A wind farm cluster control system

Abstract

The wind farm group control system provided by the present disclosure comprises a data acquisition module, a data management module, an optimization calculation module and an evaluation optimization module, the data acquisition module is used for grouping wind farms and acquiring corresponding data of the wind farms through various different systems, the data management module is used for classifying and storing the data of the wind farms into corresponding databases and managing various different databases, the optimization calculation module is used for obtaining control parameters of each group in the wind farm by using a wind farm group control model, and the evaluation optimization module is used for output evaluation of the wind farm and analysis of the control parameters of each wind turbine group in the wind farm, and each wind turbine group is controlled by using the obtained analysis result. Thus, each group of wind turbine groups in the wind farm can be uniformly controlled according to the maximum output power of the wind farm group, thereby realizing intelligent control of the wind farm group.

Description

Technical Field

[0001] This application relates to the field of wind power generation, and more particularly to a wind farm cluster control system. Background Technology

[0002] In existing technologies, each wind farm comprises dozens or hundreds of wind turbine generators, and the models of the generators within a single wind farm can be the same or different. To improve the safety and stability of the power grid and the rationality of adjusting the output of the wind turbine generators, a wind farm cluster control system is needed to uniformly regulate each wind turbine generator to meet the power requirements of the wind farm. Summary of the Invention

[0003] This application provides a wind farm cluster control system for unified regulation of each wind turbine to meet the power requirements of the wind farm.

[0004] The first aspect of this application proposes a wind farm cluster control system, which includes a data acquisition module, a data management module, an optimization calculation module, and an evaluation and optimization module.

[0005] The data acquisition module is used to group the wind farms and acquire data corresponding to the wind farms through various different systems;

[0006] The data management module is used to classify and store the data of the wind farm into corresponding databases, and to manage multiple different databases.

[0007] The optimization calculation module is used to construct the optimization objectives and constraints for the wind farm-level control and to predict the wind farm power.

[0008] The evaluation and optimization module is used to evaluate the output of the wind farm and analyze the control parameters of each wind turbine in the wind farm, and use the analysis results to control each wind turbine.

[0009] Optionally, the various systems include a distributed edge computing processing system, a wind direction prediction system, a SCADA system, and a life prediction control system;

[0010] The distributed edge computing processing system is used to acquire real-time data from each wind turbine;

[0011] The wind direction prediction system is used to acquire predicted data on wind speed and wind direction;

[0012] The SCADA system is used to obtain the total power generation.

[0013] The life prediction and control system is used to obtain fatigue load status.

[0014] Optionally, the real-time data includes actual power generation, yaw angle, and turbine status.

[0015] Optionally, the various databases include: a wind turbine status database, a wake database, and a wind turbine parameter database.

[0016] Optionally, the optimization calculation module is used to construct the wind farm power prediction model and use the wind farm power prediction model to predict the power of the wind farm.

[0017] Optionally, the system uses the Modbus / Pderofibus communication protocol for communication.

[0018] The technical solutions provided by the embodiments of this application have at least the following beneficial effects:

[0019] The wind farm cluster control system disclosed herein includes a data acquisition module, a data management module, an optimization calculation module, and an evaluation and optimization module. The data acquisition module groups the wind farms and acquires corresponding data from various systems. The data management module categorizes and stores the wind farm data in corresponding databases and manages these databases. The optimization calculation module uses a wind farm cluster control model to obtain the control parameters for each group within the wind farm. The evaluation and optimization module evaluates the wind farm's output and analyzes the control parameters of each wind turbine within the wind farm, using the analysis results to control each wind turbine. Therefore, the wind farm cluster control system proposed in this application can achieve group management of wind farm clusters, obtain the control parameters for each group using a wind farm cluster control model, and control each group of wind turbines. This allows the wind farm cluster control system to uniformly control each group of wind turbines based on the maximum output power of the wind farm cluster, thereby achieving intelligent control of the wind farm cluster.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a schematic diagram of the structure of a wind farm cluster control system according to an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The wind farm cluster control system of this application embodiment is described below with reference to the accompanying drawings.

