Tower drum all-wind-direction active control system and control method under extreme wind conditions

Abstract

The invention discloses a tower drum all-wind-direction active control system and method under extreme wind regions.The system comprises an environment monitoring module, a control module, an airflow adjusting module and an air blowing and sucking device which are connected in sequence, and the environment monitoring module is used for collecting wind field data of the environment where a tower drum is located in real time; the control module is used for performing wind field short-time prediction through a generative adversarial network model based on the wind field data, and determining a blowing and sucking air control area and blowing and sucking air flow intensity through a deep reinforcement learning model based on the real-time wind field data and wind field prediction data; and the airflow adjusting module is used for controlling the air blowing and sucking device based on the air blowing and sucking control area and the air blowing and sucking airflow intensity determined by the control module so as to perform air blowing and sucking control on the surface airflow of the tower drum and realize full-wind-direction active airflow control of the tower drum. The wind direction self-adaptive adjustment, the active airflow control and the dynamic rigidity adjustment can be realized under the extreme wind condition, so that the wind resistance of the tower drum is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of active tower control, and in particular to an active control system and a control method for all wind directions of a tower under extreme wind conditions. Background Art

[0002] Wind power generation is an important part of the development of global renewable energy, and the stability and durability of wind power towers are directly related to the long-term reliable operation of wind turbines. Towers usually need to withstand complex external environmental influences, including wind speed, wind direction, temperature fluctuations and extreme climate events (such as hurricanes, typhoons, etc.), among which wind-induced vibration is particularly prominent, seriously affecting the safety and service life of the tower.

[0003] At present, the technology of tower wind vibration control mainly focuses on passive and semi-active structural control, including increasing structural stiffness, installing damping systems, installing wind shields or guide devices. The above methods have improved the tower vibration problem to a certain extent, but they are mostly passive responses and lack dynamic adaptability. Summary of the invention

[0004] The present invention aims to solve the technical problems in the related art at least to a certain extent. To this end, the first purpose of the present invention is to provide a tower full wind direction active control system under extreme wind conditions, which can realize wind direction adaptive adjustment, active airflow control and dynamic stiffness adjustment under extreme wind conditions, optimize the aerodynamic performance of the tower, enhance the wind resistance of the tower, reduce vortex induced resonance, and when the wind condition reaches the active control threshold, the tower stiffness is increased by shape memory alloy, which can extend the service life of wind power, and has significant economic benefits and carbon reduction effects.

[0005] The second object of the present invention is to provide a method for actively controlling the full wind direction of a tower under extreme wind conditions.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] An active control system for all wind directions of a tower under extreme wind conditions, comprising:

[0008] Environmental monitoring module, used to collect wind field data of the tower environment in real time;

[0009] A control module is used to perform short-term wind field prediction based on wind field data through a generative adversarial network model, and determine the blowing and suction control area and the blowing and suction airflow intensity through a deep reinforcement learning model based on real-time wind field data and wind field prediction data;

[0010] The airflow regulating module and the blowing and suction device, the airflow regulating module is used to control the blowing and suction device based on the blowing and suction control area and the blowing and suction airflow intensity determined by the control module, so as to perform blowing and suction control on the airflow on the surface of the tower and realize active airflow control in all wind directions of the tower.

[0011] Preferably, the environment monitoring module includes a wind speed sensor, a wind direction sensor and a temperature sensor, which are respectively used to collect the wind speed, wind direction and temperature of the environment in which the tower is located.

[0012] Preferably, the system further comprises:

[0013] The stiffness adjustment module and the control module are also used to control the stiffness adjustment module to adjust the tower stiffness by controlling the change of the shape memory alloy through temperature when the wind speed change exceeds the corresponding set threshold.

[0014] Preferably, the air blowing and suction device comprises a plurality of air blowing and suction ports, and the plurality of air blowing and suction ports are arranged at equal intervals along the circumferential direction of the tower and are evenly arranged along the height direction of the tower.

[0015] Preferably, the blowing and suction control area is determined by determining the arrangement of the blowing and suction ports.

