Wind turbine response parameter acquisition method and device, equipment and medium

Abstract

The invention provides a wind turbine response parameter obtaining method and device, equipment and a medium. The wind turbine response parameter obtaining method comprises the steps that an initial active power output set and an initial frequency modulation response duration set of a wind turbine under the power grid frequency are obtained; generating an initial particle swarm according to the initial active power output set and the initial frequency modulation response duration set; performing iterative optimization on the initial particle swarm to obtain target particles; and according to the target active power output and the target frequency modulation response duration in the target particle, obtaining a target response parameter of the wind turbine under the power grid frequency. The target response parameter obtaining accuracy and precision are improved, the response parameter obtaining efficiency of the wind turbine is improved, the target response parameter obtaining method of the wind turbine is optimized, the response effect of the wind turbine on the power grid frequency is optimized, and the operation stability of the wind turbine and a power system is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and medium for acquiring response parameters of a wind turbine. Background Art

[0002] With the development of technology, new energy sources, mainly wind power, are becoming increasingly important for people's work and life. In the daily operation of wind turbines, when the grid frequency of the associated power grid changes, the wind turbines need to respond to the changed grid frequency, thereby achieving support for the grid frequency by the wind turbines.

[0003] In the related art, the response parameters of the wind turbine to the changed grid frequency can be determined by a pre-set wind turbine frequency response controller according to the parameters of the traditional synchronous generator, but the accuracy and precision are poor. Summary of the invention

[0004] The purpose of this application is to solve one of the technical problems in the above technology at least to a certain extent.

[0005] In a first aspect, the present application provides a method for obtaining response parameters of a wind turbine, comprising: obtaining an initial active power output set and an initial frequency modulation response time set of the wind turbine at a grid frequency; generating an initial particle group according to the initial active power output set and the initial frequency modulation response time set; iteratively optimizing the initial particle group to obtain target particles; and obtaining target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response time in the target particles.

[0006] The first aspect of the present application provides a method for obtaining a wind turbine response parameter, which also has the following technical features, including:

[0007] According to one embodiment of the present application, the method of obtaining the initial active power output set of the wind turbine at the grid frequency includes: obtaining the initial short-time frequency interval of the wind turbine at the grid frequency, and obtaining multiple initial short-time frequencies of the wind turbine from the initial short-time frequency interval; obtaining the initial active power output of the wind turbine at each initial short-time frequency among the multiple initial short-time frequencies to obtain the initial active power output set of the wind turbine.

[0008] According to one embodiment of the present application, obtaining the initial frequency modulation response duration set of a wind turbine at a grid frequency includes: obtaining an initial frequency modulation response duration interval of the grid to which the wind turbine belongs at the grid frequency; obtaining multiple initial frequency modulation response durations from the initial frequency modulation response duration interval to obtain the initial frequency modulation response duration set of the wind turbine.

[0009] According to an embodiment of the present application, generating an initial particle swarm according to the initial active power output set and the initial frequency modulation response duration set includes: for any initial active power output, obtaining the initial active power output and the corresponding initial frequency modulation response duration from the initial frequency modulation response duration set; generating corresponding initial particles according to the initial active power output and the initial frequency modulation response duration to obtain the initial particle swarm.

[0010] According to one embodiment of the present application, the iterative optimization of the initial particle swarm to obtain the target particle includes: for any initial particle, performing algorithm optimization on the initial particle through a particle swarm optimization algorithm to obtain an optimized candidate particle; identifying whether the candidate particle satisfies a preset iteration end condition; in response to identifying that the candidate particle satisfies the iteration end condition, obtaining the fitness value of the initial particle swarm, and obtaining the target particle according to the candidate particle and the fitness value.

[0011] According to one embodiment of the present application, the identifying whether the candidate particle satisfies a preset iteration end condition includes: in response to identifying that the candidate particle satisfies a preset particle constraint condition and the candidate particle is in the last iteration round, determining that the candidate particle satisfies the iteration end condition, wherein the particle constraint condition is obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs.

[0012] According to one embodiment of the present application, the method further includes: in response to the candidate response parameter not satisfying the particle constraint condition, continuing to perform particle optimization on the candidate particle to obtain a new candidate particle until the new candidate particle satisfies the particle constraint condition.

[0013] According to one embodiment of the present application, in response to identifying that the candidate particle satisfies the iteration end condition, obtaining the fitness value of the initial particle group, and obtaining the target particle according to the candidate particle and the fitness value, includes: in response to the candidate response parameter being greater than or equal to the fitness value, determining the candidate particle as the target particle; in response to the candidate response parameter being less than the fitness value, determining the candidate particle corresponding to the fitness value as the target particle.

[0014] According to an embodiment of the present application, the method further includes: in response to the candidate response parameter being greater than or equal to the fitness value and the candidate particle is not in the last iteration round, updating the fitness value according to the candidate response parameter to obtain a new fitness value.

