CN117669968A - Wind farm scheduling method, device, equipment and medium - Google Patents
Wind farm scheduling method, device, equipment and medium Download PDFInfo
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Abstract
The application discloses a wind farm scheduling method, device, equipment and medium, relates to the technical field of automatic control, and comprises the following steps: determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to predicted wind speed information and the installation positions of the wind turbines; acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, and calculating total power error of the wind power plant based on the target wind power plant power and the current wind power plant power; calculating pitch angle change information of each wind turbine generator under the current wind power of the wind power plant according to the energy capturing relation, a preset wind speed prediction model and pitch angles of each wind turbine generator; and distributing corresponding target pitch angles for corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines, and realizing wind farm dispatching. And the target power or the maximum captured power is tracked rapidly through common change of the pitch angles of all the wind turbines, so that the overall power of the wind farm is optimized.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a wind farm scheduling method, device, equipment and medium.
Background
In a wind power plant, wake flows formed by upstream wind turbines can lead to reduced wind speed and increased turbulence intensity in the wake flows, and if a wind turbine is in a wake flow area of a previous wind turbine, the inflow wind speed of the wind turbine is lower than the inflow wind speed of the previous wind turbine. With the scale increase and clustering development of the wind power plant, the distance between wind turbines cannot be too large due to the limitation of the field, so the total power generation of the wind power plant is affected by wake effect. For large wind farms, the power loss due to wake effects is typically 10% -20% of the total power of the wind farm. The pitch system controls the wind turbine generator by controlling the included angle (pitch angle) between the blades and the wind direction, the change range of the pitch angle is 0-90 degrees, the capability of capturing wind energy of the blades is reduced along with the increase of the pitch angle, and when the pitch angle is 90 degrees, the blades feathering, so that aerodynamic braking can be realized.
For the traditional maximum power capturing strategy of wind turbines, no consideration is given to wake flow effect among the turbines, the wind turbines in the wind power plant are operated independently, each wind turbine achieves an optimal state by controlling controllable factors such as pitch angle, wind wheel rotating speed and the like, however, for the problem of optimizing the total output power of the wind power plant, the local optimal of each wind turbine is not necessarily achieved by the interaction among the turbines.
In summary, how to consider wake flow effects among wind turbines in a wind farm to make the overall power generation of the wind farm achieve global optimum is a technical problem to be solved in the field.
Disclosure of Invention
In view of the above, the present invention aims to provide a wind farm scheduling method, a device, equipment and a medium, which can consider wake effects among wind turbines in a wind farm and enable the overall power generation of the wind farm to be globally optimal. The specific scheme is as follows:
in a first aspect, the present application discloses a wind farm scheduling method, applied to a central controller of a wind farm, comprising:
determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to predicted wind speed information and the installation position of the wind turbines;
acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power;
calculating pitch angle change information of each wind turbine generator set under the current wind power plant power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of each wind turbine generator set;
And respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching.
Optionally, the determining the energy capturing relationship between the wind turbines based on the preset wind speed prediction model and according to the predicted wind speed information and the installation position of each wind turbine includes:
acquiring current upstream wind speed information corresponding to an upstream wind turbine generator set, which is transmitted by a laser radar anemometer;
acquiring a current axial induction coefficient through a preset wind speed prediction model;
updating the wind speed information of the current wind turbine based on the impeller radius, the wake flow descent coefficient, the current axial induction coefficient, the current upstream wind speed information of the upstream wind turbine and the installation position of each wind turbine to obtain current target wind speed information as predicted wind speed information of the current wind turbine;
and acquiring an energy capture relationship among the wind turbines based on all the predicted wind speed information and the installation positions of the wind turbines.
Optionally, the calculating the pitch angle change information of each wind turbine generator set under the current wind farm power according to the energy capturing relationship, the preset wind speed prediction model and the pitch angle of each wind turbine generator set includes:
And if the total power error is not zero, calculating the partial derivative of the pitch angle of each wind turbine under the current wind farm power according to the energy capturing relation among the wind turbines and the pitch angle variation of the pitch angle of each wind turbine.
