CN116696658A - Predictive control method, predictive control system, electronic equipment and readable storage medium for floating wind turbine - Google Patents

Predictive control method, predictive control system, electronic equipment and readable storage medium for floating wind turbine Download PDF

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Publication number
CN116696658A
CN116696658A CN202310760724.5A CN202310760724A CN116696658A CN 116696658 A CN116696658 A CN 116696658A CN 202310760724 A CN202310760724 A CN 202310760724A CN 116696658 A CN116696658 A CN 116696658A
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China
Prior art keywords
wind turbine
prediction
floating wind
result
model
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CN202310760724.5A
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Chinese (zh)
Inventor
周乐
沈昕
竺晓程
欧阳华
杜朝辉
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Shanghai Jiaotong University
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Shanghai Jiaotong University
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Priority to CN202310760724.5A priority Critical patent/CN116696658A/en
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/10—Purpose of the control system
    • F05B2270/101—Purpose of the control system to control rotational speed (n)
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/10—Purpose of the control system
    • F05B2270/103—Purpose of the control system to affect the output of the engine
    • F05B2270/1033—Power (if explicitly mentioned)
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/40—Type of control system
    • F05B2270/404—Type of control system active, predictive, or anticipative
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Wind Motors (AREA)

Abstract

The application provides a prediction control method, a system, equipment and a storage medium of a floating wind turbine, wherein an environment prediction model is obtained in advance through acquisition and training of historical environment data based on a big data artificial intelligent training technology and a digital twin technology, a corresponding wind turbine state prediction model is obtained through digital twin according to the environment prediction model, and the performance and fatigue damage of the wind turbine are predicted in a targeted mode and a control scheme is formulated through the wind turbine state prediction model. The control scheme provided by the application ensures the safe operation of the wind turbine while taking the power output performance of the wind turbine into consideration, has good economic and social benefits, and has popularization value.

Description

Predictive control method, predictive control system, electronic equipment and readable storage medium for floating wind turbine
Technical Field
The application relates to the technical field of big data predictive control, and particularly discloses a predictive control method, a predictive control system, electronic equipment and a readable storage medium of a floating wind turbine.
Background
In recent years, with the deep penetration of the green environment protection concept, the popularization of wind power is increased, and in particular, the wind power at sea is increased: as a green renewable energy source with great exploitation value, the development of offshore wind energy is one of the most effective energy solutions for coping with climate change, relieving energy shortage and realizing sustainable development.
Unlike land wind turbines, wind turbines used for offshore wind power need to work continuously in a complex marine environment, and particularly for floating wind turbines, six degrees of freedom motion in three directions of an X axis, a Y axis and a Z axis exist under the combined action of wind, waves and currents. The existence of six-degree-of-freedom motion causes the risk of periodical deformation of the fan blades and the fan towers of the floating wind turbine caused by the reciprocating motion of the floating platform, so that the fatigue damage of mechanical equipment is increased, and the fatigue life of the mechanical equipment is reduced. The existing wind turbine control strategies are mostly developed around transient characteristics, attention is focused on aerodynamic characteristics and output power of the wind turbine, and consideration of fatigue damage possibly generated by the wind turbine in a long period of time in the future is lacking.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present application provides a method, system, electronic device and readable storage medium for predictive control of a floating wind turbine.
The first aspect of the application provides a predictive control method of a floating wind turbine, specifically comprising:
acquiring historical environmental data of the position of the floating wind turbine;
training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data;
generating a wind turbine state prediction model associated with the environment prediction model based on digital twinning according to the environment prediction model, wherein the wind turbine state prediction model comprises a aerodynamic force prediction sub-model of the floating wind turbine, a hydrodynamic force prediction sub-model of the floating wind turbine and a structure prediction sub-model of the floating wind turbine;
based on the prediction demand, acquiring prediction data required by an environment prediction model;
generating environment prediction data corresponding to the prediction demand according to the prediction data and the environment prediction model;
generating a power output prediction result and a structure deformation prediction result corresponding to the floating wind turbine according to the environment prediction data and the wind turbine state prediction model;
judging whether the power prediction result and the structure deformation prediction result belong to a preset range, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structure deformation prediction result does not belong to the corresponding preset range.