[0016] Preferably, the circumferential arrangement spacing of the multiple blowing and suction air ports is no more than 2m, the arrangement spacing along the height direction of the tower is no more than 20m, and the height of the blowing and suction air ports arranged circumferentially at the bottom layer from the bottom of the tower is no more than 30m.

[0017] Preferably, the shape memory alloy is circumferentially arranged on the outer surface of the tower, the circumferential arrangement spacing of the shape memory alloy is not greater than one-third of the circumference of the tower, and the longitudinal height of the shape memory alloy is not less than half of the height of the tower.

[0018] Preferably, the shape memory alloy forms a protrusion after deformation, and the shape of the protrusion includes any one or more combinations of rectangle, triangle, and arc.

[0019] Preferably, the system also includes a remote monitoring system, which is used to monitor the working status of the tower in real time so as to remotely control the airflow adjustment module and the stiffness adjustment module to cope with extreme wind conditions.

[0020] To achieve the above object, the second aspect of the present invention provides a method for actively controlling the full wind direction of a tower under extreme wind conditions, which is applied to the above-mentioned active control system for the full wind direction of a tower, comprising:

[0021] Step S1: training a deep reinforcement learning model and a generative adversarial network model;

[0022] Step S2: Arrange a wind speed sensor, a wind direction sensor and a temperature sensor on the tower to collect wind field data, and transmit the real-time collected wind field data to the control module;

[0023] Step S3: the control module performs short-term prediction of the wind farm based on the real-time detected wind farm data through the trained generative adversarial network model;

[0024] Step S4: According to the real-time wind field data and wind field prediction data, the control module automatically determines the blowing and inhaling air control area and the blowing and inhaling air flow intensity through the trained deep reinforcement learning model;

[0025] Step S5: controlling the blowing and suction of the blowing and suction device through the airflow regulating module to achieve the blowing in and suction out control of the airflow on the tower surface;

[0026] Step S6: When the wind field changes, loop steps S2 to S5; wherein, when the wind speed change exceeds the corresponding set threshold, the control module adjusts the stiffness of the tower through the shape memory alloy to cope with extreme wind conditions.

[0027] The present invention has at least the following technical effects:

[0028] The present invention proposes an active control technology for wind power tower based on full wind direction blowing and suction control, combined with aerodynamic control and tower stiffness adjustment technology, breaking through the design limitations of traditional rigid towers, and forming an active control strategy with real-time adaptability. The core advantages of this solution are wind direction adaptive adjustment, active airflow control and dynamic stiffness adjustment, which can achieve efficient adaptation and vibration suppression to extreme wind conditions, and provide innovative technical solutions for the long-term safe operation of wind turbines. At the same time, it has important engineering value in reducing maintenance costs, extending equipment life and improving wind power utilization. Therefore, under extreme wind conditions, the present invention can achieve wind direction adaptive adjustment, active airflow control and dynamic stiffness adjustment, optimize the aerodynamic performance of the tower, enhance the wind resistance of the tower, and reduce vortex induced resonance. When the wind condition reaches the active control threshold, the tower stiffness is increased by shape memory alloy, which can extend the service life of wind power, and has significant economic benefits and carbon reduction effects.

[0029] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure is a structural block diagram of a tower full wind direction active control system under extreme wind conditions according to an embodiment of the present invention.

[0031] Figure 2 This is an example diagram of the arrangement of the blowing and suction ports according to an embodiment of the present invention.

[0032] Figure 3The figure is an example diagram of the arrangement and appearance of the shape memory alloy according to an embodiment of the present invention.

[0033] Figure 4 The figure is a flow chart of a method for actively controlling all wind directions of a tower under extreme wind conditions according to an embodiment of the present invention.

[0034] Figure 5 The figure is a simplified flow chart of a method for actively controlling all wind directions of a tower according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0036] The tower full wind direction active control system and control method under extreme wind conditions of this embodiment are described below with reference to the accompanying drawings.

[0037] Figure 1 The figure is a structural block diagram of a tower full wind direction active control system under extreme wind conditions according to an embodiment of the present invention.