Optionally, the allocating a corresponding target pitch angle to each corresponding wind turbine unit based on the total power error and the pitch angle change information of each wind turbine unit to implement wind farm scheduling includes:
and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the pitch angle partial derivatives of the wind turbines, and sending the target pitch angles to the local controllers of the wind turbines so that the local controllers control a pitch mechanism to perform pitch angle increment adjustment on the wind turbines based on the target pitch angles to realize wind farm dispatching.
Optionally, the allocating a corresponding target pitch angle to each corresponding wind turbine based on the pitch angle partial derivative of each wind turbine includes:
and multiplying the pitch angle partial derivative of each wind turbine unit by the total power error to obtain a corresponding target pitch angle for distribution to each wind turbine unit.
Optionally, the calculating the total power error of the wind farm based on the target wind farm power and the current wind farm power includes:
calculating the absolute value of the difference between the power of the target wind power plant and the current wind power plant as the total power error of the wind power plant;
judging whether the total power error is zero;
and if the total power error is zero, skipping to execute the step of acquiring the target wind power plant power, the current wind power plant power and the pitch angles of the wind turbine generators sent by the local controllers.
Optionally, the wind farm scheduling method includes: and the central controller of the wind power plant is communicated with the local controllers of the wind power generation sets through preset data transmission channels.
In a second aspect, the application discloses a wind farm scheduling device, applied to a central controller of a wind farm, comprising:
the information acquisition module is used for determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to the predicted wind speed information and the installation position of each wind turbine;
the error calculation module is used for acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by each local controller so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power;
The change information calculation module is used for calculating the change information of the pitch angles of the wind turbines under the current wind farm power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of the wind turbines;
and the scheduling module is used for respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm scheduling.
In a third aspect, the present application discloses an electronic device comprising:
a memory for storing a computer program;
and a processor for executing the computer program to implement the steps of the wind farm scheduling method disclosed above.
In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the previously disclosed wind farm scheduling method.
As can be seen, the application discloses a wind farm scheduling method, applied to a central controller of a wind farm, comprising: determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to predicted wind speed information and the installation position of the wind turbines; acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power; calculating pitch angle change information of each wind turbine generator set under the current wind power plant power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of each wind turbine generator set; and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching. Therefore, the pitch angles of all wind turbines, the current wind farm power and the target wind farm power are obtained simultaneously, the current wind farm state is determined to be close to or far from the target wind farm power, the pitch angles of all wind turbines are adjusted simultaneously, tracking of the target power is achieved in a pitch angle space, climbing is conducted in multiple dimensions jointly, further the target power or the maximum capture power is tracked rapidly through joint change of the pitch angles of all wind turbines, and the overall power generation of the wind farm is optimized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for scheduling a wind farm disclosed in the present application;
FIG. 2 is a graph of a change in pitch angle space of wind farm power as disclosed herein;
FIG. 3 is a schematic diagram of a relationship between a central controller and a local controller disclosed in the present application;
FIG. 4 is a high-dimensional hill climbing control flow diagram of a wind farm scheduling strategy disclosed herein;
FIG. 5 is a graph of wind farm output power achieved based on a wind power scheduling strategy as disclosed herein;
FIG. 6 is a schematic structural diagram of a wind farm scheduling device disclosed in the present application;
fig. 7 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In a wind power plant, wake flows formed by upstream wind turbines can lead to reduced wind speed and increased turbulence intensity in the wake flows, and if a wind turbine is in a wake flow area of a previous wind turbine, the inflow wind speed of the wind turbine is lower than the inflow wind speed of the previous wind turbine. With the scale increase and clustering development of the wind power plant, the distance between wind turbines cannot be too large due to the limitation of the field, so the total power generation of the wind power plant is affected by wake effect. For large wind farms, the power loss due to wake effects is typically 10% -20% of the total power of the wind farm. The pitch system controls the wind turbine generator by controlling the included angle (pitch angle) between the blades and the wind direction, the change range of the pitch angle is 0-90 degrees, the capability of capturing wind energy of the blades is reduced along with the increase of the pitch angle, and when the pitch angle is 90 degrees, the blades feathering, so that aerodynamic braking can be realized.
