feasible value is output, thus completing the multi-parameter identification. It can be seen that this application can complete the parameter identification of wind turbines through a full-process multi-parameter identification method of gradual boundary shrinkage, dual-sensitivity quantitative evaluation, priority hierarchical identification, hierarchical iterative update, and disturbance verification closed-loop correction. By relying on the dual observation sensitivity of active and reactive power to determine the priority, and simulating step by step according to the priority to reduce computational redundancy, combined with the disturbance verification mechanism, the problems of identification divergence, local optima, and poor parameter adaptability are effectively avoided, thus accelerating the modeling efficiency and reliability of wind turbines.

[0080] In some embodiments of this application, the process of step S2, calculating the active power sensitivity and reactive power sensitivity of each parameter to be identified, is described in detail below:

[0081] S20. Calculate the weighted value of the active sensitivity and reactive sensitivity of each parameter to be identified.

[0082] Specifically, the weighted values ​​of the active and reactive sensitivity of each parameter to be identified can be calculated in various ways.

[0083] For example, the active power weight of active sensitivity can be determined based on the response of active power to parameter disturbances of each parameter to be identified.

[0084] The reactive power weight of active power sensitivity is determined based on the reactive power response to parameter disturbances of each parameter to be identified.

[0085] The weighted value is obtained by adding the product of the active power sensitivity and active power weight of each parameter to be identified to the product of the reactive power sensitivity and reactive power weight of that parameter.

[0086] For example, the sum of the active sensitivity and reactive sensitivity of each parameter to be identified can be directly calculated to obtain the weighted value.

[0087] S21. Sort the parameters to be identified in descending order of weighted value to determine the identification priority of each parameter.

[0088] Specifically, the larger the weighting value, the greater the influence of the parameter to be identified on the active and reactive power output of the wind turbine.

[0089] Therefore, parameters with larger weights can be prioritized for identification, and parameters with greater impact can be corrected first, which can reduce the overall parameter error more quickly and improve the efficiency of iterative optimization.

[0090] As can be seen from the above technical solution, this embodiment provides an optional method for calculating the active and reactive sensitivity of each parameter to be identified. Through the above method, the comprehensive influence of different parameters to be identified on the output of the wind turbine can be quantified. Based on the degree of influence, the parameters with greater influence are gradually approximated, ensuring that the parameters with greater influence are corrected first, and the overall parameter error range is narrowed more quickly. This effectively improves the efficiency of overall iterative optimization and reduces the computational redundancy caused by simultaneous identification of multiple parameters.

[0091] In some embodiments of this application, the process of assigning parameters and simulating the target wind turbine in step S4 by combining the feasible values ​​and search ranges of each parameter to be identified, and obtaining the simulation results, is described in detail below:

[0092] S40. For each parameter to be identified, determine the current search step size of the parameter to be identified based on the search range, active power sensitivity, and reactive power sensitivity of the parameter to be identified.

[0093] Specifically, the search step size of the parameter to be identified in this round can be calculated based on the search range width of the parameter to be identified, the weighted value of the parameter to be identified, and the maximum weighted value in this round.

[0094] S41. Based on the current search step size and the search range, determine multiple currently feasible solutions for the parameter to be identified.

[0095] Specifically, starting from the lower limit of the search range, the search is iterated step by step according to the current search step size to obtain multiple currently feasible solutions distributed within the current search range.

[0096] S42. Sequentially use each current feasible solution as the simulation value of the parameter to be identified, and determine the simulation result of each current feasible solution.

[0097] Specifically, each current feasible solution is used as the simulation value of the corresponding parameter to be identified in turn. The feasible values ​​of the other updated parameters to be identified in this round and the feasible values ​​of the unupdated parameters in the previous round are kept. Simulation calculations are carried out to obtain the simulated reactive power curve and simulated active power curve corresponding to the current feasible solution.

[0098] The reactive power difference can be obtained by calculating the difference between the simulated reactive power curve and the measured reactive power curve corresponding to each current feasible solution; the active power difference can be obtained by calculating the difference between the simulated active power curve and the measured active power curve corresponding to each current feasible solution; the average value between the reactive power difference and the active power difference of each current feasible solution can be calculated as the error of the current feasible solution; the current feasible solution with the lowest error is selected as the new feasible value of the parameter to be identified.

