Wind farm power generation estimation method and device, storage medium and computer device

Abstract

The wind farm power generation estimation method, device, storage medium, and computer equipment provided in this application, by introducing inflow wind speed sequence time compression and wake calculation, can efficiently restore the real-time wind speed and direction data of each wind turbine. Combined with wind direction sector division and single-unit power generation model simulation, the single-unit power generation model can realistically reflect the dynamic response characteristics of the wind turbine main control system and converter control strategy. It incorporates continuous wind conditions and wind turbine dynamic control characteristics into the evaluation, effectively making up for the shortcomings of traditional methods that are based only on wind speed probability distribution. It considers the impact of wind direction changes on wake loss and unit layout, and captures the seasonal and intraday variation patterns of wind speed and direction, thereby improving the accuracy of wind farm power generation assessment. At the same time, the simulation calculation is greatly reduced through time compression, achieving efficient and accurate wind farm power generation assessment.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, storage medium and computer equipment for estimating the power generation of a wind farm. Background Technology

[0002] Driven by the global energy transition and dual-carbon goals, wind power, as a technologically mature renewable energy source with the potential for large-scale development, is ushering in a period of rapid development. Wind farm power generation assessment is a fundamental task for wind farm planning and operation control. Its results are directly related to project investment returns, turbine selection, micro-site selection, and operation and maintenance strategy formulation, and are a key link in the whole life cycle management of wind farms.

[0003] Traditional methods are mostly based on the simple product of wind speed probability distribution and wind turbine power curve, ignoring the key impact of wind direction changes on wake loss and turbine layout. Although modern wind resource assessment technology has introduced tools such as wind rose diagram wake models, it still cannot effectively consider the seasonal and intraday variation patterns of wind direction and wind speed, resulting in the difficulty in achieving the expected accuracy of power generation assessment. Summary of the Invention

[0004] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency that, although existing technologies have introduced tools such as wind rose diagrams and wake models, they still cannot effectively consider the seasonal and intraday variations of wind direction and speed, resulting in the difficulty in achieving the expected accuracy in power generation assessment.

[0005] In a first aspect, this application provides a method for estimating the power generation of a wind farm, the method comprising: Obtain the inflow wind speed sequence of the wind farm over a period of time, and compress the inflow wind speed sequence over time to obtain the target wind speed sequence; The target wind speed sequence is input into a preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm. Based on the preset wind direction sector division method, the wind speed and wind direction data of each wind turbine are imported into the preset single-unit power generation model, and the power curve of each wind turbine changing over time is simulated. By integrating and summing the power curves of each wind turbine, the power generation of the wind farm under the current compressed scale is obtained. Then, the power generation of the wind farm is restored to the compressed scale to obtain the actual power generation of the wind farm.

[0006] In one embodiment, the step of time-compressing the inflow wind speed sequence to obtain the target wind speed sequence includes: Get the current compression scale; According to the current compression scale, the time values ​​corresponding to the time dimension in the inflow wind speed sequence are compressed to obtain the target wind speed sequence.

[0007] In one embodiment, the step of inputting the target wind speed sequence into a preset wake calculation module to obtain wind speed and direction data for each wind turbine in the wind farm includes: Obtain the spatial location, rotor radius, and thrust coefficient of each wind turbine in the wind farm; Based on the target wind speed sequence and the spatial position, rotor radius and thrust coefficient of each wind turbine, the actual wind speed and actual wind direction of each wind turbine after being affected by the wake of other wind turbines are calculated using a preset wake model, thus forming the wind speed and wind direction data of each wind turbine.

[0008] In one embodiment, the step of importing the wind speed and direction data of each wind turbine into a preset single-unit power generation model according to a preset wind direction sector division method, and simulating the wind turbine power curve of each wind turbine over time, includes: Based on the wind speed and direction data of each wind turbine, determine the wind speed sequence and wind direction sequence for each wind turbine; Based on the preset wind direction sector division method, the wind direction sequence of each wind turbine is mapped to the corresponding wind direction sector to obtain wind direction sector data; The wind speed sequence and corresponding wind direction sector data of each wind turbine are input into a preset single-unit power generation model to calculate the power generation of the wind turbine at each moment, so as to generate the power curve of each wind turbine over time.

