Wind turbine maximum power point tracking control method and system based on wake prediction

Abstract

The present disclosure belongs to the technical field of wind power control, and particularly relates to a wind turbine maximum power point tracking control method and system based on wake prediction, which comprises the following steps: obtaining the wind turbine power and the axial induction factor at the current time and after the wind turbine action response time, respectively; calculating the wind turbine power difference and the axial induction factor difference to obtain the partial derivative of the wind turbine power to the axial induction factor; between the wind turbine action response time and the maximum wake conduction time, combining the wake wind speed prediction model and the partial derivative of the wind turbine power to the axial induction factor, obtaining the predicted wake of the wind turbine; according to the predicted wake, iteratively calculating the wind turbine power, the partial derivative of the wind turbine power to the axial induction factor and the axial induction factor until the wind turbine axial induction factor value converges, obtaining the maximum power point of the wind turbine, and completing the wind turbine maximum power point tracking control based on wake prediction.

Description

Technical Field

[0001] This disclosure belongs to the field of wind power control technology, specifically relating to a maximum power point tracking control method and system for wind turbines based on wake prediction. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Wind power has become the mainstream of new energy power generation, but the slow response speed of maximum power point tracking (MPPT) control in wind power plants directly affects the output power of wind power.

[0004] When a wind turbine extracts energy from the airflow, it generates a slow-moving, turbulent air wake that flows against the wind. If another wind turbine is in the path of this wake, the reduced wake speed will decrease the power output of the downstream turbine, thus reducing the total power output of the wind farm. Currently, wind farm MPPT (Multi-Level Monitoring and Testing) relies on a central control station, which needs to receive real-time information from the turbines within the farm and issue active power control commands. With the scaling up of wind farms, the communication burden on the central control station of large-scale farms is becoming increasingly heavy, with high communication bandwidth requirements and increased construction costs. The MPPT calculation time at the wind farm level increases with the number of turbines in the farm, making it difficult to guarantee the required MPPT response time for large-scale clusters. Furthermore, a failure of the central control station will cause the entire wind farm to lose control, and may even cause large-scale turbine disconnection from the grid, resulting in low system reliability.

[0005] According to the inventor, existing wind power MPPT control mainly adopts the gradient ascent method and the quasi-Newton method. By sharing power information with adjacent units, the control convergence gradient is calculated so that the entire wind farm gradually converges to the maximum power point. In the control process, the convergence adjustment process is relatively long, and the utilization efficiency of wind energy is low. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a maximum power point tracking control method and system for wind turbines based on wake prediction. During the wake propagation process, the power change of the downwind turbine caused by the wake is calculated in advance using a wake model, and a new convergence gradient is calculated based on the predicted wake-induced change, thereby achieving pre-adjustment during the wake propagation process. This effectively reduces power loss during the adjustment process and improves the utilization efficiency of wind energy.

[0007] According to some embodiments, the first solution of this disclosure provides a maximum power point tracking control method for wind turbines based on wake prediction, which adopts the following technical solution:

[0008] A maximum power point tracking control method for wind turbines based on wake prediction includes:

[0009] The wind turbine's power generation and axial induction factor are obtained at the current moment and after the wind turbine's action response time, respectively.

[0010] Calculate the difference in wind turbine power generation and the difference in axial induction factor to obtain the partial derivative of wind turbine power generation with respect to axial induction factor;

[0011] At the moment between the wind turbine's action response time and the maximum wake conduction time, the predicted wake of the wind turbine is obtained by combining the preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor.

[0012] Based on the predicted wake, the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor are iteratively calculated until the obtained axial induction factor value of the wind turbine converges, thus obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

[0013] As a further technical limitation, a fixed-step maximum power point tracking control method is used for iterative updating of the axial induction factor, thus obtaining... Among them, a i (k) represents the axial induction factor of the i-th wind turbine at the current time, k represents the current time, K represents the step size, j represents the j-th wind turbine, and i∈(0,n) and j∈(i,n), n represents the number of wind turbines in the wind turbine unit.