[0099] Using the current search step size as the numerical difference, two current feasible solutions on both sides of the new feasible value can be selected, and the numerical interval enclosed by the two solutions can be defined as the new search range for the corresponding parameter to be identified.

[0100] Specifically, for each current feasible solution, multiple sets of matching points can be selected from the simulated reactive power curve and the measured reactive power curve of the current feasible solution. The difference between the simulated reactive power and the measured reactive power corresponding to each set of matching points is calculated, and the average value of each difference is calculated as the reactive power difference value of the current feasible solution. Similarly, multiple sets of matching points can be selected from the simulated active power curve and the measured active power curve of the current feasible solution. The difference between the simulated active power and the measured active power corresponding to each set of matching points is calculated, and the average value of each difference is calculated as the reactive power difference value of the current feasible solution.

[0101] As can be seen from the above technical solution, this embodiment provides an optional method for assigning parameters and simulating the target wind turbine by combining the feasible values ​​and search ranges of each parameter to be identified, and obtaining simulation results. Through this method, the search step size for this round can be determined by combining the influence degree and search range of the parameter to be identified itself. Larger step sizes can be set for parameters with greater influence and wider search ranges, thus accelerating the search process while ensuring search accuracy.

[0102] In some embodiments of this application, step S40, which involves determining the current search step size of each parameter to be identified based on its search range, active power sensitivity, and reactive power sensitivity, is described in detail below:

[0103] S400: Substitute the search range, active sensitivity, and reactive sensitivity of the parameter to be identified into a preset step size calculation function to calculate the current search step size of the parameter to be identified.

[0104] Specifically, the step size calculation function can be shown below:

[0105] ;

[0106] In the formula, Let be the current search step size for the i-th parameter to be identified; This is the step size adjustment coefficient; This represents the upper limit of the search range for the i-th parameter to be identified; This is the lower limit of the search range for the i-th parameter to be identified; This is the weighted value of the active and reactive sensitivity of the i-th parameter to be identified; The maximum value among the weighted values ​​of active and reactive sensitivity for each parameter to be identified is 0.1 to 0.3. To prevent the coefficient from being zero, it is a small positive number, usually taken as 0.01.

[0107] As can be seen from the above technical solution, this embodiment provides an optional calculation method for determining the current search step size of the parameter to be identified. Through the above step size calculation function, a reasonable current search step size can be automatically adapted by combining the current search range width and comprehensive influence of the parameter to be identified, without the need for manual additional setting of step size parameters, thus adapting to the search needs of different iteration stages.

[0108] In some embodiments of this application, the process of perturbation verification of each parameter to be identified in step S5 is described in detail, and the steps are as follows:

[0109] S50. Perform simulation calculations based on the updated feasible values ​​of each parameter to be identified, and determine the simulation results for this round.

[0110] Specifically, the updated feasible values ​​of each parameter to be identified can be simultaneously assigned to the simulation model of the target wind turbine, and simulation calculations can be carried out to obtain the simulated active power curve and simulated reactive power curve corresponding to the parameter combination formed in this initial update.

[0111] S51. Select multiple verification parameters from the various parameters to be identified.

[0112] Specifically, N verification parameters can be selected from the various parameters to be identified.

[0113] S52. Using the feasible value of each verification parameter as the center, generate multiple discrete disturbance points corresponding to the verification parameter.

[0114] Specifically, the product of the search range corresponding to each verification parameter and the perturbation ratio coefficient is used as the perturbation value. One or more uniformly distributed discrete perturbation points are generated on both sides of the feasible value of each verification parameter, and the feasible value of the verification parameter is used as one of the discrete perturbation points.

[0115] The disturbance ratio is generally taken as 5% to 10%.

[0116] S53. Combine and match discrete disturbance points with different verification parameters to obtain multiple sets of parameter combinations.

[0117] Specifically, each discrete disturbance point of each verification parameter can be combined with each discrete disturbance point of the other verification parameters to obtain multiple sets of parameter combinations.

[0118] S54. Determine the verification simulation power for each parameter combination, and select the parameter combination with the smallest difference between the corresponding verification simulation power and the measured power as the optimal parameter combination.