[0009] In one embodiment, the single-unit power generation model includes:

[0010] In the formula, The wind speed in the wind farm is azimuth angle is The output power of the point, Indicates the number of azimuth sectors. This indicates the sector index to which the currently calculated wind speed and direction data belongs. This represents the first discrete wind speed setting. Wind speed adjustment, This represents the first discrete wind speed setting. Wind speed adjustment, This indicates the sector index for wind direction.

[0011] In one embodiment, the step of integrating and summing the power curves of each wind turbine to obtain the wind farm's power generation at the current compression scale includes: Integrate the power curve of each wind turbine to obtain the power generation of each wind turbine under the current compression scale. The power generation of each wind turbine under the current compression scale is summed to obtain the power generation of the wind farm under the current compression scale.

[0012] In one embodiment, the step of compressing and scaling the wind farm's power generation to obtain the actual power generation of the wind farm includes: Obtain the compression ratio coefficient corresponding to the current compression scale; Multiply the wind farm's power generation under the current compression scale by the compression ratio coefficient to obtain the wind farm's actual power generation.

[0013] Secondly, this application provides a wind farm power generation estimation device, the device comprising: The wind speed acquisition module is used to acquire the inflow wind speed sequence of the wind farm over a period of time, and to compress the inflow wind speed sequence over time to obtain the target wind speed sequence. The wake calculation module is used to input the target wind speed sequence into the preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm. The power simulation module is used to import the wind speed and direction data of each wind turbine into the preset single-unit power generation model according to the preset wind direction sector division method, and simulate the wind turbine power curve of each wind turbine over time. The power generation estimation module is used to integrate and sum the power curves of each wind turbine to obtain the power generation of the wind farm under the current compressed scale, and then restore the power generation of the wind farm to the compressed scale to obtain the actual power generation of the wind farm.

[0014] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the wind farm power generation estimation method as described in any of the above embodiments.

[0015] Fourthly, this application provides a computer device, including: one or more processors, and a memory; The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, they perform the steps of the wind farm power generation estimation method as described in any of the above embodiments.

[0016] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: The wind farm power generation estimation method, device, storage medium, and computer equipment provided in this application, by introducing inflow wind speed sequence time compression and wake calculation, can efficiently restore the real-time wind speed and direction data of each wind turbine. Combined with wind direction sector division and single-unit power generation model simulation, the single-unit power generation model can realistically reflect the dynamic response characteristics of the wind turbine main control system and converter control strategy. It incorporates continuous wind conditions and wind turbine dynamic control characteristics into the evaluation, effectively making up for the shortcomings of traditional methods that are based only on wind speed probability distribution. It considers the impact of wind direction changes on wake loss and unit layout, and captures the seasonal and intraday variation patterns of wind speed and direction, thereby improving the accuracy of wind farm power generation assessment. At the same time, the simulation calculation is greatly reduced through time compression, achieving efficient and accurate wind farm power generation assessment. Attached Figure Description

[0017] 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a wind farm power generation estimation method provided in this application embodiment; Figure 2 An example diagram illustrating a wind farm power generation estimation method provided in this application embodiment; Figure 3 A structural example diagram of a single-unit power generation model provided in this application embodiment; Figure 4 A schematic diagram of a wind farm power generation estimation device provided in an embodiment of this application; Figure 5 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] 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.

[0020] In one embodiment, this application provides a method for estimating the power generation of a wind farm. The following embodiments illustrate the application of this method to a server. It is understood that the wind farm power generation estimation method can be executed by a single server or by a server cluster consisting of multiple servers, and this application does not impose any specific limitations on this.

[0021] like Figure 1 As shown, this application provides a method for estimating the power generation of a wind farm, the method comprising: S101: Obtain the inflow wind speed sequence of the wind farm over a period of time, and compress the inflow wind speed sequence over time to obtain the target wind speed sequence.

[0022] Among them, the inflow wind speed sequence refers to the continuous data sequence of environmental inflow wind speed and direction changing over time, collected at the boundary of the wind farm or at a representative wind measurement location, reflecting the dynamic characteristics of wind resources at the wind farm entrance.

[0023] In this step, when the server receives a power generation assessment instruction, it identifies the wind farm to be assessed and collects continuous wind data for the area where the wind farm is located over a period of time, forming an inflow wind speed sequence. This sequence typically includes a timestamp, the wind speed at the corresponding moment, and the wind direction. Next, the time interval between adjacent data points in the inflow wind speed sequence is proportionally reduced according to a preset compression scale. For example, if the sampling interval of the inflow wind speed sequence is ten minutes and the compression scale is 600, the compressed interval becomes one second, thus compressing the original wind speed variation process of several hours or even days into a shorter simulation time window for calculation. Furthermore, when collecting the inflow wind speed sequence, the server can collect wind data for the corresponding time period from the anemometer towers in the wind farm.

