An active wake control method and device suitable for power limiting operation of a wind farm

Abstract

The present application relates to the technical field of wind farm active wake control, and discloses an active wake control method and device suitable for wind farm limited power operation, wherein the present application combines the active scheduling suggestion of the unit affected by the wake influence level, matches the actual information of limited power and shutdown, customizes the wake optimization control strategy in the limited power mode and the non-limited power mode, accurately adapts to the real-time operation working condition of the station, can fully consider the actual wake effect loss and the unit fatigue damage, avoids the wake redirection misalignment problem caused by the mismatch between the strategy and the field working condition, effectively improves the adaptability and reliability of the wake control strategy, guarantees that the wake redirection always plays a positive benefit effect, and greatly reduces the benefit loss caused by the strategy failure.

Description

Technical Field

[0001] This invention relates to the field of active wake control technology for wind farms, and specifically to an active wake control method and device suitable for power-limited operation of wind farms. Background Technology

[0002] In wind farms, the wake effect generated by upstream wind turbines can significantly impact downstream turbines located in the wake region. Specifically, downstream turbines face problems such as reduced wind speed and increased turbulence, which in turn leads to a decrease in wind energy capture rate and an increase in fatigue load, seriously affecting the overall operating efficiency and stability of the wind farm. Therefore, effectively mitigating the wake effect and improving the operating performance of wind farms has become an important technical problem that urgently needs to be solved in the field of wind farm operation and control.

[0003] Currently, in the wind farm operation and control phase, implementing active wake control for the turbines is an effective means of reducing wake effects. This method is mainly based on wake redirection, which involves changing the yaw action. Existing wake redirection technologies, when optimizing based on yaw action, often use the yaw angle as the optimization variable, employing single-objective or multi-objective optimization modes. During the calculation process, different wind conditions are considered, wake losses are calculated using engineering wake models, and optimization objectives are solved using optimization algorithms. However, these methods only use the relative yaw angle range as a constraint and do not fully consider that if the generated wake control strategy encounters situations such as upstream turbines undergoing maintenance or power-limited operation, the actual wake effect loss cannot be effectively accounted for, leading to the failure of the original control strategy. Consequently, the benefits generated by wake redirection or power control methods will be significantly reduced. Summary of the Invention

[0004] This invention provides an active wake control method and device suitable for power-limited operation of wind farms, which solves the problem that when the upstream units are shut down for maintenance or power-limited operation, the actual wake effect loss cannot be effectively considered, causing the original control strategy to fail and the benefit of the wake redirection method to be significantly reduced.

[0005] In a first aspect, the present invention provides an active wake control method suitable for power-limited operation of wind farms, the method comprising: The wind condition data and actual coordinate information of the wind turbine are input into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine. Based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed, the degree of wake effect of each wind turbine is determined. Based on the active power dispatch strategy recommendations corresponding to the wake influence levels of each wind turbine and the power limiting and shutdown information of each wind turbine, wake optimization control strategies for each wind turbine are formulated according to power limiting mode and non-power limiting mode.

[0006] This invention combines active power dispatch recommendations based on the level of wake effect on the generating unit with actual information on power limitation and shutdown. It customizes wake optimization control strategies for power-limited and unlimited modes, accurately adapting to the real-time operating conditions of the station. It fully considers the actual wake effect loss, avoiding the problem of wake redirection inaccuracy caused by mismatch between the strategy and the on-site operating conditions. It effectively improves the adaptability and reliability of the wake control strategy, ensuring that wake redirection always has a positive benefit effect and significantly reducing the benefit loss caused by strategy failure.

[0007] In one optional implementation, the step of inputting wind condition data and actual coordinate information of the wind turbine into a preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind farm turbine includes: Collect wind condition data, actual coordinate information, and wind turbine parameters; The wind condition data, actual coordinate information and wind turbine parameters of the wind turbine are input into the wake velocity loss model corresponding to the preset wake analysis model, and the wake loss generated by all upstream units to the downstream unit is calculated. Input the wake loss corresponding to the downstream unit into the wake superposition model corresponding to the wake analysis model, and calculate the total wake velocity loss of the downstream unit after the superposition of the wake effects of all upstream units. Based on the total wake velocity loss, calculate the wind farm unit output power under zero yaw conditions or yaw conditions.

[0008] This invention systematically collects multi-dimensional basic data of wind farms, and calculates the velocity loss of the wake analysis model and the superposition model step by step. It first calculates the wake loss of a single upstream unit, and then converts it into the total superposition loss, which accurately restores the actual process of wake action between units, greatly improves the accuracy of wake velocity loss calculation, and can reliably obtain the unit output power under different yaw conditions.

[0009] In one optional implementation, determining the wake effect level of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed includes: Using the wind turbine output power corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed, calculate the percentage of wake effect loss corresponding to the output power of each wind turbine. Based on the percentage of wake effect loss corresponding to the output power of each wind turbine and the wind condition data, the degree of wake effect impact on each wind turbine is classified.

[0010] This invention quantifies the percentage loss due to wake effect and classifies the degree of wake impact of wind turbines by combining actual wind data. This provides a precise quantitative basis and actual operating condition support for the level determination, avoiding the one-sidedness of a single-dimensional judgment. It can objectively and accurately distinguish the degree of wake impact of different units, providing a scientific classification basis for the subsequent active power dispatch strategy of the wind farm. This makes the dispatch recommendations more in line with the wake characteristics of the actual operation of the wind farm, and improves the rationality and pertinence of the dispatch strategy.