[0025] Example 1

[0026] Figure 1 This is a schematic diagram of the structure of a wind farm group control system according to an embodiment of this application, as shown below. Figure 1 As shown, it may include:

[0027] The aforementioned wind farm cluster control system includes a data acquisition module, a data management module, an optimization calculation module, and an evaluation and optimization module.

[0028] The data acquisition module is used to group the wind farms and acquire data corresponding to the wind farms through various systems; the data management module is used to classify and store the wind farm data into corresponding databases and manage various databases; the optimization calculation module is used to construct the optimization objectives and constraints for wind farm-level control and predict the wind farm power; the evaluation and optimization module is used to evaluate the output of the wind farm, analyze the control parameters of each wind turbine in the wind farm, and use the analysis results to control each wind turbine.

[0029] Specifically, in some embodiments, the data acquisition module can group wind farms according to wind direction, turbine layout, and turbine status, and determine the flagship turbine group, with the remaining turbine groups being non-flagship groups. The flagship wind turbine group has a higher priority than the non-flagship wind turbine groups, thus allowing for unified control of all wind turbine groups within the wind farm by utilizing the characteristics of each turbine group, thereby ensuring the wind farm's power generation capacity.

[0030] In some embodiments, the various systems may include a distributed edge computing processing system, a wind direction prediction system, a SCADA system, and a life prediction control system.

[0031] In some embodiments, the distributed edge computing processing system is used to acquire real-time data of each wind turbine; the wind direction prediction system is used to acquire predicted data of wind speed and wind direction; the SCADA system is used to acquire total power generation; and the life prediction control system is used to acquire fatigue load status.

[0032] Specifically, in some embodiments, the aforementioned real-time data may include actual power generation, yaw angle, and turbine status.

[0033] In some embodiments, the various databases mentioned above may include a wind turbine status database, a wake database, and a wind turbine parameter database.

[0034] In some embodiments, the aforementioned optimization calculation module can utilize a wind farm group control model to obtain the control parameters for each group within the wind farm. The wind farm group control model uses the maximum power generation of the entire wind farm as the control objective to obtain the control parameters for each group.

[0035] Specifically, in some embodiments, the method for obtaining a wind farm power prediction model may include the following steps:

[0036] Step 101: Construct a dynamic model of a single wind turbine unit.

[0037] Specifically, in some embodiments, it is assumed that there is a disc impeller with an infinite number of blades, the airflow flowing through the impeller is uniformly distributed and strictly perpendicular to the impeller blades, and the frictional and flow losses of the air are ignored. When the airflow with a wind speed of V passes through a fan with a swept area of ​​A, let the power carried by the airflow be... The power obtained by the wind turbine is

[0038]

[0039]

[0040] in, air density, Wind energy utilization coefficient (wind turbine peak speed) and pitch angle (function) This represents the actual active power output of the wind turbine, where k is the energy loss coefficient considering both mechanical and electrical losses in the wind turbine unit. If k > 1, the power carried by the airflow after passing through the rotor is reduced to P. b According to the law of conservation of energy

[0041]

[0042] The back wind speed of the fan can be obtained from the reduced airflow power.

[0043]

[0044] The back wind speed of the fan is a nonlinear function of the fan's captured wind speed and the fan's output power.

[0045] Step 102: Construct a model of the wind turbine wake effect.

[0046] Specifically, in some embodiments, the wake effect of wind turbines reduces the total energy captured by the wind farm. In a group of wind turbines arranged in the same wind direction, the upwind turbines, after capturing wind energy from the airflow, experience a reduction in the wind power carried by the airflow, leading to a decrease in wind speed near the downwind turbines and consequently a reduction in their output. The wake effect is related to the spacing between the turbines; the smaller the spacing, the greater the impact of the preceding turbine on the following turbines, resulting in greater energy loss for the downstream turbines. The wake effect of a wind turbine on its downstream side can be represented by a frustum of a circle with the rotor shaft as its axis and the swept area as its top surface. Let the rotor radius of the wind turbine be... The relationship between the radius of influence r of the wake and the distance y can be expressed as:

[0047]

[0048] in, For shape coefficients (for natural wind), =0.04, otherwise =0.08).