[0024] Specifically, when simulating measured wind data spanning several years, directly running wake calculations and single-unit power generation models on the original timescale can lead to massive computational loads and excessively long simulation times. However, by using time compression processing, the original data spanning several years can be compressed into simulation data spanning several hours at a fixed ratio. For example, the time step can be compressed from 1 minute to 1 second, while fully preserving key characteristics such as intraday fluctuations and seasonal variations in wind conditions. This does not alter the temporal distribution and coupling relationship of wind speed and direction, ensuring that the physical meaning of subsequent simulations remains unaffected.

[0025] Furthermore, the setting of the compression scale needs to strike a balance between simulation accuracy and computational efficiency. The specific value can be determined by comprehensively considering the original data sampling interval, the dynamic response characteristics of the wind farm, and the temporal resolution requirements of the simulation target. This application does not impose specific restrictions in this regard.

[0026] S102: Input the target wind speed sequence into the preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm.

[0027] The wake calculation module is a functional module that calculates the impact of the wake of the upstream wind turbine on the wind speed attenuation of the downstream wind turbine based on the wake model and wind turbine parameters. It is used to quantify the wake interference effect between wind turbines. The wind speed and direction data refer to the actual inflow wind speed and direction values ​​corresponding to each wind turbine at each simulation moment after considering the wake effect. It is the real wind condition input after the wake effect correction.

[0028] In this step, the time-compressed target wind speed sequence is input into the wake calculation module. This module first reads the geographical location, rotor radius, thrust coefficient, and other turbine parameters of each turbine in the wind farm. Then, according to the time step of the target wind speed sequence, it calculates the wake impact on each turbine at each moment. Specifically, for each turbine, using the current inflow wind speed as the initial value, and combining the wind farm layout and the current wind direction, it determines whether the wake of the upstream turbine has an impact on it. The wind speed attenuation is calculated using the wake model formula, and the actual inflow wind speed of the turbine at the current moment is corrected to obtain the actual inflow wind speed of the turbine at the current moment. At the same time, the continuity of the inflow wind direction is maintained. Finally, the wind speed and wind direction data of each turbine at each simulation moment are output, forming the wind speed and wind direction data of each turbine.

[0029] Specifically, the wake effect is a key factor that causes the actual power generation efficiency of wind turbines to be lower than that of a single unit under ideal conditions. If the uncorrected inflow wind speed sequence is used directly for power simulation, the wake loss will be seriously underestimated, resulting in an overestimation of the power generation. However, by correcting the target wind speed sequence through the wake calculation module, the real inflow conditions of each wind turbine under different wind directions and conditions can be accurately restored. For example, when the wind direction is 90 degrees, the wake shading ratio of the front wind turbine to the rear wind turbine can be accurately calculated, and the attenuation wind speed of the rear wind turbine can be obtained, providing a more realistic input for the subsequent single-unit power generation model.

[0030] In one example, the wake model can employ the Jensen model, which assumes that the wind turbine wake extends linearly in a conical shape, with the wind speed uniformly decreasing within the wake region. The degree of decrease is determined by the thrust coefficient and the wake expansion constant. Other models capable of quantifying the wake effect can also be used for calculation, and this application does not impose specific limitations on them.

[0031] S103: Based on the preset wind direction sector division method, import the wind speed and wind direction data of each wind turbine into the preset single-unit power generation model, and simulate the wind turbine power curve of each wind turbine changing over time.

[0032] The single-unit power generation model is an input-output mapping system built based on the dynamic simulation of the entire wind turbine chain. It includes the wind turbine main control system, converter control system, aeromechanical model, and wind turbine electrical model, which are used to calculate the corresponding active power output through numerical simulation under given wind speed and wind direction. The wind turbine power curve refers to the time-series curve of the power generation of the wind turbine at each moment during continuous simulation time.