For the traditional maximum power capturing strategy of wind turbines, no consideration is given to wake flow effect among the turbines, the wind turbines in the wind power plant are operated independently, each wind turbine achieves an optimal state by controlling controllable factors such as pitch angle, wind wheel rotating speed and the like, however, for the problem of optimizing the total output power of the wind power plant, the local optimal of each wind turbine is not necessarily achieved by the interaction among the turbines.
Therefore, the invention provides a wind power plant scheduling scheme which can consider wake flow effects among wind turbines in a wind power plant and enable the overall power generation of the wind power plant to be globally optimal.
Referring to fig. 1, the embodiment of the invention discloses a wind farm scheduling method, which is applied to a central controller of a wind farm and comprises the following steps:
step S11: and determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to the predicted wind speed information and the installation position of the wind turbines.
In the embodiment, current upstream wind speed information corresponding to an upstream wind turbine generator set, which is sent by a laser radar anemometer, is acquired; acquiring a current axial induction coefficient through a preset wind speed prediction model; updating the wind speed information of the current wind turbine based on the impeller radius, the wake flow descent coefficient, the current axial induction coefficient, the current upstream wind speed information of the upstream wind turbine and the installation position of each wind turbine to obtain current target wind speed information as predicted wind speed information of the current wind turbine; and acquiring an energy capture relationship among the wind turbines based on all the predicted wind speed information and the installation positions of the wind turbines. It can be understood that the laser radar wind meter in the wind power plant is used for collecting the wind speed information of each wind power plant at the installation position of each wind power plant, and it is noted that for wind power plant control, the problem of response lag of the traditional wind meter in real-time control of the wind power plant is serious due to the inertia of the wind power plant blades and the time delay of signal transmission. The problem can be well solved by using the laser radar anemometer to pre-measure the wind speed, so that the wind speed information transmitted by the laser radar anemometer meets the requirements of the wind power plant scheduling aspect of the invention. Then, the energy capture relationship is determined as follows: considering the structure of wind farm, the change of pitch angle of any wind turbine cannot influence the running state of the front wind turbine, if the ith station is recorded in a certain time period
Power change of wind turbine generator system is delta P i The power influence of the ith wind turbine generator on the jth wind turbine generator is p i，j Obviously, when i > j, p i，j =0, the power change of each wind turbine in the wind farm is as follows:
obviously, the above constraints are not sufficient to solve all p i，j Considering that the power of the second wind turbine is necessarily increased or unchanged (the rated wind speed has been reached) when only the first wind turbine discards wind, i.e. p 1，1 And p is as follows 1，2 Must be of opposite sign in the case of a value other than 0, p being the same as 1，n The symbols of (n=2, 3..n) are all identical to p 1，1 Instead, the magnitude relationship is determined by the wind speed prediction model and the energy capture relationship, namely:
the influence of the upstream unit on the wind speed of the downstream unit can be determined through the wake model, and the following model is taken as an example for explanation:
v is the wind speed in the x direction of the current position; v 0 The wind speed is the wind speed in front of the upstream unit; a is an axial induction coefficient, and the wind field historical data or the CFD simulation result can be identified by the neural network to obtain a real-time axial induction coefficient; r is (r) 0 Is the radius of the impeller; k is a wake-flow descent coefficient, and when the incoming wind is natural wind, k=0.04.