[0015] The second aspect of the present application provides a device for acquiring wind turbine response parameters, including: a first acquisition module, used to acquire an initial active power output set and an initial frequency modulation response time set of the wind turbine at the grid frequency; a generation module, used to generate an initial particle group according to the initial active power output set and the initial frequency modulation response time set; an iteration module, used to iteratively optimize the initial particle group to obtain target particles; a second acquisition module, used to obtain the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response time in the target particles.

[0016] The second aspect of the present application provides a device for obtaining a response parameter of a wind turbine, which also has the following technical features, including:

[0017] According to one embodiment of the present application, the first acquisition module is also used to: obtain the initial short-time frequency interval of the wind turbine at the grid frequency, and obtain multiple initial short-time frequencies of the wind turbine from the initial short-time frequency interval; obtain the initial active power output of the wind turbine at each initial short-time frequency among the multiple initial short-time frequencies to obtain the initial active power output set of the wind turbine.

[0018] According to one embodiment of the present application, the first acquisition module is further used to: obtain an initial frequency modulation response duration interval of the power grid to which the wind turbine belongs at the power grid frequency; obtain multiple initial frequency modulation response durations from the initial frequency modulation response duration interval to obtain the initial frequency modulation response duration set of the wind turbine.

[0019] According to one embodiment of the present application, the generation module is also used to: for any initial active power output, obtain the initial active power output and the corresponding initial frequency modulation response duration from the initial frequency modulation response duration set; generate corresponding initial particles according to the initial active power output and the initial frequency modulation response duration to obtain the initial particle group.

[0020] According to one embodiment of the present application, the iteration module is further used to: for any initial particle, perform algorithm optimization on the initial particle through a particle swarm optimization algorithm to obtain an optimized candidate particle; identify whether the candidate particle meets a preset iteration end condition; in response to identifying that the candidate particle meets the iteration end condition, obtain the fitness value of the initial particle swarm, and obtain the target particle based on the candidate particle and the fitness value.

[0021] According to one embodiment of the present application, the iteration module is further used to: in response to identifying that the candidate particle satisfies a preset particle constraint condition and the candidate particle is in the last iteration round, determine that the candidate particle satisfies the iteration end condition, wherein the particle constraint condition is obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs.

[0022] According to one embodiment of the present application, the iteration module is further used to: in response to the candidate response parameter not satisfying the particle constraint condition, continue to perform particle optimization on the candidate particle to obtain a new candidate particle, until the new candidate particle satisfies the particle constraint condition.

[0023] According to one embodiment of the present application, the iteration module is further used to: in response to the candidate response parameter being greater than or equal to the fitness value, determine the candidate particle as the target particle; in response to the candidate response parameter being less than the fitness value, determine the candidate particle corresponding to the fitness value as the target particle.

[0024] According to one embodiment of the present application, the iteration module is further used to: in response to the candidate response parameter being greater than or equal to the fitness value and the candidate particle is not in the last iteration round, update the fitness value according to the candidate response parameter to obtain a new fitness value.

[0025] An embodiment of the third aspect of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for obtaining wind turbine response parameters provided in the first aspect of the present application.

[0026] The fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the method for obtaining wind turbine response parameters provided in the first aspect of the present application.

[0027] The fifth aspect of the present application provides a computer program product. When the instruction processor in the computer program product is executed, the method for obtaining the wind turbine response parameters provided in the first aspect of the present application is executed.

[0028] The method and device for obtaining the response parameters of a wind turbine provided in the present application obtain the initial active power output set and the initial frequency modulation response time set of the wind turbine at the grid frequency to generate an initial particle group, and iteratively optimize the initial particle group to obtain the target particles in the initial particle group, and obtain the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response time in the target particles. In the present application, the target particles are determined by iterative optimization of the initial particle group, so as to obtain the target response parameters of the wind turbine based on the current grid frequency, and the optimal solution in the initial particle group is used as the target response parameters of the wind turbine, thereby improving the accuracy and precision of obtaining the target response parameters. Compared with the related art of obtaining the response parameters of the wind turbine by combining the parameters of the traditional synchronous generator, the efficiency of obtaining the response parameters of the wind turbine is improved, the method for obtaining the target response parameters of the wind turbine is optimized, the response effect of the wind turbine to the grid frequency is optimized, and the stability of the operation of the wind turbine and the power system is improved.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A schematic diagram of a method for obtaining wind turbine response parameters according to an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a method for obtaining wind turbine response parameters according to another embodiment of the present application;

[0033] Figure 3 A schematic flow chart of a method for obtaining wind turbine response parameters according to another embodiment of the present application;

[0034] Figure 4 This is a schematic diagram of the structure of a device for acquiring wind turbine response parameters according to an embodiment of the present application;

[0035] Figure 5 It is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0037] The following describes the method, device, equipment and medium for obtaining wind turbine response parameters according to the embodiments of the present application with reference to the accompanying drawings.

