A wind farm controllable wake identification method and system based on operation data

Abstract

The application discloses a wind farm controllable wake identification method and system based on operation data, and the method comprises the following steps: obtaining basic parameters of a wind farm and units in the wind farm and establishing a neighboring unit set of each unit; screening out units in a normal working state and calculating a unit theoretical wake action area of the units in the normal working state, screening out, in the neighboring unit set, units falling within the theoretical wake action area and closest to the units to form a theoretical wake action unit pair; extracting current wind direction data recorded by the two units in the theoretical wake action unit pair to establish an effective wake unit set; and obtaining power of all units in the effective wake unit set, screening out the effective wake unit set with all downstream unit power being less than that of the most upstream unit to form a controllable wake unit set. Through the application, controllable wake identification can be efficiently and reliably realized, and support can be provided for landing of wind farm cooperative control technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation, in particular to a wind farm controllable wake identification method and system based on operation data. BACKGROUND

[0002] In the field of wind power generation, the wake effect of wind turbines can reduce the power generation of the wind farm, and further affect the investment returns of the entire wind farm. At present, there are various methods to reduce the influence of wake in the wind farm. In the operation stage of the wind farm, a cooperative control strategy is mainly used to try to avoid the loss of power generation caused by the wake effect. Before the implementation of the control strategy, it is necessary to solve the key technical problem of how to identify and determine the wake that meets the implementation conditions of the control strategy in an operating wind farm.

[0003] The existing wake identification technology mainly includes two kinds. One is a direct measurement technology represented by a laser radar, which observes the influence range of the wake of the unit through advanced equipment, so as to perform cooperative control. The other is a model calculation technology based on wind measurement equipment, which takes the wind direction measured by the wind measurement tower or some units in the field as the representative wind direction of the whole field, and calculates the wake range in the field by combining the wake model. The direct measurement technology represented by the laser radar uses expensive measurement equipment, which is commonly used in theoretical verification work and is difficult to be widely applied to actual engineering projects aimed at improving overall returns. The model calculation technology based on wind measurement equipment is relatively simple, but this method has many assumptions, and the uniform inflow assumption does not conform to the actual operating conditions of most wind farms, which can easily lead to redundant or even incorrect cooperative control actions in actual application, resulting in resource waste. SUMMARY

[0004] Therefore, based on the evolution and development law of the wake of the wind turbine, and in combination with the real-time operation data of the wind farm, and in consideration of the operating state of the unit and the non-uniformity of the wind farm, the present application provides a wind farm controllable wake identification method and system based on operation data, which can effectively identify the units affected by the wake and solve the problems existing in the prior art.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a wind farm controllable wake identification method based on operation data, comprising:

[0007] Obtaining basic parameters of the wind farm and units in the field, and establishing a set of adjacent units for each unit based on the basic parameters;

[0008] Obtaining actual operation data of the wind farm, and screening out units in normal working state;

[0009] For the units in normal working state, calculate the theoretical wake action area of the unit, and screen out the units in its neighboring unit set which fall within its theoretical wake action area and are closest to it, and form a theoretical wake action unit pair with it;

[0010] Extract the current wind direction data recorded by the two units in the theoretical wake action unit pair, and if the difference between the wind direction data of the two units is less than or equal to the preset threshold, determine that the two units are an effective wake unit pair, and establish an effective wake unit set;

[0011] Get the power of all units in the effective wake unit set, screen out the effective wake unit set whose all downstream units have power less than the most upstream unit, and form a controllable wake unit set.

[0012] Further, the basic parameters include: the number, layout coordinates, and impeller diameter, standard power curve of each unit in the wind farm.

[0013] Further, take all units within a distance less than N times the impeller diameter from each unit as the center to establish the neighboring unit set of the unit, where N is determined according to the actual environment of each wind farm.

[0014] Further, the process of obtaining the actual operation data of the wind farm and screening out the units in normal working state includes:

[0015] Get the wind speed, wind direction and power data of each unit at the current time from the SCADA system of the wind farm, and for each unit, according to its wind speed and power data, combined with the standard power curve, determine whether it is in normal working state.