[0011] In one optional implementation, the step of classifying the degree of wake effect impact on each wind turbine by combining the percentage of wake effect loss corresponding to the output power of each wind turbine and the wind condition data includes: When the percentage of wake effect loss corresponding to the output power of each wind turbine is less than the first loss threshold and the influence range of the prevailing wind direction corresponding to the wind condition data is in the upstream area, the wake effect level of the wind turbine is determined to be the first level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the first loss threshold and less than the second loss threshold, and the influence range of the prevailing wind direction is stable, the wake effect level of the wind turbine is determined to be the second level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the second loss threshold and less than the third loss threshold, and the influence range of the main wind direction is affected by the wake superposition of a preset number of upstream wind turbines, the wake effect level of the wind turbine is determined to be the third level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than the third loss threshold and the influence range of the main wind direction is affected by the wake superposition of the upstream wind turbine, the wake effect level of the wind turbine is determined to be the fourth level.

[0012] This invention combines the quantitative index of the percentage loss of wake effect with the operating characteristics of the influence range of the prevailing wind direction and the superposition of wakes of upstream units to establish a multi-dimensional rule for judging the wake impact level of units. This makes the level classification more in line with the actual wake effect law of wind farms, and the judgment results are accurate and in line with the on-site operating conditions, and can clearly distinguish units with different degrees of impact.

[0013] In an optional implementation, the method further includes: Based on the prevailing wind direction scenario corresponding to the wind condition data, a first active power dispatch strategy recommendation is formulated for each wind turbine corresponding to the level of wake influence. Based on the non-dominant wind direction scenario corresponding to the wind condition data, a second active power dispatch strategy suggestion is formulated for each wind turbine corresponding to the level of wake influence.

[0014] This invention provides differentiated active power dispatching strategy recommendations for turbines with varying wake impact levels based on dominant and non-dominant wind direction scenarios. This aligns with the core influence of wind direction on wake effects, avoiding the limitations of uniform dispatching. By precisely targeting wake characteristics under different wind direction scenarios, the dispatching recommendations are highly matched to the actual wake operation conditions of the wind farm, effectively reducing the cumulative impact of wake effects.

[0015] In one optional implementation, the step of formulating wake optimization control strategies for each wind turbine based on the active power dispatch strategy recommendations corresponding to the wake influence levels of each wind turbine and the power limiting and shutdown information of each wind turbine, according to power-limited and non-power-limited modes, includes: Obtain the power limitation and shutdown information of each wind turbine unit; Based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, determine whether each wind turbine is in a power limitation operation state. If not, the parameters of the wind turbines corresponding to the wind turbines in the shutdown state are corrected and the corresponding first wake optimization control strategy is generated. If so, the wind turbine parameters of the wind turbines in the limited power operation state or shutdown state are corrected and a corresponding second wake optimization control strategy is generated.

[0016] This invention combines active power dispatch recommendations based on the wake impact level of wind turbines, judges the operating status based on actual power limitation and shutdown information, and specifically adjusts turbine parameters to generate wake optimization control strategies for different modes, accurately adapting to the real-time operating conditions of wind farms. It effectively solves the problems of poor adaptability caused by traditional strategies that do not consider power limitation and shutdown and have fixed parameters, ensuring a high degree of matching between the control strategy and the actual operating status of the turbines. This improves the accuracy and reliability of wake control, ensuring that wake regulation always yields positive benefits.

[0017] Secondly, the present invention provides an active wake control device suitable for power-limited operation of wind farms, the device comprising: The input module is used to input the wind condition data and actual coordinate information of the wind turbine into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine. The rating module is used to determine the degree of wake effect impact of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed. A formulation module is used to formulate wake optimization control strategies for each wind turbine in both power-limited and non-power-limited modes, based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the active wake control method applicable to power-limited operation of wind farms described in the first aspect or any corresponding embodiment.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the active wake control method applicable to power-limited operation of a wind farm as described in the first aspect or any corresponding embodiment.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the active wake control method applicable to power-limited operation of a wind farm as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of a first active wake control method applicable to power-limited operation of a wind farm according to an embodiment of the present invention; Figure 2 This is a second flowchart illustrating an active wake control method applicable to power-limited operation of a wind farm according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the third active wake control method applicable to power-limited operation of wind farms according to an embodiment of the present invention; Figure 4 This is a yaw optimization flowchart of an active wake control method applicable to power-limited operation of wind farms according to an embodiment of the present invention; Figure 5 This is a structural block diagram of an active wake control device for wind farm power-limited operation according to an embodiment of the present invention; Figure 6This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] This invention provides an active wake control method suitable for wind farms operating under power-limited conditions. By combining active power dispatch suggestions based on the wake impact level of the turbines with actual power-limited and shutdown information, the method customizes wake optimization control strategies for power-limited and non-power-limited modes. This accurately adapts to the real-time operating conditions of the wind farm, fully considers actual wake effect losses, and avoids wake redirection inaccuracies caused by mismatch between the strategy and the on-site conditions. This effectively improves the adaptability and reliability of the wake control strategy, ensures that wake redirection always has a positive benefit effect, and significantly reduces the benefit loss caused by strategy failure.

[0027] According to an embodiment of the present invention, an active wake control method for wind farm power-limited operation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides an active wake control method suitable for power-limited operation of wind farms. Figure 1 This is a flowchart of an active wake control method for wind farm power-limited operation according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Input the wind condition data and actual coordinate information of the wind turbine into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine.

[0029] It should be noted that wind condition data refers to relevant data reflecting the natural wind resource characteristics of a wind farm, including wind direction, wind speed, environmental turbulence intensity, wind shear index, and historical wind resource data of the wind farm.

[0030] Actual coordinate information refers to the actual coordinate information of the installed unit, which is represented by CGCS2000 coordinate system data and includes the corresponding altitude data.

[0031] Wake flow analytical models refer to mainstream models such as the Jensen and BPA models, which include wake flow velocity loss models and wake flow superposition models.

[0032] Wake velocity loss refers to the reduction in inflow velocity of downstream wind turbines compared to the original inflow velocity due to the wake obstruction caused by upstream turbines.