Assuming that the distance between the serial adjacent wind turbines is D, letThe incoming wind speed of the first typhoon motor group is v 1 The incoming wind speed of the second typhoon motor group is v 2 ＝(1-2a 1 C)v 1 The incoming flow wind speed of the nth wind motor group is v n ＝(1-2a 1 C)(1-2a 2 C)…(1-2a n-1 C)v 1 . From the energy capturing point of view, the influence of a certain unit on the rear unit is regulated to be exponentially decreased along with the increase of the unit number, and the decreasing proportion is close to (1-2 aC) 2 . Assume that the power change after the adjustment of the first fan is deltaP 1 The effect on the downstream wind turbine may be sequentially noted as- (1-2 a) 1 C) 2 ΔP 1 ，-(1-2a 1 C) 2 (1-2a 2 C) 2 ΔP 1 …. And when in body implementation, the wind power plant history data or CFD simulation is matched with a neural network to correct the axial induction coefficient a in the wake model in real time or fix the axial induction coefficient a in a certain period of time.
The influence of other wind turbines on the relationship between the wind turbines and the rear wind turbine is the same as the following formula:
the axial induction coefficient a is determined by laser radar measurement data, a wind speed preprocessing algorithm and a neural network. Or by empirical formulas in a specific wind farm, and does not require repeated solutions to the above-described linear system of equations in real-time control.
Step S12: and acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power.
In this embodiment, a target wind farm power is obtained, where the target wind farm power is given by a human mode, and meanwhile, a current wind farm power and a pitch angle of each wind turbine generator set sent by each local controller are obtained, and then a total power error is calculated based on the target wind farm power and the current wind farm power, specifically: calculating the absolute value of the difference between the power of the target wind power plant and the current wind power plant as the total power error of the wind power plant; judging whether the total power error is zero; and if the total power error is zero, skipping to execute the step of acquiring the target wind power plant power, the current wind power plant power and the pitch angles of the wind turbine generators sent by the local controllers. It can be understood that the difference calculation is performed on the target wind power plant power and the current wind power plant power, then the absolute value of the difference obtained after the difference calculation is obtained, the absolute value of the difference is used as the total power error of the wind power plant, and then whether the current dispatching state of the current wind power plant is consistent with the dispatching state corresponding to the target wind power plant power is determined by judging whether the total power error is zero, namely whether the current dispatching strategy of the current wind power plant is an optimal dispatching strategy is judged; and if the total power error is zero, indicating that the current scheduling strategy is the optimal scheduling strategy, returning to execute the step of acquiring the target wind power plant power, the current wind power plant power and the pitch angles of the wind turbines sent by the local controllers, namely monitoring in real time.
In this embodiment, the central controller of the wind farm and the local controllers located in the wind turbines communicate through a preset data transmission channel. It can be understood that the controllers of the wind power plant are divided into a central controller and local controllers, the local controllers are arranged on each unit, the central controller is a main controller for controlling all the local controllers and is used for receiving the predicted wind speed of the laser radar anemometer and the feedback quantity of the states of each wind power unit, and on the basis of simultaneously acquiring the change directions of all the pitch angles and the current change to enable the states of the wind power plant to be close to or far from the target power, all the pitch angles are adjusted simultaneously, the tracking of the target power is realized in the pitch angle space, and a plurality of dimensions jointly perform mountain climbing. The central controller and the local controllers are in bidirectional communication through a preset data channel, so that the central controller sends pitch angle adjustment instructions to the local controllers through the data channel or the local controllers upload pitch angle information of the wind turbines to the central controller through the data channel.
Step S13: and calculating the pitch angle change information of each wind turbine generator set under the current wind power of the wind power plant according to the energy capturing relation, the preset wind speed prediction model and the pitch angle of each wind turbine generator set.