[0038] Figure 1 FIG. 1 is a flow chart of a method for obtaining wind turbine response parameters according to an embodiment of the present application. Figure 1 As shown, the method includes:

[0039] S101, obtaining an initial power output set and an initial frequency modulation response duration set of a wind turbine at a grid frequency.

[0040] In the embodiment of the present application, the frequency of the wind turbine needs to match the grid frequency of the power grid to which it is associated. In this scenario, the response parameters of the wind turbine at the grid frequency can be obtained through the active power output and frequency modulation response duration of the wind turbine.

[0041] Optionally, multiple active power outputs achievable by the wind turbine at the grid frequency may be obtained and marked as multiple initial active power outputs, thereby obtaining an initial active power output set consisting of the multiple initial active power outputs.

[0042] Also, for any initial power output, the duration for which the wind turbine can continue to respond under the initial power output is obtained, and the duration is determined as the initial frequency modulation response duration of the wind turbine, wherein the initial frequency modulation response duration of the wind turbine under the initial power output can be one or more, and is not specifically limited here.

[0043] Furthermore, according to at least one initial frequency modulation response duration of the wind turbine under each initial power output, an initial frequency modulation response duration set of the wind turbine consisting of multiple initial frequency modulation response durations is obtained.

[0044] S102, generating an initial particle swarm according to an initial power output set and an initial frequency modulation response duration set.

[0045] In an embodiment of the present application, each initial power output in the initial power output set and each initial frequency modulation response duration in the initial frequency modulation response duration set can be combined to obtain a parameter combination consisting of any initial power output and any initial frequency modulation response duration, and then obtain multiple parameter combinations consisting of each initial power output and each initial frequency modulation response duration.

[0046] In this scenario, based on the particle generation algorithm in the relevant technology, the initial power output and the candidate response frequency modulation duration included in any parameter combination can be algorithmically processed, and then the corresponding particles are generated according to the results of the algorithm processing as the initial particles generated by the initial power output and the candidate response frequency modulation duration.

[0047] Furthermore, according to the initial particles of each parameter component in the plurality of parameter combinations, an initial particle group generated based on the initial power output set and the initial frequency modulation response duration set is obtained.

[0048] S103, iteratively optimizing the initial particle swarm to obtain target particles.

[0049] In the embodiment of the present disclosure, each initial particle in the initial particle group includes a parameter combination consisting of an initial power output and an initial frequency modulation response duration. In this scenario, each initial particle in the initial particle group can be iteratively optimized, and then particles that meet preset iterative optimization conditions are obtained from the initial particle group as target particles based on the results of the iterative optimization.

[0050] It can be understood that, with respect to response effect 1 in which the wind turbine responds to the current grid frequency based on the initial power output and the initial frequency modulation response duration carried by the target particles, and response effect 2 in which the wind turbine responds to the current grid frequency based on the initial power output and the initial frequency modulation response duration carried by the remaining initial particles in the initial particle group except the target particles, response effect 1 is better than each response effect 2.

[0051] Optionally, the initial particle swarm may be iteratively optimized based on a particle swarm optimization (PSO) algorithm in the related art until a condition for ending the iteration is met, and then the target particles are determined from the initial particle swarm.

[0052] Optionally, the initial particle swarm may be iteratively optimized based on other types of iterative optimization algorithms that may be used by the initial particle swarm, so as to obtain target particles in the initial particle swarm.

[0053] S104, obtaining target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response duration in the target particles.

[0054] In the embodiment of the present application, the initial active power output carried by the target particle can be marked as the target active power output, and the initial frequency modulation response duration carried by the target particle can be marked as the target frequency modulation response duration.

[0055] In this scenario, the target active power output and target frequency regulation response time can be algorithmically processed based on the preset wind turbine response parameter acquisition algorithm, and then the response parameters of the wind turbine at the current grid frequency are obtained according to the results of the algorithm processing, and marked as the target response parameters of the wind turbine.

[0056] It should be noted that the target response parameter can be understood as the optimal response solution that the wind turbine can achieve under the current grid frequency.

[0057] Among them, the target active power output and the target frequency regulation response time can be multiplied together, and the product of the two can be used as the target response parameter of the wind turbine under the current grid frequency.

[0058] The method for obtaining the response parameters of a wind turbine proposed in the present application obtains the initial active power output set and the initial frequency modulation response time set of the wind turbine at the grid frequency to generate an initial particle group, and iteratively optimizes the initial particle group to obtain the target particles in the initial particle group, and obtains the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response time in the target particles. In the present application, the target particles are determined by iterative optimization of the initial particle group, so as to obtain the target response parameters of the wind turbine based on the current grid frequency, and the optimal solution in the initial particle group is used as the target response parameters of the wind turbine, thereby improving the accuracy and precision of obtaining the target response parameters. Compared with the related art of obtaining the response parameters of the wind turbine by combining the parameters of the traditional synchronous generator, the efficiency of obtaining the response parameters of the wind turbine is improved, the method for obtaining the target response parameters of the wind turbine is optimized, the response effect of the wind turbine to the grid frequency is optimized, and the stability of the operation of the wind turbine and the power system is improved.