[0033] The output power of a wind turbine refers to the actual power value that a wind turbine can convert into electrical energy under specific wind conditions and wake effects.

[0034] In this embodiment of the invention, a wind farm wake calculation method with a certain degree of fidelity is established based on engineering wake analysis models (such as mainstream models like Jensen and BPA models) that consider velocity loss, wake deflection, and wake superposition. This method can calculate the output power of wind farm turbines under zero yaw / yaw conditions. Specifically, the upstream and downstream positional relationships between turbines in the windward direction and the wake impact status of downstream turbines are determined based on actual coordinate information. Then, the wake velocity loss of each upstream turbine on the downstream turbine is calculated using the model. The total wake velocity loss of the downstream turbine is obtained by combining the wake superposition rules. Finally, the output power of each wind turbine under the corresponding wind conditions is calculated based on the total wake velocity loss.

[0035] Step S102: Determine the wake effect level of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed.

[0036] It should be noted that rated wind speed refers to the minimum wind speed specified in the design of the wind turbine unit, which is required to output rated power.

[0037] Theoretical power refers to the ideal power output value of a wind turbine at its rated wind speed, without the influence of external factors such as wake, calculated according to the design parameters.

[0038] The percentage loss due to wake effect refers to the difference between the actual output power of a wind turbine affected by the wake and the theoretical power at rated wind speed, expressed as a percentage of that theoretical power.

[0039] The wake effect level refers to the classification of the degree of influence of the wake effect on the wind turbine by the upstream unit, based on the percentage loss of the wake effect and the actual operating conditions.

[0040] In this embodiment of the invention, the influence range of the prevailing wind direction corresponding to the historical wind resource data of the wind farm in the wind condition data, the superposition of the wake of the upstream unit, and other actual operating conditions are combined, and the degree of influence of the wake on each wind turbine is comprehensively determined according to the preset level classification threshold and judgment conditions.

[0041] Step S103: Based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, formulate wake optimization control strategies for each wind turbine in power-limited mode and non-power-limited mode respectively.

[0042] It should be noted that the active power dispatch strategy recommendation refers to a targeted recommendation scheme formulated based on the degree of influence of wake on wind turbines and the wind direction scenario of the wind farm, to guide the active power output and power dispatch of wind turbines.

[0043] Power limitation and shutdown information refers to the relevant data on power limitation values, power limitation status, and start-up and shutdown status of each unit in a wind farm due to grid dispatching, equipment maintenance, and other reasons.

[0044] Power-limited mode refers to the operating mode in which wake optimization control strategies are formulated when wind farms are operating under power-limited conditions.

[0045] Unlimited power mode refers to the operating mode in which wake optimization control strategies are formulated when the wind farm operates without turbine power limitation.

[0046] Wake optimization control strategy refers to the specific implementation plan, including control parameters such as yaw angle, formulated for each wind turbine to reduce the impact of wake effect in wind farms.

[0047] In this embodiment of the invention, it is determined whether the wind farm as a whole is in a power-limited operation state. Then, the operating parameters of the corresponding units are dynamically corrected for the power-limited and unlimited modes respectively. Yaw optimization calculation is carried out in combination with the preset optimization algorithm and optimization target. Finally, according to different modes, wake optimization control strategies adapted to the actual operating conditions of each wind turbine are formulated.

[0048] This embodiment provides an active wake control method suitable for power-limited operation of wind farms. Figure 2This is a flowchart of active wake control applicable to power-limited operation of wind farms according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Input the wind condition data and actual coordinate information of the wind turbine into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine.

[0049] Specifically, step S201 includes: Step S2011: Collect wind condition data, actual coordinate information, and wind turbine parameters.

[0050] It should be noted that wind turbine parameters refer to data including rotor diameter, hub height, thrust and power curves under actual air density, and allowable yaw angle range.

[0051] In embodiments of the present invention, such as Figure 3 As shown, basic input data is collected, including wind turbine wind condition data, wind turbine parameters, actual coordinate location, and a yaw load database. The main data types are: 1) Wind condition data includes wind direction, wind speed, environmental turbulence intensity, wind shear index, and historical wind resource data of the wind farm; 2) Wind turbine parameters include rotor diameter, hub height, thrust and power curves under actual air density, and allowable yaw angle range; 3) Actual coordinate location refers to the actual installation coordinates of the turbine, expressed in CGCS2000 coordinate system and including corresponding altitude data; 4) The yaw load database is equivalent fatigue load data of key components of the turbine under different yaw combinations, calculated using OpenFAST or FAST.Farm software.

[0052] Step S2012: Input the wind condition data, actual coordinate information and wind turbine parameters of the wind turbine into the wake velocity loss model corresponding to the preset wake analysis model, and calculate the wake loss of all upstream units to downstream units.

[0053] It should be noted that the wake velocity loss model refers to the sub-model in the wake analytical model specifically used to calculate the reduction in wake wind speed caused by upstream wind turbines to downstream turbines.

[0054] Wake loss refers to the power loss caused by the wake effect generated by the operation of upstream units, which leads to a decrease in the inflow wind speed and a reduction in the wind energy capture capacity of downstream units.

[0055] In this embodiment of the invention, based on the actual coordinate information, all upstream units corresponding to the downstream units in the windward direction are identified. Then, combined with wind condition data and wind turbine parameters, the wake loss generated by each upstream unit to the downstream unit is calculated individually using the wake velocity loss model, thus obtaining the wake loss result of each upstream unit.

[0056] Specifically, the rapid wake calculation method for wind farms uses the actual coordinate information of wind turbines to determine the upstream and downstream positions of the turbines in the windward direction and whether the downstream turbines are affected by the wake of the upstream turbines. Then, it uses wake velocity loss models that consider wake deflection (such as the Jensen model or the BP model) to calculate the wake velocity loss of the downstream turbines. i All upstream units j To each i Wake loss Then, the downstream unit is calculated using a wake superposition model (such as a linear superposition model, a sum of squares superposition model, etc.). i Wake velocity loss due to the wake effect of all upstream units .