In the embodiment, as the hill climbing method is a classical maximum power tracking method in wind turbine generator control, and the combination of an empirical formula shows that the wind energy utilization coefficient is in a trend of increasing and then decreasing along with the change of the tip speed ratio on the premise of unchanged pitch angle. The maximum wind energy utilization coefficient is tracked on the premise of defining the speed ratio change direction of the blade tip. The climbing method is applied based on that when only the rotational speed and the wind energy utilization coefficient (power) are considered, this is a typical single input, single output system, and the output of the system has peaks. The current rotational speed change direction is "close" or "far" from the maximum power, which can be obtained by the feedback amount within each control cycle. In the context of a wind farm, if the maximum power capture of the entire wind farm is tracked by pitch control scheduling, this is equivalent to controlling a multiple-input, single-output control system, with multiple inputs being independent, i.e. there is no constraint between pitch angles of the different units. Taking 3 units as an example, the power state of the wind farm and the state of the "pitch angle space" are corresponding, as shown in fig. 2. Therefore, on the basis of combining fig. 2, if the total power error is not zero, calculating the pitch angle partial derivative of each wind turbine at the current wind farm power according to the energy capturing relation among the wind turbines and the pitch angle variation of the pitch angle of each wind turbine. The pitch angle partial derivative may then be used as a gradient of the current wind farm power P, so the step of calculating the gradient of the current wind farm power P is as follows:
The power of the target wind power plant is directly input to the central controller, and then the central controller gives relative instructions to each local controller, and as the controller approaches the power of the target wind power plant through multi-wheel adjustment, the central controller receives the current wind power plant power and pitch angle from each wind turbine generator before each wheel of adjustment; when the specific implementation is carried out, if a certain small power is required to be kept, the specific target wind power plant power is directly input to the central controller, and if the maximum power capture of the wind power plant is required, the maximum capture power which can be theoretically achieved by the wind power plant is input. Central controller connectionAfter receiving the target wind farm power, the adjustment is performed until the target power is not reached. For any beta i (i=1, 2,3 …), the effect of its independent variation on the overall wind farm power is mathematically very close to the partial derivative of power with respect to this pitch angle, and for all β, the gradient of the current wind farm power state P needs to be obtained, as follows:
wherein, can be used forUnderstood as beta i The contribution of the change of (c) to the wind farm power change, when all beta changes simultaneously, the individual components of the gradient cannot be obtained from the total power change divided by the change of pitch angle, respectively. P can be known from the formula of influence of each wind turbine on the relationship between the wind turbine and the rear wind turbine i，i (i=1, 2, 3..n.) is solvable, then for β i Since the constraint on the relationship between the wind speed of a certain unit and the wind speed of a downstream unit in the above derivation is not completely accurate, the partial derivative of the total power P on it can be estimated by the following formula:
wherein Δρ i The method is used for controlling the variable quantity of the pitch angle of the ith unit in the time step, and estimating the partial derivative of the power of the wind power plant to the pitch angle according to the formula in real-time control.
Step S14: and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching.
In this embodiment, a corresponding target pitch angle is respectively allocated to each corresponding wind turbine based on a pitch angle partial derivative of each wind turbine, and each target pitch angle is calculatedAnd the standard pitch angles are sent to the local controllers of the wind turbines, so that the local controllers control the pitch mechanism to perform pitch angle increment adjustment on the wind turbines based on the target pitch angles, and the wind farm scheduling is realized. It will be appreciated that since the controller includes both functionality to track the maximum captured power and to track a given target power, the central controller calculates a power error signal based on the wind farm target power and the current power. Then combining the pitch angle partial derivatives of each wind turbine generator And (3) distributing target pitch angles for all the wind turbine generators. The pitch angle partial derivative size is understood here as pitch angle β i Contribution to wind farm power variation. Then, after receiving the target pitch angle from the central controller, the local controllers arranged on the wind turbine generators control the pitch-changing mechanism to track the target pitch angle. In this way, the wind speed of the downstream wind turbine is increased by enabling the upstream wind turbine to discard wind, so that the total power generation of the wind farm is increased. In specific implementation, the power target is completed without one-time pitching, and the target power is continuously approximated through multiple pitching, so that the wind power plant scheduling is realized. Therefore, the pitch angles of all units can be changed together so as to track the target power or the maximum capture power rapidly, the wind is abandoned by the front unit, the effective wind speed of the rear unit is improved, the whole power generation power of the wind power plant is optimized, the influence of wake flow can be reduced, the whole capture power of the wind power plant is improved, the loads of blades and hubs are reduced, and the service life of the wind turbine is prolonged.