[0059] In the above embodiment, the acquisition of target particles can be combined with Figure 2 Further understanding, Figure 2 FIG. 1 is a flow chart of a method for obtaining wind turbine response parameters according to another embodiment of the present application. Figure 2 As shown, the method includes:

[0060] S201, obtaining an initial active power output set and an initial frequency modulation response time set of the wind turbine at the grid frequency.

[0061] The acquisition of the initial active power output set of the wind turbine at the grid frequency can be understood in combination with the following contents:

[0062] Optionally, an initial short-time frequency interval of the wind turbine at the grid frequency is obtained, and multiple initial short-time frequencies of the wind turbine are obtained from the initial short-time frequency interval.

[0063] In the embodiment of the present application, the interval obtained by constructing the short-term frequency range achievable by the wind turbine under the current grid frequency can be determined as the initial short-term frequency interval of the wind turbine.

[0064] In this scenario, multiple elements can be screened out from the initial short-time frequency interval of the wind turbine as multiple initial short-time frequencies of the wind turbine.

[0065] Among them, multiple initial short-time frequencies can be screened out from the initial short-time frequency interval based on a preset screening strategy, or multiple initial short-time frequencies of the wind turbine can be randomly obtained from the initial short-time frequency interval, which is not specifically limited here.

[0066] Optionally, an initial active power output of the wind turbine at each initial short-time frequency among a plurality of initial short-time frequencies is acquired to obtain an initial active power output set of the wind turbine.

[0067] In an embodiment of the present application, algorithm processing can be performed on multiple initial short-time frequencies respectively according to the active power output algorithm in the relevant technology, and then the active power output of each of the multiple initial short-time frequencies can be obtained according to the results of the algorithm processing, and they can be marked as the initial active power output of each of the multiple initial short-time frequencies.

[0068] Furthermore, each initial active power output is used to form an initial active power output set of the wind turbine.

[0069] The acquisition of the initial frequency modulation response time set of the wind turbine under the grid frequency can be understood in combination with the following contents:

[0070] Optionally, an initial frequency modulation response duration interval of the power grid to which the wind turbine belongs at the power grid frequency is obtained, and multiple initial frequency modulation response durations are obtained from the initial frequency modulation response duration interval to obtain an initial frequency modulation response duration set of the wind turbine.

[0071] In an embodiment of the present application, the power grid to which the wind turbine belongs has an allowable wind power support frequency regulation response time range at the current power grid frequency. In this scenario, the interval composed of this range can be determined as the initial frequency regulation response time interval of the wind turbine.

[0072] In this scenario, a set number of elements in the initial frequency modulation response duration interval can be used as multiple initial frequency modulation response durations, and a set of the multiple initial frequency modulation response durations can be determined as the initial frequency modulation response duration set of the wind turbine.

[0073] Among them, multiple initial FM response durations can be randomly selected from the initial FM response duration interval, or the initial FM response duration interval can be selected based on a preset interval to obtain multiple initial FM response durations, which is not specifically limited here.

[0074] S202: Generate an initial particle swarm according to an initial active power output set and an initial frequency modulation response duration set.

[0075] Optionally, for any initial active power output, the initial active power output and the corresponding initial frequency modulation response duration are obtained from the initial frequency modulation response duration set, and corresponding initial particles are generated according to the initial active power output and the initial frequency modulation response duration to obtain an initial particle group.

[0076] In the embodiment of the present application, for any initial active power output, a parameter may be randomly selected from the initial frequency modulation response duration set as the initial frequency modulation response duration corresponding to the initial active power output.

[0077] Among them, the initial active power output and its corresponding initial frequency modulation response duration are processed by the particle construction algorithm in the relevant technology, so as to obtain the particles generated by the initial active power output and its corresponding initial frequency modulation response duration according to the result of the algorithm processing, and mark the particles as initial particles.

[0078] It can be understood that each initial active power output and each initial frequency modulation response duration are randomly combined to obtain an initial particle based on a parameter combination composed of any initial active power output and any initial frequency modulation response duration, thereby obtaining an initial particle group composed of multiple initial particles obtained based on the initial active power output set and the initial frequency modulation response duration set.

[0079] S203, iteratively optimizing the initial particle swarm to obtain target particles.

[0080] Optionally, for any initial particle, the initial particle is optimized by a particle swarm optimization algorithm to obtain an optimized candidate particle.