[0057] Preferably, the wake velocity loss model can be a two-dimensional Gaussian wake model (BP model):

[0058]

[0059]

[0060] In the formula, Indicates the wake characteristic width; Indicates the thrust coefficient of the wind turbine; Indicates the diameter of the wind turbine; This represents the wake expansion coefficient, with a reference range of 0.04-0.08. Based on Barthelmie's research, it is recommended that onshore wind turbines use a value of [missing value]. Offshore wind turbine units ; Indicates the incoming wind speed; r Indicates the radius of the wind turbine; x This indicates the straight-line distance from the upstream unit along the wind direction.

[0061] Step S2013: Input the wake loss corresponding to the downstream unit into the wake superposition model corresponding to the wake analysis model, and calculate the total wake velocity loss of the downstream unit after the wake effect of all upstream units is superimposed.

[0062] It should be noted that the wake superposition model refers to a sub-model in the wake analysis model specifically used to comprehensively calculate the wake loss of a single downstream unit from multiple upstream units.

[0063] Total wake velocity loss refers to the total reduction in inflow velocity of the downstream unit compared to the original inflow velocity after the combined effect of the wake effects of all upstream units.

[0064] In this embodiment of the invention, the downstream unit is calculated using a wake superposition model (such as a linear superposition model, a sum of squares superposition model, etc.). i Wake velocity loss due to the wake effect of all upstream units The calculation formula for the wake superposition model is as follows:

[0065] In the formula, N This indicates the total number of all upstream generating units. Indicates downstream units i The inflow velocity after being affected by the wake effect of a single upstream unit; Indicates upstream units j The inflow velocity after wake loss.

[0066] Step S2014: Calculate the output power of the wind farm unit under zero yaw condition or yaw condition based on the total wake velocity loss.

[0067] It should be noted that zero yaw condition refers to the operating condition of the wind turbine where the swept surface of the wind turbine rotor is perpendicular to the direction of the incoming wind, and no yaw adjustment is made.

[0068] Yaw conditions refer to the operating conditions in which, in order to reduce the impact of the wake effect, the wind turbine adjusts the swept surface of the rotor to form a certain angle with the direction of the incoming wind through the yaw system.

[0069] In this embodiment of the invention, the actual inflow wind speed of the downstream unit after being affected by the wake is calculated, and then the output power of a single wind turbine under zero yaw condition or yaw condition is calculated according to the power conversion characteristics of the wind turbine. The total output power of the wind farm units under the corresponding operating conditions is obtained by summing them up.

[0070] Specifically, the fatigue load database is generated using OpenFAST or FAST.Farm software. This database is used in subsequent steps to read fatigue damage data of key components under different yaw combinations and to calculate the total fatigue damage of key components in the wind turbine cluster under different yaw angle combinations. The equivalent fatigue load database is formatted as [wind speed, yaw angle, key component code, equivalent fatigue damage load (DEL)]. Key components include wind turbine blade roots, towers, etc.

[0071] Step S202: Determine the wake effect level of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed.

[0072] In some optional implementations, step S202 above includes: Step S2021: Using the wind turbine output power corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed, calculate the percentage of wake effect loss corresponding to the output power of each wind turbine.

[0073] In this embodiment of the invention, based on the wake calculation method established in step S201, the basic input information of the wind turbine is input to calculate the output power of the zero-yaw wind farm group under various wind conditions. and the output power of each wind turbine and the theoretical power at rated wind speed By comparison, the percentage of wake effect loss corresponding to the unit's output power is obtained. ;in, The calculation formula is:

[0074] In the formula, Indicates air density; Indicates the radius of the wind turbine; This indicates the inflow wind speed after the wind turbine is affected by the superposition of the upstream wake.

[0075] Specifically, the output power of zero-yaw wind farm groups under various wind conditions. The calculation formula is: .

[0076] Step S2022: Based on the percentage of wake effect loss corresponding to the output power of each wind turbine and wind condition data, classify the degree of wake effect impact of each wind turbine.

[0077] In this embodiment of the invention, key operating condition information such as the influence range of the main wind direction and the superposition characteristics of the wake of the upstream units are extracted from the wind condition data. The wake impact level of each unit is comprehensively judged by comparing it with the preset wake impact level classification threshold and judgment conditions, so as to classify the wake impact level of each wind turbine unit.

[0078] Specifically, step S2022 above includes: Step a1: When the percentage of wake effect loss corresponding to the output power of each wind turbine is less than the first loss threshold and the influence range of the prevailing wind direction corresponding to the wind condition data is in the upstream area, the wake effect level of the wind turbine is determined to be the first level.

[0079] It should be noted that the first loss threshold is 5.

[0080] The first level is Level I - no wake effect.

[0081] In this embodiment of the invention, the percentage of wake effect loss is... <5, and always located in the upstream area within the historical prevailing wind direction range, without producing wake obstruction to downstream units (refer to the actual coordinate information and altitude of the wind turbine in step S201), that is, the degree of wake influence on the wind turbine is determined to be Level I - no wake influence.

[0082] Step a2: When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the first loss threshold, less than the second loss threshold, and the influence range of the main wind direction is stable, the wake effect level of the wind turbine is determined to be the second level.

[0083] It should be noted that the second loss threshold is 10.

[0084] The second level is Level II - Mild wake impact.

[0085] In this embodiment of the invention, the percentage of wake effect loss is... ≥5 and <10 (i.e.) Due to the superposition of the wake of a single upstream unit, and the relatively stable range of the historical prevailing wind direction, the degree of wake impact on the wind turbine is determined to be Level II - mild wake impact.

[0086] Step a3: When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the second loss threshold, less than the third loss threshold, and the influence range of the main wind direction is affected by the wake superposition of the preset number of wind turbines upstream, the wake effect level of the wind turbine is determined to be the third level.

