Target equipment control method and device, storage medium and electronic device

Abstract

The invention discloses a control method and device of target equipment, a storage medium and an electronic device, and relates to the field of computers.The control method of the target equipment comprises the steps that first equipment data of the target equipment in a target area in a first time period are collected, first wind speed distribution data of the target area in the first time period are collected, and the first equipment data comprise equipment operation data and equipment model data; through a control strategy model, according to the first equipment data and the first wind speed distribution data, control strategy planning is conducted on the target equipment, a first control strategy is obtained, and the first control strategy is used for adjusting the equipment output performance of the target equipment; performing equipment control on the target equipment according to the first control strategy; through adoption of the scheme, the problem of low energy production efficiency caused by lack of an effective flow control model for optimizing an operation strategy in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a control method and device for a target device, a storage medium, and an electronic device. Background Art

[0002] Wind turbines in a wind farm usually operate independently, which results in a wake effect between turbines, reducing the energy production efficiency of the entire wind farm. Although centralized wind farms can maximize total power generation through wake guidance, there is a lack of effective flow control models to optimize operation strategies.

[0003] There is a lack of an effective flow control model to optimize the operation strategy in related technologies, which leads to low energy production efficiency. No effective solution has been proposed so far.

[0004] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0005] The embodiments of the present application provide a control method and device for a target device, a storage medium and an electronic device to at least solve the problem in the related art that there is a lack of an effective flow control model to optimize the operation strategy, resulting in low energy production efficiency.

[0006] According to one aspect of an embodiment of the present application, a method for controlling a target device is provided, comprising: collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data comprises: device operation data, device model data; planning a control strategy for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device; and performing device control on the target device according to the first control strategy.

[0007] In an exemplary embodiment, after controlling the target device according to the first control strategy, the method further includes: collecting second device data of the target device within a second time period, wherein the first device data and the second device data are of the same category; performing a strategy evaluation on the first control strategy according to the second device data to obtain a strategy evaluation result; adjusting parameters of the control strategy model according to the first control strategy and the strategy evaluation result, and re-planning the control strategy for the target device through the adjusted control strategy model to obtain a second control strategy.

[0008] In an exemplary embodiment, the control strategy model is parameter adjusted according to the first control strategy and the strategy evaluation result, including: determining whether the strategy evaluation result is greater than a preset threshold; if it is determined that the strategy evaluation result is greater than or equal to the preset threshold, prohibiting parameter adjustment of the control strategy model; if it is determined that the strategy evaluation result is less than the preset threshold, adjusting the control strategy model parameters according to the first control strategy and the strategy evaluation result.

[0009] In an exemplary embodiment, collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, include: collecting third device data of the target device in the first time period through a data acquisition system, and measuring second wind speed distribution data of the target area in the first time period through a lidar system, wherein the first device data and the third device data are of the same category; and performing data preprocessing on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0010] In an exemplary embodiment, data preprocessing is performed on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data, including: performing data integrity check on the third device data and the second wind speed distribution data, and completing the third device data and the second wind speed distribution data by a data interpolation method according to the inspection results to obtain completed third device data and completed second wind speed distribution data; performing outlier detection on the completed third device data and the completed second wind speed distribution data, and correcting the detected outliers to obtain processed third device data and processed second wind speed distribution data; performing data normalization on the processed third device data and the processed second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0011] In an exemplary embodiment, a control strategy model is used to plan a control strategy for the target device according to the device data and the wind speed distribution data. Before obtaining the first control strategy, the method further includes: obtaining fourth device data of the target device in a third time period, and obtaining third wind speed distribution data of the target area in the third time period, wherein the fourth device data is of the same category as the first device data; establishing multiple physical models of the target device according to the fourth device data and the third wind speed distribution data, wherein the physical models include: a Gaussian vortex model and a secondary guide model; and optimizing the multiple physical models respectively according to the fourth device data and the third wind speed distribution data through a Bayesian optimization algorithm to obtain the control strategy model.