[0081] In an embodiment of the present application, before performing algorithm optimization on the initial particles through the particle swarm optimization algorithm, it is necessary to set the optimization algorithm parameters required by the particle swarm optimization algorithm, such as the population size of the initial particle swarm, the maximum speed of each initial particle in the initial particle swarm, the learning factor, the number of iterations of the initial particle swarm and the inertia weight, and perform algorithm optimization on the initial particles according to the set optimization algorithm parameters to obtain optimized initial particles, and mark them as candidate particles obtained after the optimization of the initial particles.

[0082] As an example, the algorithm optimization of the initial particles to obtain candidate particles can be understood in combination with the following formula:

[0083] v n,i =w(t)v i +c1r1(p b,i -x i )+c2r2(g b,i -x i )

[0084] xn,i =x i +v n,i

[0085] In the above formula, v n,i and x n,i is the velocity and position of the candidate particle obtained after iterative update of the i-th initial particle, v i and x i is the current speed and position of the i-th initial particle, is the dynamic inertia weight value updated with the current iteration cycle number t, where w min 、w max max、T max They are the minimum and maximum values ​​of the inertia weight and the total number of iterative optimization in the particle swarm algorithm, c1 and c2 are learning factors, r1 and r2 are random numbers generated in the range of 0 to 1, which are used to increase the randomness of the particle speed during optimization.

[0086] Optionally, whether the candidate particle satisfies a preset iteration end condition is identified, wherein, in response to identifying that the candidate particle satisfies a preset particle constraint condition and the candidate particle is in the last iteration round, it is determined that the candidate particle satisfies the iteration end condition, wherein the particle constraint condition is obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs.

[0087] In an embodiment of the present application, the initial particle swarm has corresponding constraints, which can be obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs, and can be marked as particle constraints of the initial particle swarm.

[0088] It can be understood that the parameters carried in the candidate particles obtained after optimization may be parameters that the wind turbine and its power grid cannot achieve. In this scenario, through the particle constraint conditions, it is possible to determine whether the parameters in the candidate particles are parameters that the wind turbine and its power grid can support.

[0089] Optionally, the iteration end condition can be set according to the particle constraint condition and the preset number of iterations of the initial particle group. In this scenario, the candidate particle obtained in the current iteration round can be compared with the particle constraint condition and the number of iterations in the iteration end condition. When the candidate particle satisfies the particle constraint condition and the candidate particle is in the last iteration round, it can be determined that the candidate particle satisfies the iteration end condition.

[0090] As an example, the particle constraint condition can be expressed as follows:

[0091]

[0092] ω g(t)≥ω min

[0093] P g (t) / ω g (t)≤τ max

[0094] t≤ΔT FR

[0095] In the above formula, ω min is the minimum fan speed; τ max is the maximum torque of the fan, P is the active power, H is the inertia constant of the fan, t is the time, T is the active power control time constant of the fan, dt is the differential with respect to time, ωg is the fan speed, and τ is the fan torque.

[0096] It should be noted that the candidate particles obtained in the current round may not meet the iteration end condition. In response to the candidate response parameters not meeting the particle constraint conditions, the candidate particles are continuously optimized to obtain new candidate particles until the new candidate particles meet the particle constraint conditions.

[0097] In an embodiment of the present application, when a candidate particle does not satisfy the particle constraint conditions, it can be understood that the parameters carried in the candidate particle may be parameters that cannot be achieved by the wind turbine and its power grid. In this scenario, the algorithm optimization can be continued for the candidate particle, and the candidate particle obtained after the algorithm optimization can be marked as a new candidate particle.

[0098] The process of performing algorithm optimization on candidate particles can be understood in conjunction with the above formula for performing algorithm optimization on initial particles, and will not be described in detail here.

[0099] Furthermore, the new candidate particles after the algorithm optimization are continuously compared with the particle constraint conditions. When the new candidate particles still do not satisfy the particle constraint conditions, the new candidate particles can be returned to continue iterative optimization.

[0100] Accordingly, when the new candidate particle satisfies the particle constraint condition, the algorithm optimization of the candidate particle can be terminated, and the new candidate particle can continue to be judged and identified as to whether it satisfies the iteration end condition.

[0101] Optionally, in response to identifying that a candidate particle meets an iteration end condition, a fitness value of the initial particle group is obtained, and a target particle is obtained according to the candidate particle and the fitness value.

[0102] In the embodiment of the present application, the candidate particle carries the active power output and frequency modulation response duration of the wind turbine, and the product value of the two can be determined as the candidate response parameter corresponding to the candidate particle.

[0103] Optionally, there is a corresponding algorithm for the fitness value, and the fitness value of the initial particle swarm can be calculated based on the algorithm, wherein the algorithm can be understood in combination with the following formula:

[0104] fitness=max E FR =ΔP FR (t) ΔT FR

[0105] In the formula, E FR is the candidate response parameter of the candidate particle, ΔT FR is the frequency modulation response duration of the wind turbine carried by the candidate particle, ΔP FR is the wind turbine carried by the candidate particle at ΔT FR Active power output under time duration.