[0087] It should be noted that the third loss threshold is 20.

[0088] The preset quantity is 2 units.

[0089] The third level is Level III - Moderate wake impact.

[0090] In this embodiment of the invention, the percentage of wake effect loss is... ≥10 and <20 (i.e.) Due to the combined effect of the wakes of two or more upstream units, the degree of wake impact on the wind turbine units is determined to be Level III - Moderate wake impact.

[0091] Step a4: When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than the third loss threshold and the influence range of the main wind direction is affected by the wake superposition of the upstream wind turbine, the wake effect level of the wind turbine is determined to be the fourth level.

[0092] It should be noted that Level 4 is Level IV - Severe wake impact.

[0093] In this embodiment of the invention, the percentage of wake effect loss is... ≥20%, continuously affected by the strong superposition of wakes from multiple upstream units under the prevailing wind direction, that is, the degree of wake impact on the wind turbine units is determined to be Level IV - severe wake impact.

[0094] It is worth mentioning that, based on the percentage of wake loss Based on historical wind resource data, the degree of wake impact on each turbine was assessed. According to the assessment results, the turbines were prioritized for each wind condition, in the order of Level I > Level II > Level III > Level IV, with comparisons made within the same level. Specific numerical values.

[0095] Step S203: Based on the prevailing wind direction scenario corresponding to the wind condition data, formulate corresponding first active power dispatch strategy recommendations for wind turbines corresponding to each level of wake influence.

[0096] It should be noted that the dominant wind direction scenario refers to the operating scenario corresponding to the wind direction in the wind farm whose frequency of occurrence exceeds a preset threshold.

[0097] The first active power dispatch strategy recommendation refers to the active power dispatch guidance scheme for wind turbines that is adapted to the wake characteristics of the prevailing wind direction scenario.

[0098] In this embodiment of the invention, based on the prevailing wind direction scenario corresponding to the wind condition data, and combined with the characteristics and influence of the wake effect in this scenario, and according to the classification criteria of each wake-affected level and the operating characteristics of the unit, a first active power dispatch strategy recommendation adapted to the wake state under the prevailing wind direction is formulated for wind turbines of different levels.

[0099] Specifically, based on the real-time wind data from step S201, the system provides power dispatching strategy recommendations for the power station and corresponding power-limited / unlimited unit rankings for two scenarios: "dominant wind direction" and "non-dominant wind direction." This ensures that the power-limiting command fully considers the dynamic changes in the wake. The dominant wind direction is defined as the wind direction with a frequency percentage > 30% based on historical wind resource data from step S201. The non-dominant wind direction is the wind direction with a frequency percentage ≤ 30%.

[0100] It is worth noting that wind direction is a major factor affecting the degree of wake loss in wind farms. Current wind farm wind resource assessment standards define the dominant wind direction as the wind direction with the highest frequency within a given period, but do not provide a mandatory definition for the percentage threshold of frequency. In the industry, 30% is generally used as the standard. Therefore, in this invention, the specific description of the dominant wind direction is as follows: taking the inflow wind direction due north as 0°, divide the wind direction into 16 intervals clockwise according to 16 azimuth angles, i.e., each interval is 22.5°. Referring to historical wind resource data (such as wind rose diagrams), when a certain wind direction interval accounts for >30% of the annual wind direction frequency, that wind direction interval is defined as the dominant wind direction.

[0101] Based on the prevailing wind direction, the following active power dispatching strategies are recommended for wind turbines corresponding to different levels of wake influence: 1) Level I - Units without wake effects: It is recommended to prioritize dispatching to the maximum output state corresponding to the rated power curve (refer to the unit power curve in step S201) and maintain full-capacity operation until a grid load adjustment instruction is received.

[0102] 2) Level II - Mild wake impact units: It is recommended to schedule the units to 90%-95% of their theoretical maximum output to avoid exacerbating the wake impact on downstream units due to excessive power.

[0103] 3) Level III - Moderate wake impact units: It is recommended to schedule the unit to 75%-85% of its theoretical maximum output. It is also recommended to consider the real-time output of upstream units without wake impact. When the output of upstream units decreases by ≥10%, the power of this unit can be increased to the upper limit of the range, and vice versa.

[0104] 4) Level IV - Severe wake impact units: Adopt the "power limitation priority" strategy and it is recommended to schedule the units to 60%-70% of their theoretical maximum output. At the same time, it is recommended to refer to the S1 unit yaw load database to ensure that the equivalent fatigue load of the unit's key components is less than or equal to the design threshold, so as to avoid abnormal loads due to power limitation.

[0105] Step S204: Based on the non-dominant wind direction scenario corresponding to the wind condition data, formulate corresponding second active power dispatch strategy recommendations for wind turbines corresponding to each level of wake influence.

[0106] It should be noted that non-dominant wind direction scenarios refer to the operating scenarios corresponding to wind directions in wind farms whose frequency of occurrence does not reach a preset threshold.

[0107] The second active power dispatch strategy recommendation refers to a wind turbine active power dispatch guidance scheme formulated for non-dominant wind direction scenarios and adapted to the wake characteristics of such scenarios.

[0108] In this embodiment of the invention, based on the non-dominant wind direction scenario corresponding to the wind condition data, and combined with the characteristics of weak wake effect superposition and unstable influence range in this scenario, according to the characteristics of the units at each level of wake influence, a second active power dispatch strategy recommendation is formulated to adapt to the wake state under the non-dominant wind direction for wind turbine units of different levels.

[0109] Specifically, based on scenarios with non-dominant wind directions, the following active power dispatching strategies are recommended for wind turbines corresponding to different levels of wake influence: 1) Class I - Units without wake effects: It is recommended to maintain full-load operation. If the real-time wind speed is greater than the rated wind speed (refer to the S1 unit parameters), then operate at the rated power to avoid overload.