[0016] Further, the criterion for determining whether it is in normal working state according to its wind speed and power data combined with the standard power curve is:

[0017] At the same wind speed, if the power data P SCADA and the power data P Sta satisfy the following formula, it is determined that the unit is in normal working state at this time:

[0018]

[0019] Wherein, the deviation δ is an empirical parameter, which is selected according to the actual discrimination situation.

[0020] Further, the process of calculating the theoretical wake action area of the unit for the units in normal working state includes:

[0021] Under the measured wind direction of the unit, the theoretical wake action area of the unit is calculated by the following formula:

[0022] D w =2k w x+D

[0023] wherein x is the distance between the downstream of the current wind direction and the unit, D is the wake action diameter at x, D is the impeller diameter, and k is the wake diffusion coefficient. w w

[0024] Further, the process of establishing the effective wake unit set comprises:

[0025] The effective wake unit set is formed by integrating all the effective wake unit pairs and integrating into one set if there is the same unit in two effective wake unit pairs.

[0026] In a second aspect, an embodiment of the present application provides a wind farm controllable wake identification system based on operation data, comprising:

[0027] A neighboring unit set establishing module is configured to acquire basic parameters of a wind farm and units in the wind farm, and establish a neighboring unit set of each unit based on the basic parameters.

[0028] A normal working unit acquiring module is configured to acquire actual operation data of the wind farm, and filter out units in a normal working state.

[0029] A theoretical wake action unit pair acquiring module is configured to calculate a theoretical wake action region of a unit in a normal working state, and filter out a unit closest to the unit and falling in the theoretical wake action region of the unit from the neighboring unit set of the unit, to form a theoretical wake action unit pair with the unit.

[0030] An effective wake unit pair acquiring module is configured to extract current wind direction data recorded by two units in the theoretical wake action unit pair, and determine that the two units are an effective wake unit pair if a difference between the wind direction data of the two units is less than or equal to a preset threshold, and establish an effective wake unit set.

[0031] A controllable wake unit set acquiring module is configured to acquire power of all units in the effective wake unit set, filter out the effective wake unit set in which power of all downstream units is less than power of an uppermost upstream unit, and form a controllable wake unit set.

[0032] In a third aspect, an embodiment of the present application provides a computer device, comprising at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the wind farm controllable wake identification method based on operation data according to any one of the embodiments of the first aspect of the present application.​​

[0033] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing computer instructions for causing a computer to execute the wind farm controllable wake identification method based on operation data according to any of the first aspect of the embodiments of the present application.

[0034] The technical scheme of the present application has the following advantages:

[0035] The wind farm controllable wake identification method and system based on operation data provided by the present application can achieve the purpose of identifying the location of controllable wake effect with the potential of coordinated control in the wind farm in real time based on the concept of wind turbine wake and real-time operation data of the wind farm, considering the differentiated operation state of the wind turbine and the non-uniformity of the flow field in the field. Compared with the existing wake direct measurement technology based on laser radar and wake model calculation technology based on wind measurement equipment, the present application is not only significantly reduced in cost but also more reliable, and can be more simply applied to actual wind farms, providing support for the landing of wind farm coordinated control technology. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical scheme in the specific embodiments or prior art of the present application, the drawings needed in the description of the specific embodiments or prior art will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0037] Figure 1 A flow chart of a specific example of the wind farm controllable wake identification method based on operation data provided in the embodiments of the present application;

[0038] Figure 2 A layout diagram of a wind farm in the embodiments of the present application;

[0039] Figure 3 A data distribution diagram of normal working state and abnormal working state in a time range in the embodiments of the present application;

[0040] Figure 4 A diagram of a theoretical wake effect area that can be drawn in a wind farm in the embodiments of the present application;

[0041] Figure 5 A diagram of an effective wake effect area that can be drawn in a wind farm using the embodiments of the present application;

[0042] Figure 6 A diagram of a controllable wake effect area that can be drawn in a wind farm provided by the embodiments of the present application;

[0043] Figure 7 This is a block diagram illustrating an example of a wind farm controllable wake identification system based on operational data provided in an embodiment of the present invention.

[0044] Figure 8 This is a composition diagram of a specific example of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0046] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] Example 1

[0048] This invention provides a method for identifying controllable wake in wind farms based on operational data, such as... Figure 1 As shown, the method includes the following steps:

[0049] Step S1: Obtain the basic parameters of the wind farm and the turbines within the farm, and establish a set of neighboring turbines for each turbine based on the basic parameters.