[0106] It can be seen from the above formula that for the candidate particles obtained in each of the N iteration rounds that have been iterated, the fitness value of the initial particle group is the maximum value of the candidate response parameters of each of the N candidate particles.

[0107] Optionally, for any candidate particle, it is possible that the candidate response parameter of the candidate particle is greater than or equal to the fitness value, but the iteration round for obtaining the candidate particle is not the last iteration round. It can be understood that, in response to the candidate response parameter being greater than or equal to the fitness value and the candidate particle is not in the last iteration round, the fitness value is updated according to the candidate response parameter to obtain a new fitness value.

[0108] As an example, for iteration rounds N and iteration rounds N-1, the fitness value of the initial particle swarm in iteration round N-1 is set to fitness value 1. Then, when the candidate response parameter of the candidate particle in iteration round N is greater than fitness value 1, the candidate response parameter can be determined as a new fitness value 2, and fitness value 2 is the new fitness value of the initial particle swarm in iteration round N+1.

[0109] Optionally, in response to the candidate response parameter being greater than or equal to the fitness value, the candidate particle is determined to be the target particle.

[0110] It can be understood that if the candidate response parameter of a candidate particle is greater than or equal to the fitness value of the initial particle group, the candidate response parameter of the candidate particle is the maximum value among the candidate response parameters of the candidate particles of all preset iteration rounds, and the maximum value is the optimal solution that the wind turbine can achieve under the current grid frequency.

[0111] In this scenario, the candidate particle can be determined as the target particle in the initial particle group.

[0112] Optionally, in response to the candidate response parameter being less than the fitness value, the candidate particle corresponding to the fitness value is determined as the target particle.

[0113] It can be understood that if the candidate response parameter of the candidate particle is less than the fitness value of the initial particle group, the fitness value is the maximum value among the candidate response parameters of the candidate particles of all preset iteration rounds, and the maximum value is determined to be the optimal solution that the wind turbine can achieve under the current grid frequency.

[0114] In this scenario, the candidate particle corresponding to the fitness value can be determined as the target particle in the initial particle group.

[0115] The method for obtaining the response parameters of a wind turbine proposed in the present application determines the target particles through iterative optimization of an initial particle group, thereby obtaining the target response parameters of the wind turbine based on the current grid frequency, and uses the optimal solution in the initial particle group as the target response parameters of the wind turbine, thereby improving the accuracy and precision of obtaining the target response parameters. Compared with the related art of obtaining the response parameters of the wind turbine by combining the parameters of a traditional synchronous generator, the efficiency of obtaining the response parameters of the wind turbine is improved, the method for obtaining the target response parameters of the wind turbine is optimized, the response effect of the wind turbine to the grid frequency is optimized, and the stability of the operation of the wind turbine and the power system is improved.

[0116] To better understand the above embodiments, Figure 3 , Figure 3 FIG. 1 is a flow chart of a method for obtaining wind turbine response parameters according to another embodiment of the present application. Figure 3 As shown, the method includes:

[0117] like Figure 3 As shown, each initial particle in the initial particle swarm is initialized, and the particle swarm iterative optimization algorithm parameters of the initial particle swarm are set, wherein the particle swarm iterative optimization algorithm parameters include the population size, maximum speed, learning factor, number of iterations and inertia weight of the initial particle swarm.

[0118] like Figure 3 As shown, the fitness value of each initial particle is calculated to find the individual extreme value and the global extreme value, thereby realizing the algorithm optimization of the initial particles.

[0119] Furthermore, the algorithm is optimized for the initial particles to obtain candidate particles, and it is identified whether the candidate particles meet the preset particle constraints, such as Figure 3 As shown, when a candidate particle does not satisfy the particle constraint condition, the algorithm optimization is continued for the candidate particle to obtain a new candidate particle until the new candidate particle satisfies the particle constraint condition.

[0120] like Figure 3As shown, when the candidate particle meets the particle constraint condition, it is identified whether the candidate particle is in the last iteration round. When it is identified that the candidate particle is not in the last iteration round, it returns to continue the next round of particle swarm iterative optimization.

[0121] Correspondingly, if the candidate particle is in the last iteration round, the iterative optimization of the particle swarm can be ended and the optimal solution is output, where the optimal solution is the target response parameter corresponding to the target particle.

[0122] The method for obtaining the response parameters of a wind turbine proposed in the present application determines the target particles through iterative optimization of an initial particle group, thereby obtaining the target response parameters of the wind turbine based on the current grid frequency, and uses the optimal solution in the initial particle group as the target response parameters of the wind turbine, thereby improving the accuracy and precision of obtaining the target response parameters. Compared with the related art of obtaining the response parameters of the wind turbine by combining the parameters of a traditional synchronous generator, the efficiency of obtaining the response parameters of the wind turbine is improved, the method for obtaining the target response parameters of the wind turbine is optimized, the response effect of the wind turbine to the grid frequency is optimized, and the stability of the operation of the wind turbine and the power system is improved.