[0110] 2) Level II - Light / Level III - Moderate wake impact units: It is recommended to schedule to 85%-90% of the theoretical maximum output. Since the wake superposition under the non-dominant wind is relatively weak, the power can be appropriately increased.

[0111] 3) Level IV - Severe wake impact units: It is recommended to schedule to 70%-80% of the theoretical maximum output, which is 10% higher than the upper limit of the prevailing wind direction. At the same time, monitor wind direction changes. If the wind direction switches to the prevailing wind direction, it is necessary to adjust to the power limit range of the corresponding prevailing wind direction as soon as possible.

[0112] It is worth mentioning that during power plant operation and scheduling, a uniform average power curtailment method is usually adopted for all units in the event of power rationing. However, this invention provides power curtailment scheduling suggestions based on the degree of wake effect of the power plant group, reducing the impact of wake effect on the units in the event of power rationing. The core idea is that units not affected by wake should undertake the main power generation task, while units severely affected by wake should undertake less power generation task.

[0113] Step S205: Based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, formulate wake optimization control strategies for each wind turbine in power-limited mode and non-power-limited mode respectively.

[0114] Specifically, step S205 includes: Step S2051: Obtain power limitation and shutdown information for each wind turbine unit.

[0115] In this embodiment of the invention, the active power scheduling strategy and unit maintenance / outage status of the power station are read.

[0116] First, based on the active power dispatch strategy of the wind farm, the data type of Category I is labeled with the unit number and maximum power limit value of the power-limited operating unit. Second, based on the unit's maintenance / outage status, the data type of Category II is labeled with the out-of-service unit number. Finally, the two types of label data are merged and organized into a power-limited / outage status dataset, with the merged format being [unit number, power-limited status (0 / 1), start / stop status (0 / 1), power-limited value]. Here, 0 represents normal operation, and 1 represents power-limited / outage. The unit of the power-limited value can be set to MW or %.

[0117] For example, according to the scheduling strategy, the maximum power of a 2MW unit with the number #7 is limited to 1.5MW, which is represented as [7,1,0,1.5], and the shutdown of a 2MW unit with the number #8 is represented as [8,0,1,0].

[0118] Step S2052: Based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, determine whether each wind turbine is in a power limitation operation state.

[0119] It should be noted that the power-limited operation state refers to the working state in which a wind turbine is forced to reduce its output power when it has wind resources that allow it to generate full power, but is limited by external factors such as grid dispatch instructions, active power regulation of the wind farm, and insufficient absorption capacity.

[0120] In this embodiment of the invention, relevant information is input. This information includes wind conditions, wind turbine parameters, actual coordinates, and a power limitation / shutdown dataset. The power limitation / shutdown dataset is read to determine if power limitation operation exists. If power limitation operation exists, yaw optimization is performed according to step S2054; otherwise, optimization is performed according to step S2053. Detailed logical judgments are as follows: Figure 4 As shown.

[0121] Step S2053: If not, then the wind turbine parameters corresponding to the wind turbine in the shutdown state are corrected and the corresponding first wake optimization control strategy is generated.

[0122] It should be noted that the first wake optimization control strategy refers to a wake optimization control scheme specifically adapted to the unlimited power operation state of wind farms, which is different from the second wake optimization control strategy under the power-limited scenario.

[0123] In this embodiment of the invention, if there is an unlimited power operation, the yaw optimization method for the unlimited power mode is as follows: 1) Based on the power limitation / shutdown data set, if there is a unit shutdown, the corresponding thrust curve and power curve reading operation of the shut-down unit will be automatically set to zero, while the parameters of the other operating units will be read normally.

[0124] 2) An adaptive non-dominated sorting genetic algorithm (NSGA-II) is used to establish a yaw optimization program. Optimization objective mode 1 is to achieve the optimal output power of the power field group, and optimization objective mode 2 is to achieve the optimal output power of the power field group plus the minimum load on key components. The optimization variable is the yaw angle of each unit. The optimization variable is limited to the allowable yaw angle range of the unit in the reference input information, which is generally set to (-30°, 30°). The two optimization objectives in the unrestricted mode can be selected autonomously.

[0125] Specifically, the optimization objective of Mode 1 is to optimize the output power of the single-objective field group. The output power of the field group units can be obtained directly using the wake fast calculation method established in S201. Under the NSGA-II optimization algorithm framework, the combination of yaw angles that maximizes the output power under different inflow wind speeds and wind directions is found.

[0126] Specifically, the optimization objective of Mode 2 is multi-objective power optimization plus minimizing fatigue load on key components. The power calculation method is the same as in Mode 1. The fatigue load calculation method utilizes an equivalent fatigue load database generated by OpenFAST or FAST.Farm software to read fatigue damage data of key components under different yaw combinations, calculating the total fatigue damage of key components in the wind turbine cluster under different yaw angle combinations. The equivalent fatigue load database is in the format of [wind speed, yaw angle, key component code, equivalent fatigue damage load (DEL)], where key components include wind turbine blade roots, towers, etc. The DEL data calculation process is as follows: The DEL data calculation first uses simulation software to traverse different wind speeds and yaw angles of the turbine, extracting load time-series data. Rainflow counting is then used to statistically analyze the load time-series data, extracting load cycle characteristic parameters (amplitude, mean, and number of cycles). Combining the SN curves of corresponding components of the wind turbine (such as blade roots and tower) with a mean correction model (eliminating the influence of static loads to obtain equivalent alternating load amplitudes), the fatigue life of each cycle is calculated. Then, the total fatigue damage is obtained by accumulating the results using the linear damage accumulation principle (Miner's rule). Finally, based on the reference number of cycles corresponding to the design life of the wind turbine, the total fatigue damage is equivalently converted into a constant load amplitude, ultimately yielding the damage equivalent load (DEL_simulation) that quantifies the equivalent fatigue damage level.