[0050] In this embodiment of the invention, basic parameters of the wind farm and its turbines are obtained from a wind farm database. Specifically, these include: the turbine number, layout coordinates, rotor diameter, and standard power curve of each turbine within the wind farm. Figure 2 The diagram shows the layout of the wind farm in this embodiment. For each turbine, a set of neighboring turbines is established by taking all turbines whose distance from it is less than N times the rotor diameter. N is determined according to the actual environment of each wind farm. Usually, N>15. In this embodiment, N is 20.

[0051] Step S2: Obtain actual operating data of the wind farm and filter out the units that are in normal operating condition.

[0052] In this embodiment of the invention, the current wind speed, wind direction, and power data of each turbine are obtained from the wind farm's SCADA system. For each turbine, its wind speed and power data are used in conjunction with a standard power curve to determine whether it is in normal operating condition. The specific criteria are as follows:

[0053] At the same wind speed, if the power data P recorded by the SCADA system SCADAPower data P recorded by the SCADA system Sta If the following formula is met, it is determined that the unit is in a normal operation state at this moment:

[0054]

[0055] If the power data P recorded by the SCADA system SCADA Power data P obtained according to a standard power curve Sta If the following formula is met, it is determined that the unit is in an abnormal operation state at this moment:

[0056]

[0057] In the formula, the deviation δ is an empirical parameter, which is selected according to actual discrimination conditions.

[0058] According to the above determination standard, the unit in a normal operation state at the current moment is screened out. As shown in the following table, it is a data distribution diagram of the normal operation state and the abnormal operation state of a unit in a certain embodiment according to the above determination standard within a time range. Figure 3

[0059] Step S3: For the unit in a normal operation state, the theoretical wake action area of the unit is calculated, and the unit closest to the unit and falling within the theoretical wake action area of the unit is screened out from the adjacent unit set, so as to form a theoretical wake action unit pair with the unit.

[0060] For the unit in a normal operation state, the following formula is used to calculate the theoretical wake action area of the unit under the actual measured wind direction of the unit:

[0061] D w = 2k w x + D

[0062] In the formula, x is the distance between the unit downstream and the unit under the current wind direction, D w is the wake action diameter at x, D is the impeller diameter, k w is the wake diffusion coefficient, which is an empirical parameter, generally 0.075 for onshore wind farms and 0.05 for offshore wind farms.

[0063] Step S4: The current wind direction data recorded by the two units in the theoretical wake action unit pair is extracted, and if the difference between the wind direction data of the two units is less than or equal to a preset threshold, the two units are determined to be an effective wake unit pair, and an effective wake unit set is established.

[0064] ​Extract the current wind direction data d1 and d2 recorded by the two units in the theoretical wake interaction unit pair, if |d1-d2|≤M, the two units are effective wake unit pairs, wherein M is determined according to the actual situation, generally M is less than 22.5 degrees.

[0065] In this embodiment, all effective wake unit pairs are summarized, if there is the same unit in two effective wake unit pairs, it is integrated into a set to form an effective wake unit set. Taking the data recorded by the wind farm in this embodiment at a certain time as an example, the effective wake interaction area in the wind farm at this stage can be drawn, as shown in the figure. Figure 5

[0066] Step S6: Obtain the power of all units in the effective wake unit set, and select the effective wake unit set in which the power of all downstream units is less than the power of the most upstream unit to form a controllable wake unit set.

[0067] Taking the data recorded by the wind farm in this embodiment at a certain time as an example, the controllable wake interaction area in the wind farm at this stage can be drawn, as shown in the figure. Figure 6 According to the above steps, the controllable wake affected unit set in the wind farm at each time can be determined in real time, and then the cooperative control strategy can be implemented in the unit set to reduce the power generation loss caused by the wake effect. Figure 6 The controllable wake position of the wind farm at the current time obtained by the method provided by the embodiment of the present application is shown in the figure, and the related cooperative control strategy should also be carried out in this set, thereby reducing the power generation loss caused by the wake effect.