[0123] Corresponding to the methods for obtaining wind turbine response parameters proposed in the above-mentioned embodiments, an embodiment of the present application further proposes a device for obtaining wind turbine response parameters. Since the device for obtaining wind turbine response parameters proposed in the embodiment of the present application corresponds to the methods for obtaining wind turbine response parameters proposed in the above-mentioned embodiments, the implementation method of the above-mentioned method for obtaining wind turbine response parameters is also applicable to the device for obtaining wind turbine response parameters proposed in the embodiment of the present application, and will not be described in detail in the following embodiments.

[0124] Figure 4 FIG. 1 is a schematic diagram of a structure of a device for acquiring wind turbine response parameters according to an embodiment of the present application. Figure 4 As shown, the wind turbine response parameter acquisition device 400 includes a first acquisition module 41, a generation module 42, an iteration module 43 and a second acquisition module 44, wherein:

[0125] A first acquisition module 41 is used to acquire an initial active power output set and an initial frequency modulation response duration set of the wind turbine at the grid frequency;

[0126] A generating module 42, configured to generate an initial particle swarm according to an initial active power output set and an initial frequency modulation response duration set;

[0127] Iteration module 43, used for iteratively optimizing the initial particle group to obtain target particles;

[0128] The second acquisition module 44 is used to obtain the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response duration in the target particles.

[0129] In an embodiment of the present application, the first acquisition module 41 is also used to: obtain the initial short-time frequency interval of the wind turbine at the grid frequency, and obtain multiple initial short-time frequencies of the wind turbine from the initial short-time frequency interval; obtain the initial active power output of the wind turbine at each initial short-time frequency in the multiple initial short-time frequencies to obtain the initial active power output set of the wind turbine.

[0130] In the embodiment of the present application, the first acquisition module 41 is also used to: obtain the initial frequency modulation response time interval of the power grid to which the wind turbine belongs at the power grid frequency; obtain multiple initial frequency modulation response time intervals to obtain an initial frequency modulation response time set of the wind turbine.

[0131] In the embodiment of the present application, the generation module 42 is also used to: for any initial active power output, obtain the initial active power output and the corresponding initial frequency modulation response duration from the initial frequency modulation response duration set; generate corresponding initial particles according to the initial active power output and the initial frequency modulation response duration to obtain an initial particle group.

[0132] In the embodiment of the present application, the iteration module 43 is also used to: for any initial particle, optimize the initial particle through the particle swarm optimization algorithm to obtain an optimized candidate particle; identify whether the candidate particle meets the preset iteration end condition; in response to identifying that the candidate particle meets the iteration end condition, obtain the fitness value of the initial particle swarm, and obtain the target particle according to the candidate particle and the fitness value.

[0133] In an embodiment of the present application, the iteration module 43 is also used to: in response to identifying that a candidate particle satisfies a preset particle constraint condition and the candidate particle is in the last iteration round, determine that the candidate particle satisfies an iteration end condition, wherein the particle constraint condition is obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs.

[0134] In the embodiment of the present application, the iteration module 43 is further used to: in response to the candidate response parameter not satisfying the particle constraint condition, continue to perform particle optimization on the candidate particle to obtain a new candidate particle, until the new candidate particle satisfies the particle constraint condition.

[0135] In the embodiment of the present application, the iteration module 43 is further used to: in response to the candidate response parameter being greater than or equal to the fitness value, determine the candidate particle as the target particle; in response to the candidate response parameter being less than the fitness value, determine the candidate particle corresponding to the fitness value as the target particle.

[0136] In the embodiment of the present application, the iteration module 43 is further used to: in response to the candidate response parameter being greater than or equal to the fitness value and the candidate particle is not in the last iteration round, update the fitness value according to the candidate response parameter to obtain a new fitness value.

[0137] The device for acquiring the response parameters of a wind turbine proposed in the present application acquires the initial active power output set and the initial frequency modulation response time set of the wind turbine at the grid frequency to generate an initial particle group, and iteratively optimizes the initial particle group to obtain the target particles in the initial particle group, and obtains the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response time in the target particles. In the present application, the target particles are determined by iterative optimization of the initial particle group, so as to obtain the target response parameters of the wind turbine based on the current grid frequency, and the optimal solution in the initial particle group is used as the target response parameters of the wind turbine, thereby improving the accuracy and precision of obtaining the target response parameters. Compared with the related art of obtaining the response parameters of the wind turbine by combining the parameters of the traditional synchronous generator, the efficiency of obtaining the response parameters of the wind turbine is improved, the method for obtaining the target response parameters of the wind turbine is optimized, the response effect of the wind turbine to the grid frequency is optimized, and the stability of the operation of the wind turbine and the power system is improved.