In a possible implementation manner of the first aspect, the prediction control method further includes:
generating a fatigue damage assessment result corresponding to the floating wind turbine according to the structure deformation prediction result;
judging whether the power prediction result and the fatigue damage evaluation result belong to a preset range, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the fatigue damage evaluation result does not belong to the corresponding preset range.
In a possible implementation of the first aspect described above, the control strategy comprises controlling a blade twist direction of the floating wind turbine and controlling a rotor rotational speed of the floating wind turbine.
In a possible implementation manner of the first aspect, the prediction control method further includes:
under the condition that any one of the power prediction result and the fatigue damage evaluation result does not belong to a corresponding preset range, executing the control strategy, and then re-executing the prediction step and obtaining the power output prediction result and the structure deformation prediction result after the control strategy is updated;
the control strategy comprises the step of controlling the blade torsion direction and/or the wind wheel rotation speed of the floating wind turbine so as to minimize the fatigue damage evaluation result and/or optimize the power prediction result.
In a possible implementation manner of the first aspect, the prediction control method further includes:
and under the condition that the power prediction result and the fatigue damage evaluation result belong to the corresponding preset ranges, the control strategy of the floating wind turbine is kept unchanged.
In a possible implementation of the first aspect, the structural deformation prediction result includes a structural deformation prediction of a wind turbine blade of the floating wind turbine and a structural deformation prediction of a wind turbine tower of the floating wind turbine;
the fatigue damage evaluation result comprises fatigue damage evaluation of a fan blade of the floating wind turbine and fatigue damage evaluation of a fan tower of the floating wind turbine.
In a possible implementation of the first aspect, the environmental prediction model is obtained based on data training of an artificial intelligence prediction model;
the wind turbine state prediction model is obtained through data training based on an artificial intelligent prediction model and/or is obtained based on a mechanism model.
A second aspect of the present application provides a predictive control system for a floating wind turbine, which is applied to the predictive control method for a floating wind turbine provided in the first aspect;
the predictive control system includes:
the acquisition unit is used for acquiring historical environment data of the position where the floating wind turbine is located;
the training unit is used for training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data;
a digital twin unit for generating a wind turbine state prediction model associated with the environmental prediction model based on the digital twin according to the environmental prediction model, wherein the wind turbine state prediction model includes a aerodynamic force predictor model of the floating wind turbine, a hydrodynamic force predictor model of the floating wind turbine, and a structural predictor model of the floating wind turbine;
the demand acquisition unit is used for acquiring prediction data required by the environment prediction model based on the prediction demand;
the environment prediction unit is used for generating environment prediction data corresponding to the prediction demand according to the prediction data and the environment prediction model;
the state prediction unit is used for generating a power output prediction result corresponding to the floating wind turbine and a corresponding structural deformation prediction result according to the environment prediction data and the wind turbine state prediction model;
and the judging control unit is used for judging whether the power prediction result and the structure deformation prediction result belong to a preset range or not, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structure deformation prediction result does not belong to the corresponding preset range.
A third aspect of the present application provides an electronic device comprising: a memory for storing a processing program; and the processor is used for realizing the predictive control method of the floating wind turbine provided by the first aspect when executing the processing program.
A fourth aspect of the present application provides a computer readable storage medium having a processing program stored thereon, which when executed by a processor, implements the method for predictive control of a floating wind turbine as provided in the first aspect.
Compared with the prior art, the application has the following beneficial effects:
according to the technical scheme provided by the application, based on a big data artificial intelligence training technology and a digital twin technology, an environment prediction model is obtained in advance through acquisition and training of historical environment data, a corresponding wind turbine state prediction model is obtained through digital twin according to the environment prediction model, and the performance and fatigue damage of a wind turbine are subjected to targeted prediction and control scheme formulation through the wind turbine state prediction model. The control scheme provided by the application ensures the safe operation of the wind turbine while taking the power output performance of the wind turbine into consideration, has good economic and social benefits, and has popularization value.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart illustrating a predictive control method for a floating wind turbine according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of optimizing a structure deformation prediction result according to an embodiment of the present application.