[0033] In this step, the server can first divide the continuous wind direction into several wind direction sectors according to preset rules, and then map the wind speed and wind direction data of each wind turbine after wake correction to the corresponding sector according to wind direction. Then, the wind speed and sector information are synchronously input into the pre-set single-unit power generation model. The single-unit power generation model calculates the power output time by time based on the built-in aerodynamic, control and electrical characteristics, and finally forms a continuous wind turbine power curve in time sequence.

[0034] Specifically, the preset rules for dividing wind direction sectors can be set to divide them equally based on the number of sectors, or by identifying the prevailing wind direction. Within the prevailing wind direction range, the azimuth angle can be set to 5 degrees, and within the non-prevailing wind direction range, the azimuth angle can be set to 10 degrees. It can be understood that the prevailing wind direction range covers the wind direction interval that occurs most frequently in the annual wind resource distribution of the wind farm. The cumulative proportion of wind energy within this interval often exceeds 70% or even higher. Therefore, using a more refined division for these sectors can more accurately characterize power changes under small wind direction deviations.

[0035] S104: Integrate and sum the power curves of each wind turbine to obtain the power generation of the wind farm under the current compressed scale, and restore the power generation of the wind farm to the compressed scale to obtain the actual power generation of the wind farm.

[0036] In this step, when the server obtains the power curves for each wind turbine, it uses the simulation step size under the compressed time scale as the integration unit to perform time integration on the power curve of each wind turbine over time, obtaining the power generation of a single wind turbine within the current compressed scale. Then, the power generation of each wind turbine is accumulated to obtain the power generation of the wind farm under the current compressed scale. Finally, based on the compression ratio used in the previous time compression, the power generation result under the compressed scale is multiplied by the ratio to restore the short-cycle simulation result to the power generation under the real time scale, ultimately obtaining the actual power generation of the wind farm.

[0037] In one embodiment, such as Figure 2 As shown, Figure 2 An example diagram illustrating a wind farm power generation estimation method provided in this application embodiment. Figure 2 In this context, the wind turbine single-unit power generation model is the same as the single-unit power generation model.

[0038] In the above embodiments, by introducing inflow wind speed sequence time compression and wake calculation, the real-time wind speed and direction data of each wind turbine can be efficiently restored. Combined with wind direction sector division and single-unit power generation model simulation, the single-unit power generation model can truly reflect the dynamic response characteristics of the wind turbine main control system and converter control strategy. It incorporates continuous wind conditions and wind turbine dynamic control characteristics into the evaluation, effectively making up for the shortcomings of traditional methods that are based only on wind speed probability distribution. It considers the impact of wind direction changes on wake loss and unit layout, and captures the seasonal and intraday variation patterns of wind speed and direction, thereby improving the accuracy of wind farm power generation assessment. At the same time, the simulation calculation is greatly reduced through time compression, achieving efficient and accurate wind farm power generation assessment.

[0039] In one embodiment, the inflow wind speed sequence is time-compressed to obtain the target wind speed sequence, including: Obtain the current compression scale, and compress the time values ​​corresponding to the time dimension in the inflow wind speed sequence according to the current compression scale to obtain the target wind speed sequence.

[0040] The current compression scale refers to the scaling factor used when scaling the inflow wind speed sequence in the time dimension.

[0041] In this embodiment, the current compression scale value is first read and determined. Then, based on this ratio, the timestamps corresponding to each set of data in the inflow wind speed sequence are scaled proportionally, keeping the original values ​​of wind speed and wind direction unchanged, and only adjusting the interval and total length of the time dimension, thereby forming a target wind speed sequence with shortened time and complete data structure.

[0042] It is understandable that by first obtaining the current compression scale and then proportionally compressing the time values ​​of the inflow wind speed sequence according to this scale, the duration can be shortened while fully preserving the temporal characteristics of wind speed and direction. This effectively reduces the computational load and time consumption caused by long-period wind condition data, and provides a unified and controllable time benchmark for subsequent wake calculations, power simulations, and power generation reconstruction. This improves the overall simulation efficiency without affecting the accuracy of power generation assessment.

[0043] In one example, the inflow wind speed sequence can be represented as:

[0044] In the formula, Indicates the first Real-time inflow wind speed data, including wind speed and direction. This indicates the wind speed sampling interval.

[0045] The inflow wind speed sequence after time compression, i.e., the target wind speed sequence, can be expressed as:

[0046] In the formula, Indicates the compression ratio.