[0014] Furthermore, the iterative update process of the axial induction factor is only related to the downwind fan, i.e., it yields... Among them, the partial derivative of the wind turbine's power generation with respect to the axial induction factor at the current moment. for

[0015]

[0016] As a further technical limitation, the preset wake wind speed prediction model adopts a wake wind speed change model. The wake wind speed change is the prediction of wind speed during the decrease and recovery process of the downwind wind speed of the wind turbine, that is, the wind speed change coefficient s is obtained. i (d): Where d is the distance from the downwind side of the i-th fan to the unit, and C T,i (t0) is the thrust coefficient of the i-th fan at time t0, and R is the sweep radius of the i-th fan.

[0017] Furthermore, the wind speed v of the i-th fan at a downwind distance d is... x (d) is: vx (d)=s i (d)v ∞ ; where v ∞ This represents the actual wind speed at a distance d when there is no fan.

[0018] As a further technical limitation, in the process of calculating the maximum power point of the wind turbine, at the moment between the wind turbine's action response time and the maximum wake conduction time, the predicted axial induction factor is obtained through the wake wind speed prediction model. Combined with the change in the active power generated by each wind turbine, the partial derivative of the axial induction factor of the downwind turbine with respect to the upwind turbine is obtained, and the axial induction factor of the wind turbine is calculated. The calculation is repeated iteratively until the numerical value of the axial induction factor of the wind turbine converges.

[0019] Furthermore, at the moment when the axial induction factor of the wind turbine converges, the power generation of the wind turbine is obtained based on the axial induction factor, thus completing the maximum power point tracking control of the wind turbine.

[0020] According to some embodiments, the second aspect of this disclosure provides a maximum power point tracking control system for wind turbines based on wake prediction, employing the following technical solution:

[0021] A maximum power point tracking control system for wind turbines based on wake prediction includes:

[0022] The acquisition module is configured to acquire the wind turbine's power generation and axial induction factor at the current moment and after the wind turbine's action response time, respectively.

[0023] The calculation module is configured to calculate the difference in power generation of the wind turbine and the difference in axial induction factor, and obtain the partial derivative of the power generation of the wind turbine with respect to the axial induction factor.

[0024] The prediction module is configured to obtain the predicted wake of the wind turbine at the moment between the wind turbine's action response time and the maximum wake conduction time, by combining a preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor.

[0025] The control module is configured to iteratively calculate the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor based on the obtained predicted wake, until the obtained axial induction factor value of the wind turbine converges, thereby obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

[0026] According to some embodiments, a third aspect of this disclosure provides a computer-readable storage medium, employing the following technical solution:

[0027] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the maximum power point tracking control method for wind turbines based on wake prediction as described in the first aspect of this disclosure.

[0028] According to some embodiments, the fourth solution of this disclosure provides an electronic device that adopts the following technical solution:

[0029] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the maximum power point tracking control method for wind turbines based on wake prediction as described in the first aspect of this disclosure.

[0030] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0031] This disclosure utilizes a wake prediction model to pre-optimize distributed control during wake propagation. It effectively utilizes the wake propagation time, accelerating the dynamic response time of the wind farm's distributed MPPT. Operating within the gradient ascent method control framework, it retains the MPPT tracking accuracy of this method, ensuring the algorithm's effectiveness. The control method described in this disclosure does not alter the control framework; after wake propagation is complete, further gradient correction is performed based on the real-time wake influence, which helps maintain the original MPPT control accuracy. Attached Figure Description

[0032] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0033] Figure 1 This is a flowchart of the maximum power point tracking control method for wind turbines based on wake prediction in Embodiment 1 of this disclosure;

[0034] Figure 2 This is a schematic diagram of the wind turbine combination arrangement affected by wake coupling in Embodiment 1 of this disclosure;

[0035] Figure 3 This is a schematic diagram of the process of the maximum power point tracking control method for wind turbines based on wake prediction in Embodiment 1 of this disclosure;

[0036] Figure 4 This is a structural block diagram of the maximum power point tracking control system for wind turbines based on wake prediction in Embodiment 2 of this disclosure. Detailed Implementation

[0037] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] In this disclosure, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are merely relational terms determined for the convenience of describing the structural relationship of the various components or elements in this disclosure, and do not specifically refer to any component or element in this disclosure, nor should they be construed as limiting this disclosure.