FIG. 3 illustrates a flow diagram of control strategy execution, according to an embodiment of the present application.
FIG. 4 is a flow chart illustrating another control strategy implementation according to an embodiment of the present application.
FIG. 5 shows a schematic structural diagram of a predictive control system for a floating wind turbine, in accordance with an embodiment of the application.
Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 7 illustrates a schematic structure of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
In order to solve the problem that the fatigue damage possibly generated by the wind turbine in the long period of time in the future is lack of consideration in the existing application scene, in some embodiments of the present application, a predictive control method for a floating wind turbine is provided: the prediction control method can ensure the safe operation of the wind turbine while considering the power output performance of the wind turbine. The above technical scheme of the present disclosure will be specifically explained and illustrated below with reference to examples.
In some embodiments of the application, FIG. 1 illustrates a predictive control method for a floating wind turbine. As shown in fig. 1, the method for controlling the floating wind turbine by prediction specifically includes:
step 101: historical environmental data of the position of the floating wind turbine is obtained. It will be appreciated that, considering that the floating wind turbines are often centralized in a wind farm area defined by the same sea area, in the implementation process of step 101, the historical wind, wave and current data of the wind farm may be obtained as the historical environmental data. The person skilled in the art can also perform data cleaning and data screening on the historical environmental data according to actual needs, so that the influence of special conditions on the periodic change of the marine environment is eliminated, and the method is not limited.
Step 102: and training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data.
Step 103: according to the environment prediction model, a wind turbine state prediction model associated with the environment prediction model is generated based on digital twin. The wind turbine state prediction model comprises a aerodynamic force prediction sub-model of the floating wind turbine, a hydrodynamic force prediction sub-model of the floating wind turbine and a structure prediction sub-model of the floating wind turbine, and the aerodynamic force prediction sub-model, the hydrodynamic force prediction sub-model and the structure prediction sub-model are respectively used for correspondingly predicting power output and structural deformation of the wind turbine.
It is understood that in the above steps 102 to 103, the environmental prediction model may be obtained by training data based on the artificial intelligence prediction model; the wind turbine state prediction model obtained by digital twin can be obtained based on an artificial intelligence prediction model, a mechanism model or a combination of the two, and is not limited herein.
Step 104: based on the prediction demand, prediction data required for the environmental prediction model is acquired. It can be understood that the technical scheme provided by the application does not limit the future time period required to be subjected to predictive control, and the future time period can be in the second level, the minute level, the hour level, the day level and the like. Accordingly, the data amount of the required prediction data needs to be set correspondingly with the magnitude of the future period.
Step 105: and generating environment prediction data corresponding to the prediction demand according to the prediction data and the environment prediction model.
Step 106: and generating a power output prediction result and a structural deformation prediction result corresponding to the floating wind turbine according to the environment prediction data and the wind turbine state prediction model. It will be appreciated that the power data predictors correspond to aerodynamic performance of the floating wind turbine, and the structural deformation predictors need to include structural deformation predictors of the wind turbine blades of the floating wind turbine and structural deformation predictors of the wind turbine towers of the floating wind turbine.
Step 107: judging whether the power prediction result and the structure deformation prediction result belong to a preset range, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structure deformation prediction result does not belong to the corresponding preset range. The control strategy may include controlling a blade twist direction of the floating wind turbine and controlling a rotor rotational speed of the floating wind turbine.
In the embodiment corresponding to the method for controlling the prediction of the floating wind turbine provided in fig. 1, fig. 2 further shows a schematic flow chart of the optimization process for the structure deformation prediction results in steps 106 to 107.
As shown in fig. 2, the prediction control method provided by the present application may further include:
step 201: and generating a fatigue damage evaluation result corresponding to the floating wind turbine according to the structural deformation prediction result. Correspondingly, the fatigue damage evaluation result comprises fatigue damage evaluation of the fan blade of the floating wind turbine and fatigue damage evaluation of the fan tower of the floating wind turbine.