[0012] According to another aspect of an embodiment of the present application, a control device for a target device is also provided, including: an acquisition module, used to acquire first device data of a target device in a target area in a first time period, and to acquire first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data, device model data; a planning module, used to perform control strategy planning for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device; a control module, used to perform device control on the target device according to the first control strategy.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the control method of the target device when running.

[0014] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the control method of the target device through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0016] Through the present application, the equipment operation data and equipment model data of the target equipment in the target area in the first time period are collected to obtain the first equipment data, and the first wind speed distribution data of the target area in the first time period are collected; the control strategy model is used to plan the control strategy for the target equipment according to the first equipment data and the first wind speed distribution data to obtain the first control strategy, wherein the first control strategy is used to adjust the equipment output performance of the target equipment; finally, the target equipment is controlled according to the first control strategy; the above scheme is adopted to provide a control strategy model to simulate the influence of the non-zero lateral velocity of the downwind generator group of the yaw generator group, and the yaw angle of each turbine in the wind farm is optimized in combination with the gradient boosting method to guide the wake, thereby improving the total power generation of the wind farm; thereby solving the problem of lack of an effective flow control model to optimize the operation strategy in the related technology, resulting in low energy production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a control method of a target device in an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for controlling a target device according to an embodiment of the present application;

[0021] Figure 3 It is a flow chart of an optional method for determining a centralized wind farm operation strategy based on a physical and data flow control model according to an embodiment of the present application;

[0022] Figure 4 It is a structural block diagram of a control device of a target device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a control method of a target device in an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, CPU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the control method of the target device in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a control method for a target device is provided. Figure 2 is a flow chart of a method for controlling a target device according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps S202-S208:

[0029] Step S202: collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data;

[0030] Step S204: planning a control strategy for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device;

[0031] Step S206: performing device control on the target device according to the first control strategy.

[0032] Through the above steps, the equipment operation data and equipment model data of the target equipment in the target area in the first time period are collected to obtain the first equipment data, and the first wind speed distribution data of the target area in the first time period are collected; the control strategy model is used to plan the control strategy for the target equipment according to the first equipment data and the first wind speed distribution data to obtain the first control strategy, wherein the first control strategy is used to adjust the equipment output performance of the target equipment; finally, the target equipment is controlled according to the first control strategy; the above scheme is adopted to provide a control strategy model to simulate the influence of the non-zero lateral velocity of the downwind generator set of the yaw generator set, and the yaw angle of each turbine in the wind farm is optimized in combination with the gradient boosting method to guide the wake, thereby improving the total power generation of the wind farm; thereby solving the problem of lack of an effective flow control model to optimize the operation strategy in the related technology, resulting in low energy production efficiency.

[0033] In an exemplary embodiment, after controlling the target device according to the first control strategy, the method further includes: collecting second device data of the target device within a second time period, wherein the first device data and the second device data are of the same category; performing a strategy evaluation on the first control strategy according to the second device data to obtain a strategy evaluation result; adjusting parameters of the control strategy model according to the first control strategy and the strategy evaluation result, and re-planning the control strategy for the target device through the adjusted control strategy model to obtain a second control strategy.

[0034] After controlling the target device according to the first control strategy, it is also necessary to collect second device data of the target device within a second time period, and then perform a strategy evaluation on the first control strategy based on the second device data to obtain a strategy evaluation result; finally, adjust the parameters of the control strategy model based on the first control strategy and the strategy evaluation result, and re-plan the control strategy for the target device through the adjusted control strategy model to obtain a second control strategy.

[0035] Optionally, the control strategy model is adjusted in parameters according to the first control strategy and the strategy evaluation result, including: determining whether the strategy evaluation result is greater than a preset threshold; if it is determined that the strategy evaluation result is greater than or equal to the preset threshold, prohibiting parameter adjustment of the control strategy model; if it is determined that the strategy evaluation result is less than the preset threshold, adjusting the control strategy model in parameters according to the first control strategy and the strategy evaluation result.