In this embodiment, the assigning, for each corresponding wind turbine, a corresponding target pitch angle based on the pitch angle partial derivative of each wind turbine includes: and multiplying the pitch angle partial derivative of each wind turbine unit by the total power error to obtain a corresponding target pitch angle for distribution to each wind turbine unit. In specific implementation, the pitch angle allocated to each wind turbine may be determined by the pitch angle partial derivative of the wind turbine Multiplied by the total power error signal of the wind farm.
In this embodiment, as shown in fig. 3, a schematic diagram of a relationship between a central controller and a local controller is disclosed, firstly, a lidar anemometer measures and acquires wind speed information, then inputs the wind speed information into a wind evolution model as a preset wind speed prediction model so as to acquire predicted wind speed of a wind turbine, then sequentially inputs the predicted wind speed of the wind turbine into each wind turbine district network, acquires corresponding wake input information, respectively inputs the wake input information into a corresponding wake model so as to respectively obtain predicted wind speed of each wind turbine, then inputs all the wake input information into the central controller so as to enable the central controller to output a target pitch angle signal based on the information, and then notifies each local controller to execute adjustment of the pitch angle of each wind turbine, so that the local controller monitors and transmits information such as actual power, pitch angle and the like of each wind turbine to the central controller, so that the central controller re-executes the whole flow according to the monitored information, and adjusts the pitch angle change information.
The high-dimensional mountain climbing control flow shown in fig. 4 divides the controllers of the wind farm into a central controller and a local controller, wherein the local controller is arranged on each wind turbine generator, and the central controller and the local controller mainly have the following functions: the input of the central controller is mainly the wind speed predicted by the laser radar and the feedback quantity of the states of all the wind turbines, and meanwhile, a pitch angle increment command (increasing or decreasing) is issued to each wind turbine, and the local controller controls the wind turbines to pitch; determining an energy capturing relation among the wind turbines according to the wind turbine installation position and the wind speed prediction model, namely adjusting the influence of a certain wind turbine on energy capturing of other wind turbines; the central controller obtains the target power of the wind power plant, the current power P of the whole wind power plant, the power of each unit and the pitch angle; the central controller calculates partial derivatives of the current whole wind power plant power P to the pitch angles beta of all units according to a wind speed prediction model and an energy capture relation, namely the gradient of the current power state P; the central controller distributes target pitch angles of different units according to the target power of the wind farm and the size of the pitch angle partial derivative; the local controller tracks the target pitch angle. The wind power plant target power is directly input to the central controller, and then the central controller gives relative instructions to each local controller, and as the controllers approach the target power through multi-wheel adjustment, the central controller receives the current power and pitch angle from each wind turbine before each wheel adjustment; in the specific implementation, if a certain small power is required to be kept, the specified target power is directly input to the central controller, and if the maximum power capture of the wind power plant is required, the maximum capture power which can be theoretically achieved by the wind power plant is input. FIG. 5 is a graph of the output power of 5MW tandem units controlled by the present invention at rated wind speed (11.4 m/s), where the total target power is 15MW for the first 400s and 25MW for the second 200 s.