[0119] Specifically, the verification reactive power and verification active power corresponding to each set of parameters can be determined;

[0120] The difference between the verification reactive power curve and the measured reactive power curve corresponding to each parameter combination can be calculated to obtain the verification reactive power difference for each parameter combination. The difference between the verification active power curve and the measured active power curve corresponding to each parameter combination can be calculated to obtain the verification active power difference for each parameter combination. The average value of the verification reactive power difference and the verification active power difference for each parameter combination can be calculated, and the parameter combination with the smallest average value can be selected as the optimal parameter combination.

[0121] S55. If the difference between the verification simulation power of the optimal parameter combination and the simulation result of this round is less than the preset comparison threshold, then it is determined that each parameter to be identified in this round has passed the verification; otherwise, it is determined that each parameter to be identified in this round has failed the verification.

[0122] Specifically, the difference between the verification reactive power curve corresponding to the optimal parameter combination and the simulated reactive power curve of the current simulation result is calculated to obtain the verification reactive power error. The difference between the verification active power curve corresponding to the optimal parameter combination and the simulated active power curve of the current simulation result is calculated to obtain the verification active power error. The average of the verification reactive power error and the verification active power error is calculated to see if it is less than a preset comparison threshold. If it is, then it is determined that each parameter to be identified in this round has passed the verification; if not, then it is determined that each parameter to be identified in this round has failed the verification.

[0123] As can be seen from the above technical solution, this embodiment provides an optional method for perturbation verification of each parameter to be identified. Through this method, it is possible to detect whether the current parameter combination is within a reasonable range of global error reduction, avoiding the problem that after sequentially correcting a single parameter, local optimization occurs but the overall combination has poor compatibility. It also allows for timely detection of parameter update offsets and secondary corrections, effectively preventing the identification result from falling into a local optimum and ensuring the stability and reliability of the overall identification result.

[0124] In some embodiments of this application, the process of selecting multiple verification parameters from various parameters to be identified is described in detail, and the steps are as follows:

[0125] S510. According to the identification priority of each parameter to be identified, select the first N parameters to be identified as each verification parameter.

[0126] Specifically, the parameters to be identified can be sorted in order of highest to lowest identification priority, and the top N parameters to be identified can be selected as the verification parameters.

[0127] Where N is less than or equal to the total value of the parameters to be identified.

[0128] As can be seen from the above technical solution, this embodiment provides an optional method for selecting multiple verification parameters from various parameters to be identified. Using this method, multiple parameters that significantly influence simulation errors can be selected for verification, reducing the computational load caused by permutations and combinations of all parameters.

[0129] In some embodiments of this application, the process of second-time updating of the feasible values ​​and search range of each parameter to be identified is described in detail, and the steps are as follows:

[0130] S60. Update the feasible values ​​of each parameter to be identified according to the optimal parameter combination.

[0131] Specifically, the parameter value corresponding to each parameter to be identified in the optimal parameter combination can be used as the new feasible value of that parameter.

[0132] S61. Based on the optimal parameter combination and the current search step size of each parameter to be identified, update the search range of the corresponding parameter to be identified.

[0133] Specifically, half of the difference between the parameter value corresponding to each parameter to be identified in the optimal parameter combination and the search step size corresponding to the parameter to be identified in this round can be calculated as the lower limit of the search range of the parameter to be identified;

[0134] The upper limit of the search range of a parameter to be identified can be calculated as half of the sum of the parameter value corresponding to the optimal parameter combination and the search step size corresponding to the parameter in this round.

[0135] As can be seen from the above technical solution, this embodiment provides an optional method for secondary updating of the feasible values ​​and search range of each parameter to be identified. Through this method, when verification determines that the overall adaptability of the parameter combination after the initial update is poor, the search range of each parameter to be identified and the initial value of the next iteration can be narrowed based on the optimal parameter combination. This allows the search in the next iteration stage to focus more on the interval where the optimal parameter is located, further improving the accuracy and efficiency of the search, avoiding invalid searches in irrelevant intervals, and shortening the overall identification process time.