[0049] Furthermore, the wind speed loss caused by the wake effect of multiple units mutually blocking each other can be calculated using a deep learning Mosaic model, and the momentum of each overlapping wake remains unchanged in the downwind direction. Let K be the set of units that cause wake effects on unit y. The wake influence area of ​​these K units at y can be decomposed into a set of grids w(k), where the area of ​​each grid is the independent wake area of ​​unit k at y. The wake factor is Then the wake at point y of the unit satisfies the equation:

[0050]

[0051] in, The thrust of unit i is and the wind speed of unit i is . Paddle pitch angle Wind turbine speed nonlinear functions, Thrust coefficient, wake factor Wind speed attenuation coefficient ,in Given the back wind speed of unit k, calculate the ensemble correlation wake factor of the wake effect on unit y:

[0052]

[0053] From the relationship between wake factor and wind speed attenuation, we can obtain

[0054]

[0055] The total wind speed attenuation coefficient of unit y due to the wake effect is:

[0056]

[0057] in, Let y be the swept area of ​​unit y; Let be the intersection of the wake area of ​​unit k and the rotor area of ​​unit y, which is the influence area of ​​the wake of each unit k at y within the rotor range of unit y.

[0058]

[0059] Therefore, the total wind speed attenuation coefficient due to the wake effect of the upstream wind turbine is...

[0060]

[0061] Finally, the capture wind speed of unit y was calculated to be:

[0062]

[0063] Step 103: Construct a time delay effect model between wind turbine units.

[0064] In some embodiments, the geographical distribution of wind farms is relatively wide, and the wind speed and wake effects take a certain amount of time to be transmitted from the upwind turbines to the downwind turbines. This can be approximated as a straight line with the wind speed direction and the transmission speed being the upstream wind speed of the wind farm, ignoring losses. The wind speed time delay at turbine y, Ty = y / v, and the wake effect time delay of turbine k on turbine y, Tky = Lky / v, can be calculated, where Lky is the downwind distance between turbines k and y. After accounting for the time delays, the captured wind speed of turbine y at time t can be obtained.

[0065]

[0066] Step 104: Construct a wind farm cluster control model.

[0067] The wind farm group control model described above uses a cosine trigonometric function model to simulate the radial velocity distribution and a sinine trigonometric function model to simulate the axial velocity distribution. The model is as follows:

[0068]

[0069] Furthermore, if the wake radii of the two models are the same, then the relationship between the trigonometric function period of the model and the wake radius is as follows:

[0070] ,

[0071] When the radial distance r extends to the wake radius At that time, the wind speed returned to the incoming wind speed. ,

[0072] .

[0073] Where the areas contained in the velocity type are equal, then:

[0074]

[0075] in, The velocity magnitude is obtained by using a turbulence model, and thus we can obtain...

[0076] A= - ;

[0077] T= / ;

[0078] B= -

[0079] K= /

[0080] C=

[0081] Based on the above, the prediction step can be obtained as follows:

[0082]

[0083] Corrective steps:

[0084] .

[0085] In some embodiments, the above-described optimization calculation module is also used to predict the power of the wind farm.

[0086] Specifically, in some embodiments, an objective function and constraints for the overall power control optimization problem are constructed, and the overall power is predicted. Furthermore, to reduce computational load and to improve the feasibility and accuracy of the optimization solution, the aforementioned optimization calculation module can employ intelligent optimization algorithms to solve the objective function.

[0087] In some embodiments, the evaluation and optimization module can also evaluate the wind field treatment after the wind field group is controlled, and perform sensitivity analysis on the control effect obtained by the control parameters to determine the way to optimize the control parameters.

[0088] Furthermore, in some embodiments, the above system may employ the Modbus / Pderofibus communication protocol for inter-module communication.