Step 202: judging whether the power prediction result and the fatigue damage evaluation result belong to a preset range or not, and generating a corresponding judgment result.
Step 203: and executing a corresponding control strategy on the floating wind turbine according to the judging result under the condition that any one of the power predicting result and the fatigue damage evaluating result does not belong to a corresponding preset range.
It can be understood that for the floating wind turbine, the existence of six degrees of freedom motion causes the risk of periodical deformation of the fan blade and the fan tower of the floating wind turbine caused by the reciprocating motion of the floating platform, and the periodical deformation is too large, so that the influence on the normal working condition of the floating wind turbine is obviously caused, but the single periodical deformation can possibly damage the mechanical structure of the floating wind turbine within a preset range, and the damage can also seriously influence the normal working condition of the floating wind turbine after accumulating to a certain extent, so that the fatigue damage evaluation result needs to be further generated on the basis of the structural deformation prediction result and whether the fatigue damage evaluation result belongs to the preset normal range is judged, and the normal and safe operation of the floating wind turbine is further ensured.
In the embodiment corresponding to the method for predictive control of a floating wind turbine provided in fig. 1, fig. 3 further shows a schematic flow chart for executing the control strategy in step 107. As shown in fig. 3, the prediction control method provided by the present application may further include:
step 301: and under the condition that any one of the power prediction result and the fatigue damage evaluation result does not belong to a corresponding preset range, executing the control strategy, and then re-executing the prediction step and obtaining the power output prediction result and the structural deformation prediction result after updating the control strategy. Wherein the control strategy comprises the steps of controlling the blade torsion direction and/or the wind wheel rotation speed of the floating wind turbine so as to minimize the fatigue damage evaluation result and/or optimize the power prediction result.
It can be understood that, in any one of the power prediction result and the fatigue damage evaluation result does not belong to the corresponding preset range, the corresponding item needs to be restored to the normal preset range by adjusting the control strategy, and in the adjustment process of the control strategy, multi-objective optimization needs to be developed with the minimum fatigue damage and the optimal aerodynamic performance of the wind turbine as targets, and the optimization result is reevaluated until the power prediction result and the fatigue damage evaluation result are both in the preset range. In the practical application process, a group of alternative pareto fronts can be given after the multi-objective optimization is completed.
In the embodiment corresponding to the method for predictive control of a floating wind turbine provided in fig. 1, fig. 4 further shows another flow chart for executing the control strategy in step 107. As shown in fig. 4, the prediction control method provided by the present application may further include:
step 401: and under the condition that the power prediction result and the fatigue damage evaluation result belong to the corresponding preset ranges, the control strategy of the floating wind turbine is kept unchanged.
It can be appreciated that when the power prediction result and the fatigue damage evaluation result both belong to the corresponding preset ranges, it is indicated that the floating wind turbine is operated in the current state, and the aerodynamic performance is good and there is no risk of fatigue damage in the future period of the desired prediction, in which case the current control strategy is kept unchanged.
In some embodiments of the present application, fig. 5 shows a predictive control system for a floating wind turbine, where the predictive control system for a floating wind turbine is applied to any one of the predictive control methods for a floating wind turbine provided in the foregoing embodiments. Specifically, as shown in fig. 5, the predictive control system of the floating wind turbine includes:
and the obtaining unit 001 is used for obtaining historical environment data of the position where the floating wind turbine is located.
And the training unit 002 is used for training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data.
A digital twin unit 003 for generating a wind turbine state prediction model associated with the environmental prediction model based on the digital twin, in accordance with the environmental prediction model. The wind turbine state prediction model comprises a aerodynamic force prediction sub-model of the floating wind turbine, a hydrodynamic force prediction sub-model of the floating wind turbine and a structure prediction sub-model of the floating wind turbine.
A demand acquisition unit 004 for acquiring prediction data required for the environmental prediction model based on the prediction demand.
The environment prediction unit 005 is configured to generate environment prediction data corresponding to the prediction demand based on the prediction data and the environment prediction model.
The state prediction unit 006 is configured to generate a power output prediction result and a corresponding structural deformation prediction result corresponding to the floating wind turbine according to the environmental prediction data and the wind turbine state prediction model.