[0047] In one embodiment, the target wind speed sequence is input into a preset wake calculation module to obtain wind speed and direction data for each wind turbine in the wind farm, including: S1: Obtain the spatial location, rotor radius, and thrust coefficient of each wind turbine in the wind farm.

[0048] S2: Based on the target wind speed sequence and the spatial position, rotor radius and thrust coefficient of each wind turbine, the actual wind speed and actual wind direction of each wind turbine after being affected by the wake of other wind turbines are calculated using the preset wake model, forming the wind speed and wind direction data of each wind turbine.

[0049] The rotor radius refers to the radius of the rotating plane of the wind turbine impeller, which determines the swept area of ​​the wind turbine to capture wind energy. The thrust coefficient is a dimensionless parameter that describes the wind turbine's extraction of momentum from the incoming airflow and the generation of aerodynamic drag, reflecting the degree to which the wind turbine impede the incoming flow.

[0050] In this embodiment, the spatial coordinates, rotor radius, and thrust coefficient of each wind turbine in the wind farm are first obtained. Then, the time-compressed target wind speed sequence is used as the inflow condition, and calculations are performed sequentially using a preset wake calculation model. Specifically, for each wind turbine, the wake superposition effect of the other wind turbines is traversed. Based on the relative position of the wind turbine and the rotor radius, the wake overlap area and influence range are determined. The wind speed attenuation is calculated in combination with the thrust coefficient, and finally, the actual wind speed and actual wind direction of each wind turbine under wake interference are obtained, forming complete wind speed and direction data. The dynamic characteristics of the thrust coefficient changing with the operating state are introduced in the above process, and the wake expansion and impeller overlap area are accurately calculated using the rotor radius, thereby truly reflecting the complex wake obstruction and superposition relationship between wind turbines under different wind directions, providing highly reliable input wind speed data for each wind turbine.

[0051] In one example, the expression for calculating wind speed attenuation is as follows:

[0052] In the formula, This represents the wind speed reduction caused by fan j to fan i, that is, the reduction in wind speed caused by upstream fan j to downstream fan i. This represents the initial wind speed of fan i, which can be set as the inflow wind speed. This represents the thrust coefficient of wind turbine j. This represents the overlap area of ​​the wakes of wind turbines i and j. This represents the wake radius of fan j at fan i. It represents pi (π).

[0053] In one embodiment, according to a preset wind direction sector division method, the wind speed and direction data of each wind turbine are imported into a preset single-unit power generation model to simulate and obtain the wind turbine power curve of each wind turbine over time, including: S1: Based on the wind speed and direction data of each wind turbine, determine the wind speed sequence and wind direction sequence of each wind turbine.

[0054] S2 maps the wind direction sequence of each wind turbine to the corresponding wind direction sector based on the preset wind direction sector division method, and obtains wind direction sector data.

[0055] S3: Input the wind speed sequence and corresponding wind direction sector data of each wind turbine into the preset single-unit power generation model, calculate the wind turbine power generation at each time, and generate the wind turbine power curve of each wind turbine over time.

[0056] Among them, the wind direction sector refers to several discrete intervals that are divided into 360 degrees according to preset rules.

[0057] In this embodiment, the actual wind speed and direction values ​​for each wind turbine at each moment are first extracted from the results output by the wake calculation module and arranged in chronological order to form wind speed and wind direction sequences. Next, according to preset wind direction sector division rules, such as dividing 360 degrees into 72 sectors, each five degrees wide, or using a non-uniform division with five degrees in the prevailing wind direction and ten degrees elsewhere, the actual wind direction value at each moment is converted into the corresponding sector number, resulting in a wind direction sector data sequence. Subsequently, the wind speed sequence value and wind direction sector data at the same moment are synchronously input into the single-unit power generation model. The power generation is calculated moment by moment according to the aerodynamic characteristics and control logic built into the single-unit power generation model. Finally, the power at each moment is arranged in chronological order to generate a wind turbine power curve showing the change of each wind turbine over time.

[0058] Specifically, by dividing wind direction into sectors, continuous wind direction is mapped into discrete sector data. Combined with wind speed sequence input, the power is calculated point by point in the single-unit power generation model and a time-series power curve is generated. This not only fully preserves the dynamic change characteristics of wind speed and wind direction, but also improves the efficiency and stability of power calculation through sector processing, and can accurately reflect the real power generation characteristics of wind turbines under different wind directions.