[0136] Next, we will combine Figure 2 This application provides a detailed description of the wind turbine control parameter identification device. The wind turbine control parameter identification device described below can be compared with the wind turbine control parameter identification method described above.

[0137] See Figure 2 It can be observed that the wind turbine control parameter identification device may include:

[0138] The determination module 10 is used to determine the measured power of the target wind turbine and multiple parameters to be identified, and to determine the feasible value and search range of each parameter to be identified;

[0139] The calculation module 20 is used to calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0140] The sorting module 30 is used to determine the identification priority of each parameter to be identified based on the active sensitivity and reactive sensitivity of each parameter to be identified.

[0141] The simulation module 40 is used to assign parameters and simulate the target wind turbine according to the identification priority of each parameter to be identified, and in turn combine the feasible value and search range of each parameter to be identified to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0142] The verification module 50 is used to perform perturbation verification on each parameter to be identified after updating the feasible values ​​and search range of all parameters to be identified in this round. If each parameter to be identified fails the verification in this round, the update module 60 is called. If each parameter to be identified passes the verification in this round, the calculation module 20 is called until the preset stopping condition is met. The feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

[0143] The update module 60 is used to update the feasible values ​​and search range of each parameter to be identified for a second time, and call the calculation module 20 until the preset stopping condition is met.

[0144] Furthermore, the computing module 20 may include:

[0145] The first calculation unit is used to calculate the weighted value of the active sensitivity and reactive sensitivity of each parameter to be identified.

[0146] The second unit is used to sort the parameters to be identified in descending order of weighted value to determine the identification priority of each parameter.

[0147] Furthermore, the simulation module 40 may include:

[0148] The step size determination unit is used to determine the current search step size of each parameter to be identified based on the search range, active power sensitivity and reactive power sensitivity of the parameter to be identified.

[0149] The feasible solution determination unit is used to determine multiple current feasible solutions for the parameter to be identified according to the current search step size and the search range.

[0150] The simulation result determination unit is used to sequentially take each current feasible solution as the simulation value of the parameter to be identified, and determine the simulation result of each current feasible solution.

[0151] Furthermore, the step size determination unit may include:

[0152] The step size calculation function uses a sub-unit to substitute the search range, active sensitivity, and reactive sensitivity of the parameter to be identified into the preset step size calculation function to calculate the current search step size of the parameter to be identified.

[0153] The step size calculation function is as follows:

[0154] ;

[0155] In the formula, Let be the current search step size for the i-th parameter to be identified; This is the step size adjustment coefficient; This represents the upper limit of the search range for the i-th parameter to be identified; This is the lower limit of the search range for the i-th parameter to be identified; This is the weighted value of the active and reactive sensitivity of the i-th parameter to be identified; The maximum value among the weighted values ​​of active and reactive sensitivity for each parameter to be identified; To prevent zero coefficient.

[0156] Furthermore, the verification module 50 may include:

[0157] The simulation calculation unit is used to perform simulation calculations based on the updated feasible values ​​of each parameter to be identified, and to determine the simulation results for this round.

[0158] The verification parameter selection unit is used to select multiple verification parameters from each parameter to be identified;

[0159] The discrete disturbance point generation unit is used to generate multiple discrete disturbance points corresponding to each verification parameter, centered on the feasible value of each verification parameter.

[0160] The discrete disturbance point matching unit is used to combine and match discrete disturbance points with different verification parameters to obtain multiple sets of parameter combinations.

[0161] The parameter combination selection unit is used to determine the verification simulation power of each parameter combination, and select the parameter combination with the smallest difference between the corresponding verification simulation power and the measured power as the optimal parameter combination.

[0162] The verification result determination unit is used to determine that each parameter to be identified in this round has passed the verification if the difference between the verification simulation power of the optimal parameter combination and the simulation result of this round is less than a preset comparison threshold; otherwise, it determines that each parameter to be identified in this round has failed the verification.

[0163] Furthermore, the verification parameter selection unit may include:

[0164] The identification priority utilization sub-unit is used to select the top N parameters to be identified as each verification parameter according to the identification priority of each parameter to be identified.

[0165] Furthermore, the update module 60 may include:

[0166] The feasible value update unit is used to update the feasible value of each parameter to be identified according to the optimal parameter combination.