[0127] Based on the yaw optimization results under the above-mentioned unrestricted power mode, the data is organized and represented as a yaw control matrix dataset of [wind speed, wind direction, unit number, yaw angle], forming an active wake control strategy and distributing it to each unit. Based on this method, the wake control strategy can be dynamically adjusted according to the real-time scheduling strategy of the station.

[0128] Step S2054: If yes, then the wind turbine parameters corresponding to the wind turbines in the power-limited operation state or shutdown state are corrected and the corresponding second wake optimization control strategy is generated.

[0129] It should be noted that the second wake optimization control strategy refers to a wake optimization control scheme specifically adapted to the limited power operation state of wind farms, with the core optimization objective being to minimize the fatigue load on key components of the unit.

[0130] In this embodiment of the invention, if a power-limited operation occurs, the yaw optimization method for the power-limited mode is as follows: 1) Based on the power limitation / shutdown dataset, for shut-down units, the corresponding thrust and power curves are automatically reset to zero during reading operations. Considering the performance differences of actual operating units, historical SCADA data from the wind farm is used to calibrate the power and thrust curves referenced during unit calculations. For units operating under power limitation, if the power corresponding to the incoming wind speed exceeds the power limitation value based on the original power curve, the power is automatically corrected to the power limitation limit. The thrust coefficient is also corrected based on the pitch angle change in power limitation scheduling to ensure the accuracy of wake calculation. For other units not operating under power limitation, parameters are read according to normal operation.

[0131] It is important to note that conventional one-time design wake yaw optimization calculations only read uniform unit power and thrust curve data, ignoring the changes in actual power curves caused by power-limited operation, which can lead to the failure of the original wake analysis and wake redirection methods. In this invention, differentiated treatment is applied to power-limited / shutdown conditions. The power and thrust curves of each unit are set as separate input files, allowing for timely correction based on the power-limited / shutdown dataset to ensure the accuracy of wake analysis.

[0132] 2) An adaptive non-dominated sorting genetic algorithm (NSGA-II) is used to establish a yaw optimization program. The optimization objective mode 3 is set to minimize the load on the key components of the power plant group. The optimization variable is also the yaw angle of each unit. Considering the power-limited scheduling requirements, the output power of the power plant group is added as a constraint.

[0133] Specifically, in Mode 3, the equivalent fatigue load database is used to read the fatigue damage data of key components under different yaw combinations, and the total fatigue damage of key components of the airfield group under different operating conditions and unit yaw angle combinations is calculated. At the same time, the wake fast calculation method is used to compare the output power of the airfield group units with the total power limit required by the power dispatch command. It is required that the output power after yaw optimization is less than 5% of the deviation from the dispatch command on the airfield side; otherwise, optimization is performed again.

[0134] It should be noted that setting the output power of the power plant group as a constraint is because under non-full-load wind speeds, some units may implement yaw actions based on load reduction, which may cause the output power of themselves or nearby upstream and downstream units to increase or decrease. Therefore, additional constraints need to be set.

[0135] Based on the above yaw optimization results, the data is organized and represented as a yaw control matrix dataset of [wind speed, wind direction, unit number, yaw angle], forming an active wake control strategy and distributing it to each unit.

[0136] This embodiment also provides an active wake control device suitable for power-limited operation of wind farms. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0137] This embodiment provides an active wake control device suitable for power-limited operation of wind farms, such as... Figure 5 As shown, it includes: The input module 301 is used to input the wind condition data and actual coordinate information of the wind turbine into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine. The rating module 302 is used to determine the degree of wake effect loss of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed. Module 303 is designed to formulate wake optimization control strategies for each wind turbine based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, respectively, in power-limited mode and non-power-limited mode.

[0138] In some alternative implementations, the input module 301 includes: The data acquisition unit is used to collect wind condition data, actual coordinate information, and wind turbine parameters. The input unit is used to input the wind condition data, actual coordinate information and wind turbine parameters of the wind turbine into the preset wake velocity loss model corresponding to the wake analysis model, and calculate the wake loss of all upstream units to downstream units. The calculation unit is used to input the wake loss corresponding to the downstream unit into the wake superposition model corresponding to the wake analysis model, and calculate the total wake velocity loss of the downstream unit after the wake effect of all upstream units is superimposed. The output unit is used to calculate the output power of wind farm units under zero yaw or yaw conditions based on the total wake velocity loss.

[0139] In some alternative implementations, the grading module 302 includes: The loss ratio unit is used to calculate the percentage of wake effect loss corresponding to the output power of each wind turbine by using the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed. The division unit is used to classify the degree of wake effect impact on each wind turbine by combining the percentage of wake effect loss corresponding to the output power of each wind turbine and wind condition data.

[0140] In some alternative implementations, the partitioning unit includes: The first-level sub-unit is used to determine the level of the wind turbine's wake effect when the percentage of wake effect loss corresponding to the output power of each wind turbine is less than the first loss threshold and the influence range of the prevailing wind direction corresponding to the wind condition data is in the upstream area. The second-level sub-unit is used to determine the wake effect level of the wind turbine as the second level when the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the first loss threshold, less than the second loss threshold, and the influence range of the main wind direction is stable. The third-level sub-unit is used to determine the wake effect level of the wind turbine as the third level when the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the second loss threshold, less than the third loss threshold, and the influence range of the main wind direction is affected by the wake superposition of a preset number of wind turbines upstream. The fourth-level sub-unit is used to determine the level of the wind turbine's wake effect when the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than the third loss threshold and the influence range of the main wind direction is affected by the wake superposition of the upstream wind turbine.

[0141] In some alternative embodiments, the device includes: The first formulation unit is used to formulate corresponding first active power dispatch strategy recommendations for wind turbines corresponding to each level of wake influence based on the prevailing wind direction scenario corresponding to the wind condition data. The second formulation unit is used to formulate corresponding second active power dispatch strategy recommendations for wind turbines corresponding to each level of wake influence based on the non-dominant wind direction scenario corresponding to the wind condition data.