[0059] In one embodiment, such as Figure 3 As shown, Figure 3 This is a structural example diagram of a single-unit power generation model provided in an embodiment of this application. Figure 3In the wind turbine aeromechanical model, there are two main components: an aerodynamic model and a mechanical transmission chain model. The aerodynamic model calculates the captured aerodynamic torque and power based on wind speed and direction data received from the wake calculation module, using aerodynamic principles. This torque is then used by the mechanical transmission chain model to calculate the mechanical torque transmitted to the generator rotor. The wind turbine electrical model refers to the electrical model of the generator section, including a motor model and a converter model. The voltage and current output by the motor are connected to the power grid via the converter interface. The converter control outputs control pulses based on the electrical quantities fed back from the wind turbine electrical model to control the converter's operating state and output the active power command value given by the wind turbine master controller. The wind turbine master controller receives the turbine state quantities and, through a control strategy, issues mechanical control commands to the wind turbine aeromechanical model.

[0060] In one embodiment, the single-unit power generation model includes:

[0061] In the formula, The wind speed in the wind farm is azimuth angle is The output power of the point, Indicates the number of azimuth sectors. This indicates the sector index to which the currently calculated wind speed and direction data belongs. This represents the first discrete wind speed setting. Wind speed adjustment, This represents the first discrete wind speed setting. Wind speed adjustment, This indicates the sector index for wind direction.

[0062] In this embodiment, , It represents the adjacent wind speed step size, the maximum power tracking zone accuracy is at least 0.5 m / s, and the wind speed range includes at least the cut-in wind speed, the maximum power tracking zone, the constant speed zone, and the constant power zone. This represents the current calculated wind speed.

[0063] In one embodiment, the power curves of each wind turbine are integrated and summed to obtain the wind farm's power generation at the current compression scale, including: S1: Integrate the power curve of each wind turbine to obtain the power generation of each wind turbine under the current compression scale.

[0064] S2: Sum the power generation of each wind turbine under the current compression scale to obtain the power generation of the wind farm under the current compression scale.

[0065] In this embodiment, the power curve of a single wind turbine over time is first integrated using the simulation time step as the integration unit. The power generation at each moment is multiplied by the corresponding time step and then accumulated point by point to obtain the power generation of that wind turbine under the current compression time scale. After completing the individual integration of all wind turbines, the power generation values ​​of each wind turbine under the compression scale are added together to obtain the total power generation of the entire wind farm under the current simulation scale, i.e., the wind farm's power generation. In this process, the true power generation characteristics of each wind turbine affected by wake and wind direction are preserved, while also achieving accurate statistics of the total power generation of the wind farm.

[0066] In one example, the process of calculating the wind farm's power generation at the current compression scale can be represented as:

[0067] In the formula, This indicates the amount of electricity generated by the wind farm. This represents the power curve of the m-th wind turbine. Indicates the simulation start time. Indicates the simulation end time. This indicates the number of wind turbines in a wind farm.

[0068] In one embodiment, the wind farm's power generation is compressed and scaled back to obtain the actual power generation of the wind farm, including: S1: Obtain the compression ratio coefficient corresponding to the current compression scale.

[0069] S2: Multiply the wind farm's power generation under the current compression scale by the compression ratio factor to obtain the wind farm's actual power generation.

[0070] In this embodiment, a compression ratio coefficient corresponding to the current compression scale is extracted and determined from the previous time compression stage. This coefficient is consistent with the scaling ratio used in the time compression stage. After obtaining the accurate compression ratio coefficient, the wind farm power generation calculated under the compressed scale is multiplied by this coefficient to restore the equivalent total power generation obtained from the short-cycle simulation to the actual power generation under the real time scale, i.e., the actual power generation of the wind farm. This leverages the high-efficiency computational advantages of time compression while ensuring the accuracy and engineering applicability of the power generation results.

[0071] In one example, the process of calculating the actual power generation of a wind farm can be represented as:

[0072] In the formula, This indicates the actual power generation of the wind farm. This represents the compression ratio coefficient.

[0073] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0074] The wind farm power generation estimation device provided in the embodiments of this application is described below. The wind farm power generation estimation device described below and the wind farm power generation estimation method described above can be referred to each other.