As can be seen, the application discloses a wind farm scheduling method, applied to a central controller of a wind farm, comprising: determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to predicted wind speed information and the installation position of the wind turbines; acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power; calculating pitch angle change information of each wind turbine generator set under the current wind power plant power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of each wind turbine generator set; and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching. Therefore, the pitch angles of all wind turbines, the current wind farm power and the target wind farm power are obtained simultaneously, the current wind farm state is determined to be close to or far from the target wind farm power, the pitch angles of all wind turbines are adjusted simultaneously, tracking of the target power is achieved in a pitch angle space, climbing is conducted in multiple dimensions jointly, further the target power or the maximum capture power is tracked rapidly through joint change of the pitch angles of all wind turbines, and the overall power generation of the wind farm is optimized.
Referring to fig. 6, the invention also discloses a wind farm dispatching device, which is applied to a central controller of a wind farm, and comprises:
the information acquisition module 11 is used for determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to the predicted wind speed information and the installation position of each wind turbine;
an error calculation module 12, configured to obtain a target wind farm power, a current wind farm power, and pitch angles of the wind turbines sent by each local controller, so as to calculate a total power error of the wind farm based on the target wind farm power and the current wind farm power;
the change information calculation module 13 is configured to calculate, according to the energy capturing relationship, the preset wind speed prediction model, and pitch angles of the wind turbines, pitch angle change information of the wind turbines at the current wind farm power;
the scheduling module 14 is configured to allocate a corresponding target pitch angle to each corresponding wind turbine unit based on the total power error and the pitch angle change information of each wind turbine unit, so as to implement wind farm scheduling.
The method comprises the steps that energy capture relations among wind turbines are determined based on a preset wind speed prediction model according to predicted wind speed information and installation positions of the wind turbines; acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power; calculating pitch angle change information of each wind turbine generator set under the current wind power plant power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of each wind turbine generator set; and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching. Therefore, the pitch angles of all wind turbines, the current wind farm power and the target wind farm power are obtained simultaneously, the current wind farm state is determined to be close to or far from the target wind farm power, the pitch angles of all wind turbines are adjusted simultaneously, tracking of the target power is achieved in a pitch angle space, climbing is conducted in multiple dimensions jointly, further the target power or the maximum capture power is tracked rapidly through joint change of the pitch angles of all wind turbines, and the overall power generation of the wind farm is optimized.
Further, the embodiment of the present application further discloses an electronic device, and fig. 7 is a block diagram of the electronic device 20 according to an exemplary embodiment, where the content of the figure is not to be considered as any limitation on the scope of use of the present application.
Fig. 7 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is configured to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps in the wind farm scheduling method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
Processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 21 may also comprise a main processor, which is a processor for processing data in an awake state, also called CPU (Central Processing Unit ); a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the processor 21 on the mass data 223 in the memory 22, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further comprise a computer program capable of performing other specific tasks in addition to the computer program capable of performing the wind farm scheduling method performed by the electronic device 20 as disclosed in any of the previous embodiments. The data 223 may include, in addition to data received by the electronic device and transmitted by the external device, data collected by the input/output interface 25 itself, and so on.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the previously disclosed wind farm scheduling method. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in random access Memory RAM (Random Access Memory), memory, read-Only Memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (Electric Erasable Programmable Read Only Memory), registers, hard disk, a removable disk, a CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The method, the device, the equipment and the medium for dispatching the wind farm provided by the invention are described in detail, and specific examples are applied to the explanation of the principle and the implementation mode of the invention, and the explanation of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. A method of scheduling a wind farm, comprising:
determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to predicted wind speed information and the installation position of the wind turbines;
acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by local controllers, so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power;
calculating pitch angle change information of each wind turbine generator set under the current wind power plant power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of each wind turbine generator set;
and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm dispatching.
2. The wind farm scheduling method according to claim 1, wherein the determining the energy capturing relationship between the wind turbines based on the preset wind speed prediction model and according to the predicted wind speed information and the installation position of the wind turbines comprises:
Acquiring current upstream wind speed information corresponding to an upstream wind turbine generator set, which is transmitted by a laser radar anemometer;
acquiring a current axial induction coefficient through a preset wind speed prediction model;
updating the wind speed information of the current wind turbine based on the impeller radius, the wake flow descent coefficient, the current axial induction coefficient, the current upstream wind speed information of the upstream wind turbine and the installation position of each wind turbine to obtain current target wind speed information as predicted wind speed information of the current wind turbine;
and acquiring an energy capture relationship among the wind turbines based on all the predicted wind speed information and the installation positions of the wind turbines.