[0068] Embodiment 2

[0069] The embodiment of the present application provides a wind farm controllable wake identification system based on operation data, as shown in the figure, which comprises: Figure 7

[0070] The adjacent unit set establishment module 1 is used to obtain the basic parameters of the wind farm and the units in the wind farm, and establish the adjacent unit set of each unit based on the basic parameters; this module executes the method described in step S1 in embodiment 1, which will not be repeated here.

[0071] The normal working unit acquisition module 2 is used to obtain the actual operation data of the wind farm, and select the units in the normal working state; this module executes the method described in step S2 in embodiment 1, which will not be repeated here.

[0072] ​​The theoretical wake effect unit pair acquisition module 3 is used to calculate the theoretical wake effect area of ​​a unit in normal operation, and select the unit that falls within its theoretical wake effect area and is closest to it from the set of neighboring units to form a theoretical wake effect unit pair with it; this module performs the method described in step S3 of embodiment 1, which will not be repeated here.

[0073] The effective wake generator set acquisition module 4 is used to extract the current wind direction data recorded by the two generators in the theoretical wake generator pair. If the difference between the wind direction data of the two generators is less than or equal to a preset threshold, the two generators are determined to be an effective wake generator pair, and an effective wake generator set is established. This module executes the method described in step S4 of embodiment 1, which will not be repeated here.

[0074] The controllable wake turbine set acquisition module 5 is used to acquire the power of all turbines in the effective wake turbine set, and filter out the effective wake turbine set where the power of all downstream turbines is less than the power of the upstream turbine, thus forming a controllable wake turbine set. This module executes the method described in step S5 of embodiment 1, which will not be repeated here.

[0075] Example 3

[0076] This invention provides a computer device, such as... Figure 8 As shown, the system includes: at least one processor 501, such as a CPU (Central Processing Unit), at least one communication interface 503, a memory 504, and at least one communication bus 502. The communication bus 502 is used to enable communication between these components. The communication interface 503 may include a display screen or a keyboard; optionally, the communication interface 503 may also include a standard wired interface or a wireless interface. The memory 504 may be a high-speed RAM (Random Access Memory) or a non-volatile memory, such as at least one disk storage device. Optionally, the memory 504 may also be at least one storage device located remotely from the processor 501. The processor 501 can execute the wind farm controllable wake identification method based on operational data according to Embodiment 1. The memory 504 stores a set of program code, and the processor 501 calls the program code stored in the memory 504 to execute the wind farm controllable wake identification method based on operational data according to Embodiment 1.

[0077] The communication bus 502 can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, or the like. The communication bus 502 can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 8 Only one line is used to represent the communication bus 502, but this does not mean that there is only one bus or only one type of bus.

[0078] The memory 504 can include volatile memory, such as random-access memory (RAM), and non-volatile memory, such as flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The memory 504 can also include a combination of the above-mentioned types of memory.

[0079] The processor 501 can be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP.

[0080] The processor 501 can further include a hardware chip. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0081] Optionally, the memory 504 is further configured to store program instructions. The processor 501 can invoke the program instructions to implement the wind farm controllable wake identification method based on operation data as described in Embodiment 1.

[0082] The embodiment of the present application also provides a computer readable storage medium, and computer executable instructions are stored on the computer readable storage medium. The computer executable instructions can execute the wind farm controllable wake identification method based on operation data in the embodiment 1. The storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0083] Obviously, the above embodiment is only an example for clearly illustrating, but not a limitation to the embodiments. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, all the embodiments do not need to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A wind farm controllable wake identification method based on operational data, characterized in that, The method comprises the following steps: acquiring basic parameters of a wind farm and wind turbines in the wind farm, and establishing a set of neighboring wind turbines for each wind turbine based on the basic parameters; acquiring actual operation data of the wind farm, and screening wind turbines in a normal working state, including: acquiring wind speed, wind direction and power data of each wind turbine at a current time from a SCADA system of the wind farm, and determining whether each wind turbine is in a normal working state according to wind speed and power data of the wind turbine in combination with a standard power curve; wherein the determination standard is that the wind turbine is in a normal working state according to wind speed and power data of the wind turbine in combination with a standard power curve; At the same wind speed, if the power data recorded by the SCADA system Power data obtained from standard power curves If the following formula is satisfied, the unit is determined to be in normal operating condition at that moment: In the formula, the deviation is an empirical parameter, which is selected according to the actual discrimination condition; calculating a theoretical wake action area of a wind turbine in a normal working state, and screening a wind turbine in the set of neighboring wind turbines that falls within the theoretical wake action area and is closest to the wind turbine, to form a theoretical wake action pair with the wind turbine; wherein the process of calculating the theoretical wake action area of the wind turbine in a normal working state comprises: calculating the theoretical wake action area of the wind turbine in a normal working state under a measured wind direction of the wind turbine by using the following formula: wherein x is the distance between the downstream of the current wind direction and the turbine, D w is the wake action diameter at x , D is the diameter of the impeller, k w is the wake diffusion coefficient; extracting current wind direction data recorded by the two wind turbines in the theoretical wake action pair, and determining that the two wind turbines are an effective wake pair if a difference between the wind direction data of the two wind turbines is less than or equal to a preset threshold, and establishing an effective wake set; acquiring power of all wind turbines in the effective wake set, screening an effective wake set in which all downstream wind turbines have power less than that of an upstream wind turbine, and forming a controllable wake set.