[0041] In this disclosure, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this disclosure based on the specific circumstances, and they should not be construed as limitations on this disclosure.

[0042] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0043] Example 1

[0044] Embodiment 1 of this disclosure introduces a maximum power point tracking control method for wind turbines based on wake prediction.

[0045] like Figure 1 The maximum power point tracking control method for wind turbines based on wake prediction, as shown, includes:

[0046] The wind turbine's power generation and axial induction factor are obtained at the current moment and after the wind turbine's action response time, respectively.

[0047] Calculate the difference in wind turbine power generation and the difference in axial induction factor to obtain the partial derivative of wind turbine power generation with respect to axial induction factor;

[0048] At the moment between the wind turbine's action response time and the maximum wake conduction time, the predicted wake of the wind turbine is obtained by combining the preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor.

[0049] Based on the predicted wake, the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor are iteratively calculated until the obtained axial induction factor value of the wind turbine converges, thus obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

[0050] like Figure 2 The wind turbine generator set shown is considered to have a row of n wind turbines arranged side by side, and the power generation capacity of the i-th turbine is... Axial induction factor is The axial induction factor is the controlled object of the control method described in this embodiment. By controlling it, the wind energy utilization coefficient of the unit is changed. Its relationship with the wind energy utilization coefficient of the unit is C. pi =4a i (1-a i ) 2 However, due to the wake effect, changing the axial induction factor will not only change the power generation of the corresponding wind turbine, but also affect the power generation of the wind turbine units under that wind turbine.

[0051] To maximize the total power generation of the wind turbine cluster, a fixed-step maximum power point pursuit control algorithm is used to iteratively update the axial induction factor of each unit. This method is also known as the hill-climbing method:

[0052]

[0053] Among them, a i (k) represents the axial induction factor of the i-th wind turbine at the current time, k represents the current time, K represents the step size, j represents the j-th wind turbine, and i∈(0,n) and j∈(i,n), n represents the number of wind turbines in the wind turbine unit.

[0054] To facilitate implementation, formula (1) is simplified so that the update of the axial induction factor is only related to the downwind unit, resulting in the following axial induction factor update rate:

[0055]

[0056] The partial derivatives are calculated as follows:

[0057]

[0058] In wake prediction, this embodiment adopts the assumptions of frozen turbulence and constant average wind direction to simplify the explanation of the principle. In practical applications, the wake-coupled units can be dynamically grouped according to different wind directions to form a pattern such as... Figure 2 The array configuration shown.

[0059] Considering the wake and its propagation model of the generator unit is sufficient, which also greatly simplifies the computational complexity. The wake model consists of three prediction models: wake wind speed variation, wake radius expansion, and wake centerline trajectory. For ease of principle analysis, and for each generator unit as... Figure 2 The arrangement shown does not involve expansion or other effects; therefore, only the wake wind speed variation model is considered. The wake wind speed variation is a prediction of the decrease and recovery process of the wind speed downwind of the unit, and its wind speed variation coefficient is as follows:

[0060]

[0061] Where d is the distance from the downwind side of the i-th fan to the unit, and C T,i (t0) is the thrust coefficient of the i-th fan at time t0, and R is the sweep radius of the i-th fan.

[0062] In this embodiment, the wind speed v of the i-th fan at a downwind distance d is... x (d) is:

[0063] v x (d)=s i (d)v ∞ (5)

[0064] Among them, v ∞ This represents the actual wind speed at a distance d when there is no fan.

[0065] Specifically, the process of the maximum power point tracking control method for wind turbines based on wake prediction is as follows: Figure 3 As shown, specifically:

[0066] Step S01: Initial parameter setting, dynamic grouping of units affected by wake.