[0142] In some alternative implementations, the designation module 303 includes: The acquisition unit is used to acquire power limit and shutdown information for each wind turbine unit; The judgment unit is used to determine whether each wind turbine is in a power-limited operation state based on the active power dispatch strategy suggestions corresponding to the degree of wake influence of each wind turbine and the power limitation and shutdown information of each wind turbine. The first optimization unit is used to correct the wind turbine parameters of the wind turbine corresponding to the shutdown state and generate the corresponding first wake optimization control strategy if no. The second optimization unit is used to modify the wind turbine parameters of wind turbines in the case of power-limited operation or shutdown states and generate the corresponding second wake optimization control strategy.

[0143] The active wake control device for wind farm power-limited operation provided in this embodiment of the invention can execute the active wake control method for wind farm power-limited operation provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0144] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0145] The following is a detailed reference. Figure 6 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0146] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0147] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the active wake control method for wind farm power-limited operation according to embodiments of the present invention.

[0148] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0149] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the active wake control method for wind farm power-limited operation shown in the above embodiments is implemented.

[0150] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0151] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An active wake control method suitable for power-limited operation of wind farms, characterized in that, The method includes: The wind condition data and actual coordinate information of the wind turbine are input into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine. Based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed, the degree of wake effect of each wind turbine is determined. Based on the active power dispatch strategy recommendations corresponding to the wake influence levels of each wind turbine and the power limiting and shutdown information of each wind turbine, wake optimization control strategies for each wind turbine are formulated according to power limiting mode and non-power limiting mode.

2. The method according to claim 1, characterized in that, The process of inputting wind condition data and actual coordinate information of the wind turbine into a preset wake analysis model to calculate wake velocity loss and obtain the output power of the wind farm turbine includes: Collect wind condition data, actual coordinate information, and wind turbine parameters; The wind condition data, actual coordinate information and wind turbine parameters of the wind turbine are input into the wake velocity loss model corresponding to the preset wake analysis model, and the wake loss generated by all upstream units to the downstream unit is calculated. Input the wake loss corresponding to the downstream unit into the wake superposition model corresponding to the wake analysis model, and calculate the total wake velocity loss of the downstream unit after the superposition of the wake effects of all upstream units. Based on the total wake velocity loss, calculate the output power of the wind farm units under zero yaw conditions or yaw conditions.

3. The method according to claim 1, characterized in that, The determination of the wake effect level of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed includes: Using the wind turbine output power corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed, calculate the percentage of wake effect loss corresponding to the output power of each wind turbine. Based on the percentage of wake effect loss corresponding to the output power of each wind turbine and the wind condition data, the degree of wake effect impact on each wind turbine is classified.

4. The method according to claim 3, characterized in that, The percentage of wake effect loss corresponding to the output power of each wind turbine and the wind condition data are used to classify the degree of wake effect impact on each wind turbine, including: When the percentage of wake effect loss corresponding to the output power of each wind turbine is less than the first loss threshold and the influence range of the prevailing wind direction corresponding to the wind condition data is in the upstream area, the wake effect level of the wind turbine is determined to be the first level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the first loss threshold and less than the second loss threshold, and the influence range of the prevailing wind direction is stable, the wake effect level of the wind turbine is determined to be the second level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than or equal to the second loss threshold and less than the third loss threshold, and the influence range of the main wind direction is affected by the wake superposition of a preset number of upstream wind turbines, the wake effect level of the wind turbine is determined to be the third level. When the percentage of wake effect loss corresponding to the output power of each wind turbine is greater than the third loss threshold and the influence range of the main wind direction is affected by the wake superposition of the upstream wind turbine, the wake effect level of the wind turbine is determined to be the fourth level.

5. The method according to claim 1, characterized in that, The method further includes: Based on the prevailing wind direction scenario corresponding to the wind condition data, a first active power dispatch strategy recommendation is formulated for each wind turbine corresponding to the level of wake influence. Based on the non-dominant wind direction scenario corresponding to the wind condition data, a second active power dispatch strategy suggestion is formulated for each wind turbine corresponding to the level of wake influence.

6. The method according to claim 1, characterized in that, The active power dispatching strategy recommendations based on the wake influence levels of each wind turbine and the power limiting and shutdown information of each wind turbine are used to formulate wake optimization control strategies for each wind turbine in both power-limited and non-power-limited modes, including: Obtain the power limitation and shutdown information of each wind turbine unit; Based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine, determine whether each wind turbine is in a power limitation operation state. If not, the parameters of the wind turbines corresponding to the wind turbines in the shutdown state are corrected and the corresponding first wake optimization control strategy is generated. If so, the wind turbine parameters of the wind turbines in the limited power operation state or shutdown state are corrected and a corresponding second wake optimization control strategy is generated.

7. An active wake control device suitable for power-limited operation of wind farms, characterized in that, The device includes: The input module is used to input the wind condition data and actual coordinate information of the wind turbine into the preset wake analysis model to calculate the wake velocity loss and obtain the output power of the wind turbine. The rating module is used to determine the degree of wake effect impact of each wind turbine based on the percentage of wake effect loss between the output power of the wind turbine corresponding to each wind condition data and the theoretical power corresponding to the rated wind speed. A formulation module is used to formulate wake optimization control strategies for each wind turbine in both power-limited and non-power-limited modes, based on the active power dispatch strategy recommendations corresponding to the wake influence level of each wind turbine and the power limitation and shutdown information of each wind turbine.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the active wake control method applicable to power-limited operation of a wind farm, as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the active wake control method applicable to power-limited operation of a wind farm, as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the active wake control method applicable to power-limited operation of a wind farm, as described in any one of claims 1 to 6.

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