[0038] like Figure 1 As shown, the active control system for all wind directions of the tower under extreme wind conditions includes an environment monitoring module, a control module, an airflow adjustment module and a blowing and suction device which are connected in sequence.

[0039] Among them, the environmental monitoring module is used to collect the wind field data of the tower environment in real time; the control module is used to make short-term wind field predictions based on the wind field data through a generative adversarial network model, and determine the blowing and suction control area and the blowing and suction airflow intensity through a deep reinforcement learning model based on the real-time wind field data and wind field prediction data; the airflow regulation module is used to control the blowing and suction device based on the blowing and suction control area and the blowing and suction airflow intensity determined by the control module, so as to perform blowing and suction control on the airflow on the surface of the tower and realize active airflow control in all wind directions of the tower.

[0040] In this embodiment, the deep reinforcement learning model uses tensorflow or pytorch (tensorflow and pytorch are both deep learning frameworks) to build the framework, the wind farm short-term prediction model uses a generative adversarial network model, and the training data comes from the wind tunnel laboratory and the measured data under actual working conditions.

[0041] Under actual working conditions, the environmental monitoring modules are mostly arranged in the cabin, including wind speed sensors, wind direction sensors and temperature sensors, which are used to monitor the wind speed, wind direction, temperature and other data in the environment where the tower is located in real time.

[0042] The data collected by the environmental monitoring module will be input into the control module. The control module will make a short-term prediction of the future wind field through the trained model and give active control instructions, including determining the arrangement of the blowing and suction ports and the intensity of the blowing and suction airflow. Among them, determining the arrangement of the blowing and suction ports can determine the blowing and suction control area.

[0043] The control module transmits the active control instruction to the airflow regulation module, and the airflow regulation module starts to actively control the blowing and suction device to reduce the lift resistance of the tower.

[0044] When the wind field conditions change significantly, the environmental monitoring module will output the collected new real-time data to the control module. The control module will generate new active control instructions in real time and output them to the airflow adjustment module for blowing and suction control to reduce the lift resistance of the tower.

[0045] The control module is also used to output control instructions to the stiffness adjustment module when the wind speed change exceeds the corresponding set threshold to assist in active control of blowing and suction, specifically by controlling the change and stiffness of the shape memory alloy through temperature to achieve tower stiffness adjustment.

[0046] In this embodiment, the air blowing and suction device includes a plurality of air blowing and suction ports, and the plurality of air blowing and suction ports are arranged at equal intervals along the circumferential direction of the tower and are evenly arranged along the height direction of the tower.

[0047] Among them, the circumferential arrangement spacing of multiple blowing and suction air outlets is not greater than 2m, the arrangement spacing along the height direction of the tower is not greater than 20m, and the height of the blowing and suction air outlets arranged circumferentially at the bottom layer from the bottom of the tower is not greater than 30m.

[0048] Figure 2 This is an example diagram of the arrangement of the blowing and suction ports according to an embodiment of the present invention. Figure 2 The mark 3-1 in the middle indicates the air inlet and outlet. Figure 2 The circumferential arrangement spacing of the central annular blowing and suction air ports is one-third of the tower circumference. The longitudinal blowing and suction air ports of a 90-meter-high tower are arranged at 30m, 50m, 60m, 70m and 80m along the tower height.

[0049] In this embodiment, the position of the air inlet and outlet is set to be adjustable in all wind directions, and the control module can adaptively control the airflow adjustment module according to the change of wind direction to control the air inlet and outlet device to automatically adjust the relative relationship between the air blowing position and the air inlet position. Therefore, the control module in this embodiment can automatically adjust the airflow direction and airflow intensity according to the real-time monitored environmental data.

[0050] In this embodiment, the shape memory alloy is arranged circumferentially on the outer surface of the tower, the circumferential arrangement spacing of the shape memory alloy is not greater than one-third of the circumference of the tower, and the longitudinal height of the shape memory alloy is not less than half of the height of the tower. In addition, the outer surface of the shape memory alloy is deformed to form a bulge, and the shape of the bulge includes any one or more combinations of rectangle, triangle, and arc. In this embodiment, the stiffness adjustment module can control the shape change of the shape memory alloy according to the temperature change of the shape memory alloy, thereby realizing the stiffness adjustment of the tower.