The judging control unit 007 is configured to judge whether the power prediction result and the structural deformation prediction result both belong to a preset range, and execute a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structural deformation prediction result does not belong to a corresponding preset range.
It can be understood that the system settings of the acquiring unit 001 to the determining control unit 007 are identical to and correspond to the steps 101 to 107 in the method for predicting and controlling the floating wind turbine according to the foregoing embodiment, and are not described herein.
It is to be appreciated that aspects of the present subject matter can be implemented as a system, method, or program product. Accordingly, aspects of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects that may be commonly referred to herein as a "circuit," module, "or" platform.
It will be appreciated by those skilled in the art that the elements or modules or steps of the application described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they are stored in a storage medium and, in some cases, executed by computing devices, in a different order than that shown or described, or they may be implemented as individual integrated circuit modules, or as individual integrated circuit modules.
Fig. 6 shows a schematic structural diagram of an electronic device for implementing the predictive control method for a floating wind turbine according to the previous embodiments, according to some embodiments of the present application. An electronic apparatus 600 implemented according to the implementation method in the present embodiment is described in detail below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not be construed as limiting the functionality and scope of use of any embodiment of the present application.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. The construction of the electronic device 600 may include, but is not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting the different platform components (including the storage unit 620 and the processing unit 610), a display unit 640 (for providing a human-machine interaction interface), etc.
The storage unit 620 stores a program code, which may be executed by the processing unit 610, so that the processing unit 610 implements the predictive control method of the floating wind turbine provided in the foregoing embodiment.
The storage unit 620 may include readable media in the form of volatile storage units, such as random access units (RAM) 6201 and/or cache storage units 6202, and may further include read only memory units (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may represent one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an image acceleration port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., an external camera, an external microphone, a bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., a router, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage platforms, and the like.
In some embodiments of the present application, there is also provided a computer readable storage medium having a computer program stored thereon, which when executed by a processor is capable of implementing the predictive control method for a floating wind turbine according to the foregoing embodiments.
Although this embodiment does not specifically recite other specific embodiments, in some possible implementations, various aspects described in the technical solutions of the present application may also be implemented in a form of a program product, which includes program code for causing a terminal device to execute the steps of the embodiments in the various embodiments of the technical solutions of the present application when the program product is run on the terminal device.
Fig. 7 illustrates a schematic diagram of a computer-readable storage medium, according to some embodiments of the application. As shown in fig. 7, a program product 800 for implementing the above method in an embodiment according to the present application is described, which may employ a portable compact disc read-only memory (CD-ROM) and comprise program code and may be run on a terminal device, such as a personal computer. Of course, the program product produced according to the present embodiment is not limited thereto, and in the technical solution of the present application, the readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in combination with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, locally on the user's device, as a stand-alone software package, locally on the user's computing device on a remote computing device, or entirely on a remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, through the technical scheme provided by the application, based on the big data artificial intelligence training technology and the digital twin technology, the environment prediction model is obtained in advance through the collection and training of the historical environment data, the corresponding wind turbine state prediction model is obtained through digital twin according to the environment prediction model, and the performance and fatigue damage of the wind turbine are specifically predicted and the control scheme is formulated through the wind turbine state prediction model. The control scheme provided by the application ensures the safe operation of the wind turbine while taking the power output performance of the wind turbine into consideration, has good economic and social benefits, and has popularization value.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.

Claims (10)

1. A predictive control method for a floating wind turbine, comprising:
acquiring historical environment data of the position of the floating wind turbine;
training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data;
generating a wind turbine state prediction model associated with the environment prediction model based on digital twinning according to the environment prediction model, wherein the wind turbine state prediction model comprises a aerodynamic force prediction sub-model of the floating wind turbine, a hydrodynamic force prediction sub-model of the floating wind turbine and a structure prediction sub-model of the floating wind turbine;
based on the prediction demand, acquiring prediction data required by the environment prediction model;
generating environment prediction data corresponding to the prediction demand according to the prediction data and the environment prediction model;
generating a power output prediction result and a corresponding structural deformation prediction result corresponding to the floating wind turbine according to the environment prediction data and the wind turbine state prediction model;
judging whether the power prediction result and the structural deformation prediction result belong to a preset range, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structural deformation prediction result does not belong to the corresponding preset range.