[0167] The search range update unit is used to update the search range of the corresponding parameter to be identified based on the optimal parameter combination and the current search step size of each parameter to be identified.

[0168] The wind turbine control parameter identification device provided in this application embodiment can be applied to wind turbine control parameter identification equipment, such as PC terminals, cloud platforms, servers, and server clusters. Optionally, Figure 3 The hardware structure block diagram of the wind turbine control parameter identification device is shown. (Refer to...) Figure 3 The hardware structure of the wind turbine control parameter identification device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0169] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0170] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0171] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0172] The memory stores a program, which the processor can call. The program is used for:

[0173] Determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range for each parameter to be identified;

[0174] Calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0175] Based on the active and reactive sensitivity of each parameter to be identified, the identification priority of each parameter to be identified is determined.

[0176] According to the identification priority of each parameter to be identified, the target wind turbine is assigned parameter values ​​and simulated in turn, combining the feasible value and search range of each parameter to be identified, to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0177] After updating the feasible values ​​and search range of all parameters to be identified in this round, a perturbation verification is performed on each parameter to be identified.

[0178] If any of the parameters to be identified fails the verification in this round, the feasible values ​​and search range of each parameter to be identified will be updated a second time, and the process will return to the step of calculating the active and reactive sensitivity of each parameter to be identified.

[0179] If each parameter to be identified passes the verification in this round, the process will directly return to the step of calculating the active and reactive sensitivity of each parameter to be identified until the preset stopping condition is met; the feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

[0180] Optionally, the refined and extended functions of the program can be referred to the above description.

[0181] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for:

[0182] Determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range for each parameter to be identified;

[0183] Calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity.

[0184] Based on the active and reactive sensitivity of each parameter to be identified, the identification priority of each parameter to be identified is determined.

[0185] According to the identification priority of each parameter to be identified, the target wind turbine is assigned parameter values ​​and simulated in turn, combining the feasible value and search range of each parameter to be identified, to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time.

[0186] After updating the feasible values ​​and search range of all parameters to be identified in this round, a perturbation verification is performed on each parameter to be identified.

[0187] If any of the parameters to be identified fails the verification in this round, the feasible values ​​and search range of each parameter to be identified will be updated a second time, and the process will return to the step of calculating the active and reactive sensitivity of each parameter to be identified.

[0188] If each parameter to be identified passes the verification in this round, the process will directly return to the step of calculating the active and reactive sensitivity of each parameter to be identified until the preset stopping condition is met; the feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

[0189] Optionally, the refined and extended functions of the program can be referred to the above description.

[0190] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0191] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0192] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments of this application can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying control parameters of a wind turbine generator set, characterized in that, include: Determine the measured power of the target wind turbine and multiple parameters to be identified, and determine the feasible value and search range for each parameter to be identified; Calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity. Based on the active and reactive sensitivity of each parameter to be identified, the identification priority of each parameter to be identified is determined. According to the identification priority of each parameter to be identified, the target wind turbine is assigned parameter values ​​and simulated in turn, combining the feasible value and search range of each parameter to be identified, to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time. After updating the feasible values ​​and search range of all parameters to be identified in this round, a perturbation verification is performed on each parameter to be identified. If any of the parameters to be identified fails the verification in this round, the feasible values ​​and search range of each parameter to be identified will be updated a second time, and the process will return to the step of calculating the active and reactive sensitivity of each parameter to be identified. If each parameter to be identified passes the verification in this round, the process will directly return to the step of calculating the active and reactive sensitivity of each parameter to be identified until the preset stopping condition is met; the feasible value of each parameter to be identified is the final identification value of the corresponding parameter.

2. The wind turbine control parameter identification method according to claim 1, characterized in that, The determination of the identification priority for each parameter to be identified based on its active and reactive power sensitivity includes: Calculate the weighted value of the active sensitivity and reactive sensitivity for each parameter to be identified; The parameters to be identified are sorted in descending order of weighted value to determine the identification priority of each parameter.