[0075] like Figure 4 As shown, this application provides a wind farm power generation estimation device 200, the device comprising: The wind speed acquisition module 201 is used to acquire the inflow wind speed sequence of the wind farm over a period of time, and to compress the inflow wind speed sequence over time to obtain the target wind speed sequence. The wake calculation module 202 is used to input the target wind speed sequence into the preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm. The power simulation module 203 is used to import the wind speed and direction data of each wind turbine into the preset single-unit power generation model according to the preset wind direction sector division method, and simulate the wind turbine power curve of each wind turbine changing over time. The power generation estimation module 204 is used to integrate and sum the power curves of each wind turbine to obtain the power generation of the wind farm under the current compressed scale, and to restore the power generation of the wind farm to the compressed scale to obtain the actual power generation of the wind farm.

[0076] In the above embodiments, by introducing inflow wind speed sequence time compression and wake calculation, the real-time wind speed and direction data of each wind turbine can be efficiently restored. Combined with wind direction sector division and single-unit power generation model simulation, the single-unit power generation model can truly reflect the dynamic response characteristics of the wind turbine main control system and converter control strategy. It incorporates continuous wind conditions and wind turbine dynamic control characteristics into the evaluation, effectively making up for the shortcomings of traditional methods that are based only on wind speed probability distribution. It considers the impact of wind direction changes on wake loss and unit layout, and captures the seasonal and intraday variation patterns of wind speed and direction, thereby improving the accuracy of wind farm power generation assessment. At the same time, the simulation calculation is greatly reduced through time compression, achieving efficient and accurate wind farm power generation assessment.

[0077] In one embodiment, the wind speed acquisition module includes: The scale acquisition submodule is used to obtain the current compression scale; The sequence compression submodule is used to compress the time values ​​corresponding to the time dimension in the inflow wind speed sequence according to the current compression scale to obtain the target wind speed sequence.

[0078] In one embodiment, the wake calculation module includes: The data acquisition submodule is used to acquire the spatial location, rotor radius, and thrust coefficient of each wind turbine in the wind farm; The wake calculation submodule is used to calculate the actual wind speed and actual wind direction of each wind turbine after being affected by the wake of other wind turbines, based on the target wind speed sequence and the spatial position, rotor radius and thrust coefficient of each wind turbine, using a preset wake model, thus forming the wind speed and wind direction data of each wind turbine.

[0079] In one embodiment, the power simulation module includes: The sequence determination submodule is used to determine the wind speed sequence and wind direction sequence for each wind turbine based on the wind speed and wind direction data of each wind turbine. The sector determination submodule is used to map the wind direction sequence of each wind turbine to the corresponding wind direction sector based on the preset wind direction sector division method, so as to obtain wind direction sector data. The curve generation submodule is used to input the wind speed sequence and corresponding wind direction sector data of each wind turbine into the preset single-unit power generation model, calculate the wind turbine power generation at each time, and generate the wind turbine power curve of each wind turbine over time.

[0080] In one embodiment, the power simulation module includes:

[0081] In the formula, The wind speed in the wind farm is azimuth angle is The output power of the point, Indicates the number of azimuth sectors. This indicates the sector index to which the currently calculated wind speed and direction data belongs. This represents the first discrete wind speed setting. Wind speed adjustment, This represents the first discrete wind speed setting. Wind speed adjustment, This indicates the sector index for wind direction.

[0082] In one embodiment, the power generation estimation module includes: The curve integration submodule is used to integrate the power curve of each wind turbine to obtain the power generation of each wind turbine under the current compression scale. The power generation calculation submodule is used to sum the power generation of each wind turbine under the current compression scale to obtain the power generation of the wind farm under the current compression scale.

[0083] In one embodiment, the power generation estimation module includes: The coefficient acquisition submodule is used to obtain the compression ratio coefficient corresponding to the current compression scale; The power generation restoration submodule is used to multiply the wind farm's power generation under the current compression scale by the compression ratio coefficient to obtain the wind farm's actual power generation.

[0084] The division of modules in the aforementioned wind farm power generation estimation device is merely illustrative. In other embodiments, the wind farm power generation estimation device can be divided into different modules as needed to complete all or part of its functions. Each module in the aforementioned wind farm power generation estimation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0085] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the wind farm power generation estimation method as described in any of the above embodiments.

[0086] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the wind farm power generation estimation method as described in any of the above embodiments.

[0087] Indicatively, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 5The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the wind farm power generation estimation method of any of the above embodiments.