[0067] Based on wind direction, turbines affected by wake are grouped. A graph discovery algorithm is used, with boundary turbines identifying wind direction and then transmitting this information to all turbines within the wind farm via adjacent units. According to a pre-defined wind farm topology, each turbine, considering wind direction, identifies upwind and downwind units and establishes communication links within the same group via direct communication lines or indirect communication with other units. The turbine response time T is set based on the distance between turbines within the group. s,t (The unit's response time to active power commands is set according to the manufacturer's manual) and the maximum wake conduction time T s,d(Maximum distance divided by cut-in wind speed and then multiplied by a margin of 1.2). Initial axial induction factor control commands are given to each unit, assuming the time is t0.

[0068] Step S02: Issue the initial control command and wait for the action response time T. s,t Then, that is, t = t0 + T s,t Measure and calculate the power generation of each wind turbine And calculate the difference in the value of the fan before and after the control command is issued. The difference between the axial induction factor and the control object The partial derivative of the power generation of each unit with respect to its own induction factor is calculated using formula (3).

[0069] Step S03: at t0+T s,t <t<t0+T s,d At that time, based on the wake wind speed prediction model, combined with formulas (4) and (5), the change in active power of each unit after the effect of this induction factor is predicted. The partial derivative of the axial induction factor of the downwind fan to the upwind fan is calculated by formula (3), and the new axial induction factor of each fan is calculated by formula (2). The difference between the control command and the fan under the predicted wake is calculated. The difference between the axial induction factor and the control object The partial derivatives of the power generation of each wind turbine with respect to its own induction factor are calculated using formula (3).

[0070] Step S04: Repeat step S03 until the axial induction factor values ​​of each fan converge. Record the calculation time as τ at this point. The time at this point is t = t0 + T. s,t +τ, update the partial derivatives of the latest power generation of each unit with respect to its own induction factor before step S02 and after step S03 by formula (3).

[0071] Step S05: At t = t0 + T s,d At +τ, the axial induction factor obtained in step three has caused a wake that has been transmitted to the downwind turbine. At this point, the power generation of each turbine is measured and calculated. The partial derivative of the axial induction factor of the downwind unit to the upwind unit is calculated by formula (3), and the new axial induction factor of each unit is updated by formula (2), and a new control command is issued.

[0072] Step S06: Repeat steps S01-S05 until the wind farm system converges to the maximum power point.

[0073] This embodiment applies the wake prediction model to the distributed MPPT control of the wind field layer based on gradient ascent, which significantly improves the convergence speed of the gradient ascent method, realizes rapid tracking of the maximum power point, and retains the tracking accuracy of the original gradient ascent method.

[0074] Example 2

[0075] Embodiment 2 of this disclosure introduces a maximum power point tracking control system for wind turbines based on wake prediction.

[0076] like Figure 4 The maximum power point tracking control system for wind turbines based on wake prediction, as shown, includes:

[0077] The acquisition module is configured to acquire the wind turbine's power generation and axial induction factor at the current moment and after the wind turbine's action response time, respectively.

[0078] The calculation module is configured to calculate the difference in power generation of the wind turbine and the difference in axial induction factor, and obtain the partial derivative of the power generation of the wind turbine with respect to the axial induction factor.

[0079] The prediction module is configured to obtain the predicted wake of the wind turbine at the moment between the wind turbine's action response time and the maximum wake conduction time, by combining a preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor.

[0080] The control module is configured to iteratively calculate the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor based on the obtained predicted wake, until the obtained axial induction factor value of the wind turbine converges, thereby obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

[0081] The detailed steps are the same as those of the maximum power point tracking control method for wind turbines based on wake prediction provided in Example 1, and will not be repeated here.

[0082] Example 3

[0083] Embodiment 3 of this disclosure provides a computer-readable storage medium.

[0084] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the maximum power point tracking control method for wind turbines based on wake prediction as described in Embodiment 1 of this disclosure.

[0085] The detailed steps are the same as those of the maximum power point tracking control method for wind turbines based on wake prediction provided in Example 1, and will not be repeated here.

[0086] Example 4

[0087] Embodiment 4 of this disclosure provides an electronic device.