3. The wind turbine control parameter identification method according to claim 1, characterized in that, The process involves combining feasible values ​​and search ranges for each parameter to be identified, assigning parameter values ​​to the target wind turbine, and performing simulations to obtain simulation results, including: For each parameter to be identified, the current search step size of the parameter is determined based on the search range, active power sensitivity, and reactive power sensitivity of the parameter to be identified. Based on the current search step size and the search range, determine multiple currently feasible solutions for the parameter to be identified; Each current feasible solution is used as the simulation value of the parameter to be identified in turn, and the simulation result of each current feasible solution is determined.

4. The wind turbine control parameter identification method according to claim 3, characterized in that, The step of determining the current search step size of the parameter to be identified based on the search range, active power sensitivity, and reactive power sensitivity includes: The search range, active sensitivity, and reactive sensitivity of the parameter to be identified are substituted into the preset step size calculation function to calculate the current search step size of the parameter to be identified. The step size calculation function is as follows: ； In the formula, Let be the current search step size for the i-th parameter to be identified; This is the step size adjustment coefficient; This represents the upper limit of the search range for the i-th parameter to be identified; This is the lower limit of the search range for the i-th parameter to be identified; This is the weighted value of the active and reactive sensitivity of the i-th parameter to be identified; The maximum value among the weighted values ​​of active and reactive sensitivity for each parameter to be identified; To prevent zero coefficient.

5. The wind turbine control parameter identification method according to claim 1, characterized in that, The perturbation verification of each parameter to be identified includes: Simulation calculations are performed based on the updated feasible values ​​of each parameter to be identified to determine the results of this round of simulation. Select multiple verification parameters from each parameter to be identified; Centered on the feasible value of each verification parameter, generate multiple discrete perturbation points corresponding to the verification parameter; By combining and matching discrete disturbance points with different verification parameters, multiple sets of parameter combinations are obtained; Determine the verification simulation power for each parameter combination, and select the parameter combination with the smallest difference between the corresponding verification simulation power and the measured power as the optimal parameter combination; If the difference between the verification simulation power of the optimal parameter combination and the simulation result of this round is less than a preset comparison threshold, then it is determined that each parameter to be identified in this round has passed the verification; otherwise, it is determined that each parameter to be identified in this round has failed the verification.

6. The wind turbine control parameter identification method according to claim 5, characterized in that, The selection of multiple verification parameters from the various parameters to be identified includes: Based on the identification priority of each parameter to be identified, the first N parameters to be identified are selected as each verification parameter.

7. The wind turbine control parameter identification method according to claim 5, characterized in that, The secondary update of the feasible values ​​and search range of each parameter to be identified includes: The feasible values ​​of each parameter to be identified are updated according to the optimal parameter combination. Based on the optimal parameter combination and the current search step size of each parameter to be identified, the search range of the corresponding parameter to be identified is updated.

8. A wind turbine control parameter identification device, characterized in that, include: The determination module is used to determine the measured power of the target wind turbine and multiple parameters to be identified, and to determine the feasible value and search range of each parameter to be identified; The calculation module is used to calculate the active power sensitivity and reactive power sensitivity of each parameter to be identified. Each active power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with active power as the observation quantity, and each reactive power sensitivity is used to characterize the trajectory sensitivity of the corresponding parameter to be identified with reactive power as the observation quantity. The sorting module is used to determine the identification priority of each parameter based on its active and reactive power sensitivity. The simulation module is used to assign parameters and simulate the target wind turbine according to the identification priority of each parameter to be identified, and in turn combine the feasible value and search range of each parameter to be identified to obtain simulation results; based on the simulation results and the measured power, the feasible value and search range of each parameter to be identified are updated for the first time. The verification module is used to perform perturbation verification on each parameter to be identified after updating the feasible values ​​and search range of all parameters to be identified in this round; if each parameter to be identified fails the verification in this round, the update module is called. If each parameter to be identified passes the verification in this round, the calculation module is called until the preset stopping condition is met; the feasible value of each parameter to be identified is the final identification value of the corresponding parameter. The update module is used to update the feasible values ​​and search range of each parameter to be identified, and call the calculation module until the preset stopping condition is met.

9. A wind turbine control parameter identification device, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the wind turbine control parameter identification method as described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the wind turbine control parameter identification method as described in any one of claims 1-7.

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