[0089] The wind farm cluster control system disclosed herein includes a data acquisition module, a data management module, an optimization calculation module, and an evaluation and optimization module. The data acquisition module groups the wind farms and acquires corresponding data from various systems. The data management module categorizes and stores the wind farm data in corresponding databases and manages these databases. The optimization calculation module uses a wind farm cluster control model to obtain the control parameters for each group within the wind farm. The evaluation and optimization module evaluates the wind farm's output and analyzes the control parameters of each wind turbine within the wind farm, using the analysis results to control each wind turbine. Therefore, the wind farm cluster control system proposed in this application can achieve group management of wind farm clusters, obtain the control parameters for each group using a wind farm cluster control model, and control each group of wind turbines. This allows the wind farm cluster control system to uniformly control each group of wind turbines based on the maximum output power of the wind farm cluster, thereby achieving intelligent control of the wind farm cluster.

[0090] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0091] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0092] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A wind farm cluster control system, characterized in that, The wind farm cluster control system includes a data acquisition module, a data management module, an optimization calculation module, and an evaluation and optimization module. The data acquisition module is used to group wind farms and acquire data corresponding to wind farms through various different systems; The data management module is used to classify and store the data of the wind farm into corresponding databases, and to manage multiple different databases. The optimization calculation module is used to obtain the control parameters of each group in the wind farm using the wind farm group control model; The evaluation and optimization module is used to evaluate the output of the wind farm and analyze the control parameters of each wind turbine in the wind farm, and use the analysis results to control each wind turbine. Specifically, the data acquisition module is used to group wind farms according to wind direction, wind turbine layout, and wind turbine status to obtain non-flagship wind turbine generators and flagship generators with higher priority than non-flagship wind turbine generators. The evaluation and optimization module is also used to perform sensitivity analysis on the control effect obtained from the control parameters, so as to optimize the control parameters. Obtain the wind farm group control model, including: A dynamic model of a wind turbine is constructed, in which the back wind speed of the turbine is a nonlinear function of the turbine's captured wind speed and its output power. The back wind speed of the turbine is: Where A is the swept area of ​​the fan, P w ρ is the actual active power output of the wind turbine, k is the air density, k is the energy loss coefficient of the wind turbine unit's combined mechanical and electrical losses, and v is the wind speed captured by the unit. Construct a wind turbine wake effect model, where the capture wind velocity of unit y is: Where K is the set of units that cause wake effects on unit y, and the wake effect area of ​​these K units at y is decomposed into a mesh set w(k). δ is the wake factor. yw(k) This is the wind speed attenuation coefficient. It is the intersection of the wake area of ​​unit k and the rotor area of ​​unit y; A time-delay effect model between wind turbine units is constructed, wherein, in this model, the wind speed captured by unit y at time t after taking time delay into account is: Among them, T ky Let T be the time delay of the wake effect of unit k on unit y. y The wind speed time delay at position y of the unit; Constructing a wind farm group control model, including: The wind farm group control model uses a cosine trigonometric function model to simulate the radial velocity distribution and a sinine trigonometric function model to simulate the axial velocity distribution. The model is as follows: If the wake radii of the two models are the same, the relationship between the period of the trigonometric function of the model and the wake radius is as follows: ， When the radial distance r extends to the wake radius r x At that time, the wind speed recovers to the incoming wind speed u0: Among them, the areas contained in the velocity type are equal, according to the following formula: Among them, u The velocity magnitude calculated using the turbulence model is given by the following formula: A=u0-u T=π / r x B=u0-u K=π / r x C=u Based on the above, the prediction step is as follows: The corrective steps are as follows: 。 2. The wind farm cluster control system according to claim 1, characterized in that, The various systems include a distributed edge computing processing system, a wind direction prediction system, a SCADA system, and a life prediction and control system. The distributed edge computing processing system is used to acquire real-time data from each wind turbine; The wind direction prediction system is used to acquire predicted data on wind speed and wind direction; The SCADA system is used to obtain the total power generation. The life prediction and control system is used to obtain fatigue load status.

3. The wind farm cluster control system according to claim 2, characterized in that, The real-time data includes actual power generation, yaw angle, and turbine status.

4. The wind farm cluster control system according to claim 1, characterized in that, The various databases include: wind turbine status database, wake database, and wind turbine parameter database.

5. The wind farm cluster control system according to claim 1, characterized in that, The optimization calculation module is also used to predict the power of the wind farm.

6. The wind farm cluster control system according to claim 1, characterized in that, The system uses the Modbus / Pderofibus communication protocol for communication.

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