[0088] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0089] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0090] 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 a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0091] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0092] 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. 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 estimating the power generation of a wind farm, characterized in that, The method includes: Obtain the inflow wind speed sequence of the wind farm over a period of time, and compress the inflow wind speed sequence over time to obtain the target wind speed sequence; The target wind speed sequence is input into a preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm. Based on the preset wind direction sector division method, the wind speed and wind direction data of each wind turbine are imported into the preset single-unit power generation model, and the power curve of each wind turbine changing over time is simulated. By integrating and summing the power curves of each wind turbine, the power generation of the wind farm under the current compressed scale is obtained. Then, the power generation of the wind farm is restored to the compressed scale to obtain the actual power generation of the wind farm.

2. The wind farm power generation estimation method according to claim 1, characterized in that, The step of time-compressing the inflow wind speed sequence to obtain the target wind speed sequence includes: Get the current compression scale; According to the current compression scale, the time values ​​corresponding to the time dimension in the inflow wind speed sequence are compressed to obtain the target wind speed sequence.

3. The wind farm power generation estimation method according to claim 1, characterized in that, The step of inputting the target wind speed sequence into a preset wake calculation module to obtain wind speed and direction data for each wind turbine in the wind farm includes: Obtain the spatial location, rotor radius, and thrust coefficient of each wind turbine in the wind farm; Based on the target wind speed sequence and the spatial position, rotor radius and thrust coefficient of each wind turbine, the actual wind speed and actual wind direction of each wind turbine after being affected by the wake of other wind turbines are calculated using a preset wake model, thus forming the wind speed and wind direction data of each wind turbine.

4. The wind farm power generation estimation method according to claim 1, characterized in that, The process involves importing the wind speed and direction data of each wind turbine into a preset single-unit power generation model based on a pre-defined wind direction sector division method, and simulating the power curve of each wind turbine over time, including: Based on the wind speed and direction data of each wind turbine, determine the wind speed sequence and wind direction sequence for each wind turbine; Based on the preset wind direction sector division method, the wind direction sequence of each wind turbine is mapped to the corresponding wind direction sector to obtain wind direction sector data; The wind speed sequence and corresponding wind direction sector data of each wind turbine are input into a preset single-unit power generation model to calculate the power generation of the wind turbine at each moment, so as to generate the power curve of each wind turbine over time.

5. The wind farm power generation estimation method according to any one of claims 1 to 4, characterized in that, The single-unit power generation model includes: In the formula, The wind speed in the wind farm is azimuth angle is The output power of the point, Indicates the number of azimuth sectors. This indicates the sector index to which the currently calculated wind speed and direction data belongs. Represents the first discrete wind speed setting. Wind speed adjustment, Represents the first discrete wind speed setting. Wind speed adjustment, This indicates the sector index for wind direction.

6. The wind farm power generation estimation method according to claim 1, characterized in that, The process of integrating and summing the power curves of each wind turbine to obtain the wind farm's power generation under the current compression scale includes: Integrate the power curve of each wind turbine to obtain the power generation of each wind turbine under the current compression scale. The power generation of each wind turbine under the current compression scale is summed to obtain the power generation of the wind farm under the current compression scale.

7. The wind farm power generation estimation method according to claim 1, characterized in that, The process of compressing and scaling the power generation of the wind farm to obtain the actual power generation of the wind farm includes: Obtain the compression ratio coefficient corresponding to the current compression scale; Multiply the wind farm's power generation under the current compression scale by the compression ratio coefficient to obtain the wind farm's actual power generation.

8. A wind farm power generation estimation device, characterized in that, The device includes: The wind speed acquisition module is used to acquire the inflow wind speed sequence of the wind farm over a period of time, and to compress the inflow wind speed sequence over time to obtain the target wind speed sequence. The wake calculation module is used to input the target wind speed sequence into the preset wake calculation module to obtain the wind speed and direction data of each wind turbine in the wind farm. The power simulation module is used to import the wind speed and direction data of each wind turbine into the preset single-unit power generation model according to the preset wind direction sector division method, and simulate the wind turbine power curve of each wind turbine over time. The power generation estimation module is used to integrate and sum the power curves of each wind turbine to obtain the power generation of the wind farm under the current compressed scale, and then restore the power generation of the wind farm to the compressed scale to obtain the actual power generation of the wind farm.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the wind farm power generation estimation method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions that, when executed by the one or more processors, perform the steps of the wind farm power generation estimation method as described in any one of claims 1 to 7.

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