2. The predictive control method for a floating wind turbine of claim 1, further comprising:
generating a fatigue damage assessment result corresponding to the floating wind turbine according to the structural deformation prediction result;
judging whether the power prediction result and the fatigue damage evaluation result belong to a preset range, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the fatigue damage evaluation result does not belong to the corresponding preset range.
3. The method of predictive control of a floating wind turbine of claim 2, wherein the control strategy includes controlling a direction of blade twist of the floating wind turbine and controlling a rotor rotational speed of the floating wind turbine.
4. The predictive control method for a floating wind turbine of claim 2, further comprising:
under the condition that any one of the power prediction result and the fatigue damage evaluation result does not belong to the corresponding preset range, executing the control strategy, and then re-executing a prediction step and obtaining the power output prediction result and the structural deformation prediction result after updating the control strategy;
the control strategy comprises the step of controlling the blade torsion direction and/or the wind wheel rotation speed of the floating wind turbine so as to minimize the fatigue damage evaluation result and/or optimize the power prediction result.
5. The predictive control method for a floating wind turbine of claim 2, further comprising:
and under the condition that the power prediction result and the fatigue damage evaluation result both belong to the corresponding preset range, keeping the control strategy of the floating wind turbine unchanged.
6. The method for predictive control of a floating wind turbine of claim 2, wherein the structural deformation prediction result includes a structural deformation prediction of a wind turbine blade of the floating wind turbine and a structural deformation prediction of a wind turbine tower of the floating wind turbine;
the fatigue damage evaluation result comprises fatigue damage evaluation of the fan blade of the floating wind turbine and fatigue damage evaluation of the fan tower of the floating wind turbine.
7. The method for predictive control of a floating wind turbine according to claim 1, wherein the environmental predictive model is data trained based on an artificial intelligence predictive model to obtain;
the wind turbine state prediction model is obtained through data training based on an artificial intelligence prediction model and/or is obtained based on a mechanism model.
8. A predictive control system for a floating wind turbine, characterized by being applied to the predictive control method for a floating wind turbine according to any one of claims 1 to 7;
the predictive control system includes:
the acquisition unit is used for acquiring historical environment data of the position of the floating wind turbine;
the training unit is used for training and generating an environment prediction model of the position of the floating wind turbine according to the historical environment data;
a digital twin unit for generating a wind turbine state prediction model associated with the environmental prediction model based on digital twin according to the environmental prediction model, wherein the wind turbine state prediction model comprises a aerodynamic force predictor model of the floating wind turbine, a hydrodynamic force predictor model of the floating wind turbine, and a structural predictor model of the floating wind turbine;
the demand acquisition unit is used for acquiring prediction data required by the environment prediction model based on the prediction demand;
the environment prediction unit is used for generating environment prediction data corresponding to the prediction demand according to the prediction data and the environment prediction model;
the state prediction unit is used for generating a power output prediction result and a corresponding structural deformation prediction result corresponding to the floating wind turbine according to the environment prediction data and the wind turbine state prediction model;
and the judging control unit is used for judging whether the power prediction result and the structural deformation prediction result belong to a preset range or not, and executing a corresponding control strategy on the floating wind turbine according to the judging result when any one of the power prediction result and the structural deformation prediction result does not belong to the corresponding preset range.
9. An electronic device, comprising:
a memory for storing a processing program;
a processor which, when executing the processing program, implements the predictive control method of a floating wind turbine according to any one of claims 1 to 7.
10. A readable storage medium, wherein a processing program is stored on the readable storage medium, and when executed by a processor, the processing program implements the predictive control method of a floating wind turbine according to any one of claims 1 to 7.
CN202310760724.5A 2023-06-26 2023-06-26 Predictive control method, predictive control system, electronic equipment and readable storage medium for floating wind turbine Pending CN116696658A (en)

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