[0036] After obtaining the strategy evaluation result, determine whether the strategy evaluation result is greater than the preset threshold. If the strategy evaluation result is greater than or equal to the preset threshold, there is no need to adjust the parameters of the control strategy model, and the control strategy model can continue to be used; if the strategy evaluation result is less than the preset threshold, it means that the adjustment effect of the first control strategy is not good, so it is necessary to adjust the parameters of the control strategy model according to the first control strategy and the strategy evaluation result, and carry out the subsequent re-planning process.

[0037] Optionally, collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, include: collecting third device data of the target device in the first time period through a data acquisition system, and measuring second wind speed distribution data of the target area in the first time period through a lidar system, wherein the first device data and the third device data are of the same category; performing data preprocessing on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0038] At the beginning, it is necessary to collect basic information of the wind farm (target area), including the location, quantity, model, power curve, etc. of wind turbines. Collect the wind speed distribution data of the atmospheric boundary layer measured by the LiDAR system (laser radar system) and the operating data of each turbine recorded by the SCADA system (i.e., data acquisition system) (such as power production, nacelle position, blade angle, etc.). Then, it is necessary to pre-process the obtained third device data and second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0039] Optionally, data preprocessing is performed on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data, including: performing data integrity check on the third device data and the second wind speed distribution data, and completing the third device data and the second wind speed distribution data by a data interpolation method according to the inspection results to obtain completed third device data and completed second wind speed distribution data; performing outlier detection on the completed third device data and the completed second wind speed distribution data, and correcting the detected outliers to obtain processed third device data and processed second wind speed distribution data; performing data normalization on the processed third device data and the processed second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0040] First, a data integrity check is required to check whether there are missing values ​​in all collected data, such as no power record for a certain wind turbine at a specific time point. Missing data can be interpolated based on historical trends or data from neighboring turbines. Then outlier detection and processing is performed, using statistical methods (such as Z-score, IQR) or machine learning-based anomaly detection algorithms (such as isolation forest, LOF) to identify outliers. Outliers may be caused by sensor failures, extreme weather conditions, or other abnormal factors. For identified outliers, they can be corrected or deleted to avoid negative impact on model training. Next, a data consistency check can be performed on the data to ensure that the data collected from different sources are consistent in timestamps, units, and formats. For example, all wind speed data should be converted to a unified unit of measurement (such as m / s), and all timestamps should be adjusted to the same time base. Finally, the data needs to be normalized to convert all data to a common scale to eliminate the impact of unit and magnitude differences on model training. Data normalization can be performed using methods such as MinMaxScaler and Z-score normalization.

[0041] Optionally, through the control strategy model, the control strategy planning is performed for the target device according to the device data and the wind speed distribution data, and before obtaining the first control strategy, the method also includes: obtaining fourth device data of the target device in a third time period, and obtaining third wind speed distribution data of the target area in the third time period, wherein the fourth device data is of the same category as the first device data; establishing multiple physical models of the target device according to the fourth device data and the third wind speed distribution data, wherein the physical models include: a Gaussian vortex model and a secondary guide model; according to the fourth device data and the third wind speed distribution data, respectively optimizing the multiple physical models through a Bayesian optimization algorithm to obtain the control strategy model.

[0042] This application also proposes a process for establishing a control strategy model, which specifically includes the following process: first construct a physical model, based on the collected wind conditions and turbine operation data, establish a physical model, especially a Gaussian vortex model and a secondary guidance model, to describe and predict the dynamic characteristics of the wind turbine wake. The Gaussian vortex model takes into account the velocity loss and expansion of the wake, while the secondary guidance model simulates the effect of non-zero lateral velocity on the wake deflection under yaw operation. Then, the model parameters are optimized, using Bayesian optimization or gradient boosting methods, combined with historical operation data (i.e., the fourth device data and the third wind speed distribution data mentioned above), to optimize the model parameters, including the vortex expansion rate, the Gaussian vortex proportional constant, etc. The goal is to minimize the difference between the model prediction and the actual observation data to improve the prediction accuracy of the model.