2. The wind farm controllable wake identification method based on operational data according to claim 1, characterized in that, The basic parameters include: numbers, layout coordinates, impeller diameters and standard power curves of the wind turbines in the wind farm.

3. The wind farm controllable wake identification method based on operational data according to claim 2, characterized in that, Taking all wind turbines within a distance less than N times of the impeller diameter of each wind turbine as a center, a set of neighboring wind turbines of the wind turbine is established, wherein N is determined according to an actual environment of each wind farm.

4. The wind farm controllable wake identification method based on operational data according to claim 1, characterized in that, The process of establishing the effective wake set comprises: integrating all effective wake pairs into an effective wake set if the same wind turbine exists in two effective wake pairs.

5. A wind farm controllable wake identification system based on operational data, characterized in that, The method comprises the following steps: a neighboring wind turbine set establishing module, configured to acquire basic parameters of a wind farm and wind turbines in the wind farm, and establish a set of neighboring wind turbines for each wind turbine based on the basic parameters; a normal working wind turbine acquiring module, configured to acquire actual operation data of the wind farm, and screen wind turbines in a normal working state, including: acquiring wind speed, wind direction and power data of each wind turbine at a current time from a SCADA system of the wind farm, and determining whether each wind turbine is in a normal working state according to wind speed and power data of the wind turbine in combination with a standard power curve; wherein the determination standard is that the wind turbine is in a normal working state according to wind speed and power data of the wind turbine in combination with a standard power curve; At the same wind speed, if the power data recorded by the SCADA system Power data obtained from standard power curves If the following formula is satisfied, the unit is determined to be in normal operating condition at that moment: In the formula, the deviation is an empirical parameter, which is selected according to the actual discrimination condition; a theoretical wake action pair acquiring module, configured to calculate a theoretical wake action area of a wind turbine in a normal working state, and screen a wind turbine in the set of neighboring wind turbines that falls within the theoretical wake action area and is closest to the wind turbine, to form a theoretical wake action pair with the wind turbine; wherein the process of calculating the theoretical wake action area of the wind turbine in a normal working state comprises: Under the measured wind direction of the machine group, the following formula is used to calculate the theoretical wake action area of the machine group: wherein x is the distance between the downstream of the current wind direction and the turbine, D w is the wake action diameter at x , D is the diameter of the impeller, k w is the wake diffusion coefficient; The effective wake machine group combination acquisition module is configured to extract current wind direction data recorded by two machine groups in the theoretical wake action machine group combination, and determine that the two machine groups are an effective wake machine group combination if the difference between the wind direction data of the two machine groups is less than or equal to a preset threshold, and establish an effective wake machine group set. The controllable wake machine group set acquisition module is configured to acquire power of all machine groups in the effective wake machine group set, filter out the effective wake machine group set in which all downstream machine groups have power less than that of the most upstream machine group, and form a controllable wake machine group set.

6. A computer device, comprising: The method comprises the following steps: at least one processor, and a memory connected in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the wind farm controllable wake identification method based on operation data according to any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores computer instructions for causing the computer to perform the wind farm controllable wake identification method based on operation data according to any one of claims 1-4.

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