[0088] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the maximum power point tracking control method for wind turbines based on wake prediction as described in Embodiment 1 of this disclosure.

[0089] The detailed steps are the same as those of the maximum power point tracking control method for wind turbines based on wake prediction provided in Example 1, and will not be repeated here.

[0090] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

[0091] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A maximum power point tracking control method for wind turbines based on wake prediction, characterized in that, include: The wind turbine's power generation and axial induction factor are obtained at the current moment and after the wind turbine's action response time, respectively. Calculate the difference in wind turbine power generation and the difference in axial induction factor to obtain the partial derivative of wind turbine power generation with respect to axial induction factor; At the moment between the wind turbine's action response time and the maximum wake conduction time, the predicted wake of the wind turbine is obtained by combining the preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor. Based on the predicted wake, the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor are iteratively calculated until the obtained axial induction factor value of the wind turbine converges, thus obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

2. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 1, characterized in that, The axial induction factor is iteratively updated using a fixed-step maximum power point tracking control method, thus obtaining... Among them, a i (k) represents the axial induction factor of the i-th wind turbine at the current time, k represents the current time, K represents the step size, j represents the j-th wind turbine, and i∈(0,n) and j∈(i,n), n represents the number of wind turbines in the wind turbine unit.

3. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 2, characterized in that, The iterative update process of the axial induction factor is only related to the downwind fan, that is, it yields... Among them, the partial derivative of the wind turbine's power generation with respect to the axial induction factor at the current moment. for 4. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 1, characterized in that, The preset wake wind speed prediction model adopts a wake wind speed change model. The wake wind speed change is the prediction of wind speed during the decrease and recovery process of the downwind wind speed of the wind turbine, that is, the wind speed change coefficient s is obtained. i (d): Where d is the distance from the downwind side of the i-th fan to the unit, and C T,i (t0) is the thrust coefficient of the i-th fan at time t0, and R is the sweep radius of the i-th fan.

5. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 4, characterized in that, The wind speed v at the downwind distance d of the i-th wind turbine x (d) is: v x (d)=s i (d)v ∞ ; Among them, v ∞ This represents the actual wind speed at a distance d when there is no fan.

6. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 1, characterized in that, In the process of calculating the maximum power point of the wind turbine, at the moment between the wind turbine's action response time and the maximum wake conduction time, the predicted axial induction factor is obtained through the wake wind speed prediction model. Combined with the change in the active power generated by each wind turbine, the partial derivative of the axial induction factor of the downwind turbine with respect to the upwind turbine is obtained, and the axial induction factor of the wind turbine is calculated. The calculation is repeated iteratively until the numerical value of the axial induction factor of the wind turbine converges.

7. The maximum power point tracking control method for wind turbines based on wake prediction as described in claim 6, characterized in that, At the moment when the axial induction factor of the wind turbine converges, the power generation of the wind turbine is obtained based on the axial induction factor, thus completing the maximum power point tracking control of the wind turbine.

8. A maximum power point tracking control system for wind turbines based on wake prediction, characterized in that, include: The acquisition module is configured to acquire the wind turbine's power generation and axial induction factor at the current moment and after the wind turbine's action response time, respectively. The calculation module is configured to calculate the difference in power generation of the wind turbine and the difference in axial induction factor, and obtain the partial derivative of the power generation of the wind turbine with respect to the axial induction factor. The prediction module is configured to obtain the predicted wake of the wind turbine at the moment between the wind turbine's action response time and the maximum wake conduction time, by combining a preset wake wind speed prediction model and the partial derivative of the wind turbine's power generation with respect to the axial induction factor. The control module is configured to iteratively calculate the wind turbine power generation, the partial derivative of the wind turbine power generation with respect to the axial induction factor, and the axial induction factor based on the obtained predicted wake, until the obtained axial induction factor value of the wind turbine converges, thereby obtaining the maximum power point of the wind turbine and completing the maximum power point tracking control of the wind turbine based on wake prediction.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the maximum power point tracking control method for wind turbines based on wake prediction as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the maximum power point tracking control method for wind turbines based on wake prediction as described in any one of claims 1-7.

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