[0051] Figure 3 The figure is an example diagram of the arrangement and appearance of the shape memory alloy according to an embodiment of the present invention. Figure 3 In the embodiment, the circumferential arrangement spacing of the shape memory alloy is one sixth of the circumference of the tower. When the tower height is 90m, the height of the shape memory alloy is 60m, which is not less than half of the tower height. A protrusion is formed on the outer surface of the shape memory alloy.

[0052] Optionally, the system also includes a remote monitoring system, which is used to monitor the working status of the tower in real time so as to remotely control the airflow adjustment module and the stiffness adjustment module to cope with extreme wind conditions.

[0053] Specifically, the remote monitoring system can detect the actual operation status of the tower in real time, and the staff can manually control the airflow adjustment module and the stiffness adjustment module in real time to further ensure the smooth operation of the system.

[0054] Furthermore, the present invention also provides a method for actively controlling the full wind direction of a tower under extreme wind conditions, which is applied to the above-mentioned active control system for the full wind direction of the tower.

[0055] Figure 4 FIG. 1 is a flow chart of a method for actively controlling the full wind direction of a tower under extreme wind conditions according to an embodiment of the present invention. Figure 4 As shown, the method includes:

[0056] Step S1: Train the deep reinforcement learning model and the generative adversarial network model.

[0057] Step S2: Arrange a wind speed sensor, a wind direction sensor and a temperature sensor on the tower to collect wind field data, and transmit the real-time collected wind field data to the control module.

[0058] Step S3: The control module performs short-term prediction of the wind field based on the real-time detected wind field data through the trained generative adversarial network model.

[0059] Step S4: Based on the real-time wind field data and wind field prediction data, the control module automatically determines the blowing and suction control area and the blowing and suction airflow intensity through the trained deep reinforcement learning model.

[0060] Step S5: controlling the blowing and suction of the blowing and suction device through the airflow regulating module to achieve the blowing in and sucking out control of the airflow on the tower surface.

[0061] Step S6: When the wind field changes, loop steps S2 to S5; wherein, when the wind speed change exceeds the corresponding set threshold, the control module adjusts the stiffness of the tower through the shape memory alloy to cope with extreme wind conditions.

[0062] Figure 5 The figure is a simplified flow chart of a method for actively controlling all wind directions of a tower according to an embodiment of the present invention. Figure 4 The steps in can be simply summarized as Figure 5 The six steps in the process mainly include model training, sensor layout and data acquisition, short-term wind farm prediction, model feedback, active control and stiffness adjustment.

[0063] It should be noted that the specific implementation of the method for actively controlling the full wind direction of the tower under extreme wind conditions in an embodiment of the present invention can be found in the specific implementation of the active control system for the full wind direction of the tower under extreme wind conditions mentioned above. To avoid redundancy, it will not be repeated here.

[0064] In summary, the present invention proposes an active control technology for wind power tower based on full wind direction blowing and suction control, combined with aerodynamic control and tower stiffness adjustment technology, breaking through the design limitations of traditional rigid towers, and forming an active control strategy with real-time adaptability. The core advantages of this solution are wind direction adaptive adjustment, active airflow control and dynamic stiffness adjustment, which can achieve efficient adaptation and vibration suppression to extreme wind conditions, and provide innovative technical solutions for the long-term safe operation of wind turbines. At the same time, it has important engineering value in reducing maintenance costs, extending equipment life and improving wind power utilization. Therefore, under extreme wind conditions, the present invention can achieve wind direction adaptive adjustment, active airflow control and dynamic stiffness adjustment, optimize the aerodynamic performance of the tower, enhance the wind resistance of the tower, reduce vortex induced resonance, and when the wind condition reaches the active control threshold, the tower stiffness is increased by shape memory alloy, which can extend the service life of wind power, and has significant economic benefits and carbon reduction effects.