3. The wind farm scheduling method according to claim 2, wherein said calculating pitch angle change information of each of the wind turbines at the current wind farm power according to the energy capturing relation, the preset wind speed prediction model, and a pitch angle of each of the wind turbines comprises:
and if the total power error is not zero, calculating the partial derivative of the pitch angle of each wind turbine under the current wind farm power according to the energy capturing relation among the wind turbines and the pitch angle variation of the pitch angle of each wind turbine.
4. A method of scheduling a wind farm according to claim 3, wherein said respectively assigning a corresponding target pitch angle to each corresponding wind turbine based on the total power error and pitch angle change information of each wind turbine to achieve wind farm scheduling comprises:
and respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the pitch angle partial derivatives of the wind turbines, and sending the target pitch angles to the local controllers of the wind turbines so that the local controllers control a pitch mechanism to perform pitch angle increment adjustment on the wind turbines based on the target pitch angles to realize wind farm dispatching.
5. The wind farm scheduling method of claim 4, wherein said respectively assigning a corresponding target pitch angle for each corresponding wind turbine based on a pitch angle partial derivative of each wind turbine comprises:
and multiplying the pitch angle partial derivative of each wind turbine unit by the total power error to obtain a corresponding target pitch angle for distribution to each wind turbine unit.
6. The wind farm scheduling method of claim 1, wherein the calculating the total power error of the wind farm based on the target wind farm power and the current wind farm power comprises:
Calculating the absolute value of the difference between the power of the target wind power plant and the current wind power plant as the total power error of the wind power plant;
judging whether the total power error is zero;
and if the total power error is zero, skipping to execute the step of acquiring the target wind power plant power, the current wind power plant power and the pitch angles of the wind turbine generators sent by the local controllers.
7. A wind farm scheduling method according to any of the claims 1-6, wherein the central controller of the wind farm communicates with the local controllers at each of the wind turbines via a preset data transmission channel.
8. A wind farm scheduling device, characterized by a central controller applied to a wind farm, comprising:
the information acquisition module is used for determining an energy capture relationship among the wind turbines based on a preset wind speed prediction model and according to the predicted wind speed information and the installation position of each wind turbine;
the error calculation module is used for acquiring target wind power plant power, current wind power plant power and pitch angles of wind turbines sent by each local controller so as to calculate total power error of the wind power plant based on the target wind power plant power and the current wind power plant power;
The change information calculation module is used for calculating the change information of the pitch angles of the wind turbines under the current wind farm power according to the energy capture relation, the preset wind speed prediction model and the pitch angles of the wind turbines;
and the scheduling module is used for respectively distributing corresponding target pitch angles for the corresponding wind turbines based on the total power error and the pitch angle change information of the wind turbines so as to realize wind farm scheduling.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the wind farm scheduling method according to any of the claims 1 to 7.
10. A computer-readable storage medium storing a computer program; wherein the computer program when executed by a processor implements the steps of a wind farm scheduling method according to any of the claims 1 to 7.
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| CN117967498A (en) * | 2024-03-15 | 2024-05-03 | 三峡新能源海上风电运维江苏有限公司 | Method, device, equipment, medium and program product for controlling variable pitch of fan |
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| CN117967498A (en) * | 2024-03-15 | 2024-05-03 | 三峡新能源海上风电运维江苏有限公司 | Method, device, equipment, medium and program product for controlling variable pitch of fan |
| CN117967498B (en) * | 2024-03-15 | 2024-10-01 | 三峡新能源海上风电运维江苏有限公司 | Method, device, equipment, medium and program product for controlling variable pitch of fan |
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