[0043] In an optional embodiment, the present application proposes a method for determining a centralized wind farm operation strategy based on a physical and data flow control model, the process of which is as follows: Figure 3 As shown, the following steps are included:

[0044] Step S301: data collection and preprocessing.

[0045] Collect basic information about the wind farm, including the location, quantity, model, power curve, etc. of wind turbines. Collect wind speed distribution data measured by the LiDAR system in the atmospheric boundary layer and operating data of each turbine recorded by the SCADA system (such as power production, nacelle position, blade angle, etc.). Perform data cleaning and processing on the raw data, including normalized data processing.

[0046] Step S302: Establish a wind farm flow control model (ie, the above-mentioned control strategy model).

[0047] Based on the physical power-yaw model, the wake distribution under different conditions is accurately predicted, and the flow control model of the wind farm is established. First, the power of the windward (not affected by the vortex) generator set is expressed as Considering that the wind speed and direction in the atmospheric boundary layer vary with height z, the force of the wind turbine blade at each position is expressed as a function of the radial r and azimuth angle θ position. The power of the windward generator set is calculated as follows: Where, Ω is the angular velocity of the blade, df τ is the tangential force at a specific blade cross section.

[0048] The power of the generator set in the downwind direction and affected by the vortex is expressed as The effect of non-zero lateral velocity of the downwind generator set of the yawed generator set is simulated using an optimized linear eddy superposition and analytical secondary guidance model. The eddy currents of the wind turbine are modeled as two-dimensional Gaussian velocity losses. The upwind generator set is defined as i and the downwind generator set is defined as j. The velocity loss associated with the upwind generator set i is expressed as: Where D is the diameter of the generator set. The streamwise and transverse directions are x and y, respectively. The normalized vortex diameter that varies with streamwise position x is: k w The eddy model parameters are estimated using an inverse problem approach based on Bayesian optimization for eddy and atmospheric boundary layer turbulence parameterized as an unknown eddy expansion rate by minimizing the mean square error between the model predictions and the field calibration data: Where γ is the yaw angle.

[0049] The proportional constant of Gaussian eddy current is σ0. The transverse center of gravity of the eddy current of generator set i is y c，i . Flow velocity loss δui (x) is: Among them, the initial flow velocity loss δu 0，i =2a i u 0，i , the empirical rotor area average speed of generator set i is u 0，i erf is the error function. The axial induction factor of generator set i is: Among them, C T，i is the thrust coefficient of generator set i. The thrust coefficient is estimated by linear interpolation from the thrust coefficient table provided by the manufacturer, which records discrete values ​​of the thrust coefficient and wind speed. The wind speed used for interpolation is the average rotor speed,

[0050]

[0051] where u e，j is the average rotor speed of the downwind generator set j with a single upwind vortex, N f is the number of generators located upwind of generator j and whose vortices will affect generator j, u ∞ Indicates free flow speed.

[0052] If generator set i operates under free flow conditions, then u 0，i =u ∞ , is the average rotor speed of generator set i.

[0053] A yaw-misaligned wind turbine will produce a potentially non-zero lateral velocity in the vortex of the downwind turbine, which will lead to a secondary guidance effect, that is, the vortex of the yaw-aligned turbine will be deflected by the non-zero lateral velocity. The lateral velocity model is constructed using the secondary guidance model: Among them, N f is the number of generators located upwind of generator j and whose vortices will affect generator j. The initial lateral velocity is: Assuming that the lateral velocity is Gaussian, the average lateral velocity of the rotor of the downwind generator set in the vortex of the yaw generator set is: where y T It is the lateral center of gravity of the downwind generator set j.