[0138] To achieve the above embodiments, the present application also provides an electronic device, a computer-readable storage medium and a computer program product.

[0139] Figure 5 A block diagram of an electronic device according to an embodiment of the present application, such as Figure 5 As shown, the device 500 includes a memory 51, a processor 52, and a computer program stored in the memory 51 and executable on the processor 52. When the processor 52 executes the program instructions, the execution is realized. Figures 1 to 3 A method for obtaining wind turbine response parameters of an embodiment.

[0140] In order to implement the above embodiments, the present application also provides a non-transitory computer-readable storage medium storing computer instructions, the computer instructions are used to enable the computer to execute Figures 1 to 3 A method for obtaining wind turbine response parameters of an embodiment.

[0141] In order to implement the above embodiment, the present application also provides a computer program product. When the instruction processor in the computer program product executes, Figures 1 to 3 A method for obtaining wind turbine response parameters of an embodiment.

[0142] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0143] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for obtaining wind turbine response parameters, characterized in that: The method comprises: Obtaining an initial active power output set and an initial frequency modulation response time set of the wind turbine at the grid frequency; Generate an initial particle swarm according to the initial active power output set and the initial frequency modulation response duration set; Iteratively optimizing the initial particle group to obtain target particles; According to the target active power output and the target frequency modulation response duration in the target particles, the target response parameters of the wind turbine at the grid frequency are obtained.

2. The method according to claim 1, characterized in that The obtaining of the initial active power output set of the wind turbine at the grid frequency includes: Acquire an initial short-time frequency interval of the wind turbine at the power grid frequency, and acquire multiple initial short-time frequencies of the wind turbine from the initial short-time frequency interval; The initial active power output of the wind turbine at each initial short-time frequency among the multiple initial short-time frequencies is acquired to obtain the initial active power output set of the wind turbine.

3. The method according to claim 2, characterized in that The step of obtaining the initial frequency modulation response duration set of the wind turbine at the grid frequency includes: Obtaining an initial frequency modulation response duration interval of the power grid to which the wind turbine belongs at the power grid frequency; A plurality of initial frequency modulation response durations are acquired from the initial frequency modulation response duration interval to obtain the initial frequency modulation response duration set of the wind turbine.

4. The method according to claim 1, characterized in that: The generating an initial particle swarm according to the initial active power output set and the initial frequency modulation response duration set includes: For any initial active power output, obtaining the initial active power output and the corresponding initial frequency modulation response duration from the initial frequency modulation response duration set; According to the initial active power output and the initial frequency modulation response duration, corresponding initial particles are generated to obtain the initial particle group.

5. The method according to claim 1, characterized in that The iterative optimization of the initial particle group to obtain target particles includes: For any initial particle, the initial particle is optimized by a particle swarm optimization algorithm to obtain an optimized candidate particle; Identify whether the candidate particle meets a preset iteration end condition; In response to identifying that the candidate particle satisfies the iteration end condition, the fitness value of the initial particle group is acquired, and the target particle is obtained according to the candidate particle and the fitness value.

6. The method according to claim 5, characterized in that The step of identifying whether the candidate particle satisfies a preset iteration end condition comprises: In response to identifying that the candidate particle satisfies a preset particle constraint condition and the candidate particle is in the last iteration round, determining that the candidate particle satisfies the iteration end condition, wherein the particle constraint condition is obtained based on the mechanical parameters of the wind turbine and the electromagnetic parameters of the power grid to which the wind turbine belongs.

7. The method according to claim 6, characterized in that The method further comprises: In response to the candidate response parameter not satisfying the particle constraint condition, the candidate particle is continuously optimized to obtain a new candidate particle until the new candidate particle satisfies the particle constraint condition.

8. The method according to claim 5, characterized in that In response to identifying that the candidate particle satisfies the iteration end condition, obtaining the fitness value of the initial particle group, and obtaining the target particle according to the candidate particle and the fitness value, comprising: In response to the candidate response parameter being greater than or equal to the fitness value, determining the candidate particle as the target particle; In response to the candidate response parameter being less than the fitness value, the candidate particle corresponding to the fitness value is determined as the target particle.

9. The method according to claim 8, characterized in that The method further comprises: In response to the candidate response parameter being greater than or equal to the fitness value, and the candidate particle is not in the last iteration round, updating the fitness value according to the candidate response parameter to obtain a new fitness value.

10. A device for obtaining wind turbine response parameters, characterized in that: The device comprises: The first acquisition module is used to acquire an initial active power output set and an initial frequency modulation response time set of the wind turbine at the grid frequency; A generating module, used for generating an initial particle swarm according to the initial active power output set and the initial frequency modulation response duration set; An iterative module, used for iteratively optimizing the initial particle swarm to obtain target particles; The second acquisition module is used to obtain the target response parameters of the wind turbine at the grid frequency according to the target active power output and the target frequency modulation response duration in the target particles.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 9.

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