[0065] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0066] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. An active control system for all wind directions of a tower under extreme wind conditions, characterized in that: include: Environmental monitoring module, used to collect wind field data of the tower environment in real time; A control module is used to perform short-term wind field prediction based on wind field data through a generative adversarial network model, and determine the blowing and suction control area and the blowing and suction airflow intensity through a deep reinforcement learning model based on real-time wind field data and wind field prediction data; The airflow regulating module and the blowing and suction device, the airflow regulating module is used to control the blowing and suction device based on the blowing and suction control area and the blowing and suction airflow intensity determined by the control module, so as to perform blowing and suction control on the airflow on the surface of the tower and realize active airflow control in all wind directions of the tower.

2. The tower full wind direction active control system under extreme wind conditions as claimed in claim 1, characterized in that: The environmental monitoring module includes a wind speed sensor, a wind direction sensor and a temperature sensor, which are respectively used to collect the wind speed, wind direction and temperature of the environment in which the tower is located.

3. The tower full wind direction active control system under extreme wind conditions as claimed in claim 1, characterized in that: Also includes: The stiffness adjustment module and the control module are also used to control the stiffness adjustment module to adjust the tower stiffness by controlling the change of the shape memory alloy through temperature when the wind speed change exceeds the corresponding set threshold.

4. The active control system for all wind directions of a tower under extreme wind conditions as claimed in claim 3, characterized in that: The air blowing and suction device comprises a plurality of air blowing and suction ports, which are arranged at equal intervals along the circumferential direction of the tower and are evenly arranged along the height direction of the tower.

5. The tower full wind direction active control system under extreme wind conditions as claimed in claim 4, characterized in that: The blowing and suction control area is determined by determining the arrangement of the blowing and suction ports.

6. The tower full wind direction active control system under extreme wind conditions as claimed in claim 4, characterized in that: The circumferential arrangement spacing of multiple blowing and suction air outlets shall not exceed 2m, and the arrangement spacing along the height direction of the tower shall not exceed 20m. The height of the blowing and suction air outlets arranged circumferentially on the bottom layer shall not exceed 30m from the bottom of the tower.

7. The active control system for all wind directions of a tower under extreme wind conditions as claimed in claim 3, characterized in that: The shape memory alloy is arranged circumferentially on the outer surface of the tower, the circumferential arrangement spacing of the shape memory alloy is not greater than one-third of the circumference of the tower, and the longitudinal height of the shape memory alloy is not less than half of the height of the tower.

8. The tower full wind direction active control system under extreme wind conditions as claimed in claim 7, characterized in that: The shape memory alloy forms a protrusion after deformation, and the shape of the protrusion includes any one or more combinations of rectangle, triangle, and arc.

9. The active control system for all wind directions of a tower under extreme wind conditions according to any one of claims 3 to 8, characterized in that: It also includes a remote monitoring system, which is used to monitor the working status of the tower in real time so as to remotely control the airflow adjustment module and the stiffness adjustment module to cope with extreme wind conditions.

10. A method for actively controlling the full wind direction of a tower under extreme wind conditions, applied to the active control system for the full wind direction of a tower as claimed in any one of claims 1 to 9, characterized in that: include: Step S1: training a deep reinforcement learning model and a generative adversarial network model; Step S2: Arrange a wind speed sensor, a wind direction sensor and a temperature sensor on the tower to collect wind field data, and transmit the real-time collected wind field data to the control module; Step S3: the control module performs short-term prediction of the wind farm based on the real-time detected wind farm data through the trained generative adversarial network model; Step S4: According to the real-time wind field data and wind field prediction data, the control module automatically determines the blowing and inhaling air control area and the blowing and inhaling air flow intensity through the trained deep reinforcement learning model; Step S5: controlling the blowing and suction of the blowing and suction device through the airflow regulating module to achieve the blowing in and suction out control of the airflow on the tower surface; Step S6: When the wind field changes, loop steps S2 to S5; wherein, when the wind speed change exceeds the corresponding set threshold, the control module adjusts the stiffness of the tower through the shape memory alloy to cope with extreme wind conditions.

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