[0054] The function of the vortex centerline velocity of the windward generator set in the streamwise position x is: The lateral center of gravity of the vortex generated by the windward generator set i is: The power generated by the fan is defined as: Among them, C p is the power coefficient, ρ is the air density, and A is the rotor area. The average rotor speed u of the downwind generator set j with a single upwind vortex e，j for: Among them, u 0，i is the average rotor speed of generator set i;

[0055]

[0056] Considering eddies from multiple wind turbines, an optimized linear eddy superposition method is used: The power is calculated for each generator in the wind farm by a step-by-step calculation procedure that starts in the upwind direction and calculates the eddy velocity and power in sequence. In the linear superposition, the eddy velocity loss is defined relative to the local velocity encountered by the generator affected by the eddy. Therefore, when calculating the eddy for the generator itself affected by the eddy, we set u 0，i =u e，j For free-flow generators, u 0，i =u ∞ We define the power coefficient C p (γ) is modeled as: C p (γ) = P r ·C p (γ=0), where P p is an empirical factor related to the turbine that needs to be adjusted.

[0057] Finally, historical data specific to the site and wind conditions collected by the wind farm under standard, independent operation (γ = 0) are used to obtain model parameters such as the eddy expansion rate for yaw angle prediction.

[0058] Step S303: According to the optimized flow control model, a collective control strategy for the turbines is formulated to optimize the yaw angle of each turbine and maximize the power generation of the entire wind farm. The optimal yaw angle control strategy for a wind farm with N generators is predicted using a flow control model based on gradient optimization: This is a dynamically adjusting control strategy that ensures optimal performance in varying wind conditions.

[0059] Step S304: The SCADA system monitors the operation status of the wind farm in real time, collects and analyzes power production data and wind speed data, evaluates the implementation effect of the control strategy in real time, adjusts model parameters and control strategy, and improves the prediction accuracy and adaptability of the model.

[0060] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0061] In this embodiment, a control device for a target device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0062] Figure 4 : is a structural block diagram of a control device of a target device according to an embodiment of the present application, the device comprising:

[0063] The collection module 42 is used to collect first device data of a target device in a target area in a first time period, and collect first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data;

[0064] A planning module 44, configured to perform control strategy planning for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device;

[0065] The control module 46 is configured to control the target device according to the first control strategy.

[0066] Through the above-mentioned device, the equipment operation data and equipment model data of the target equipment in the target area in the first time period are collected to obtain the first equipment data, and the first wind speed distribution data of the target area in the first time period are collected; the control strategy model is used to plan the control strategy for the target equipment according to the first equipment data and the first wind speed distribution data to obtain the first control strategy, wherein the first control strategy is used to adjust the equipment output performance of the target equipment; finally, the target equipment is controlled according to the first control strategy; the above-mentioned scheme is adopted to provide a control strategy model to simulate the influence of the non-zero lateral velocity of the downwind generator set of the yaw generator set, and the yaw angle of each turbine in the wind farm is optimized in combination with the gradient boosting method to guide the wake, thereby improving the total power generation of the wind farm; thereby solving the problem of lack of an effective flow control model to optimize the operation strategy in the related technology, resulting in low energy production efficiency.

[0067] Optionally, the above-mentioned control module 46 is also used to collect second device data of the target device within a second time period, wherein the first device data and the second device data are of the same category; perform a strategy evaluation on the first control strategy based on the second device data to obtain a strategy evaluation result; adjust parameters of the control strategy model based on the first control strategy and the strategy evaluation result, and re-plan the control strategy for the target device through the adjusted control strategy model to obtain a second control strategy.

[0068] Optionally, the above-mentioned control module 46 is also used to determine whether the strategy evaluation result is greater than a preset threshold; when it is determined that the strategy evaluation result is greater than or equal to the preset threshold, parameter adjustment of the control strategy model is prohibited; when it is determined that the strategy evaluation result is less than the preset threshold, parameter adjustment of the control strategy model is performed according to the first control strategy and the strategy evaluation result.

[0069] Optionally, the above-mentioned acquisition module 42 is also used to collect third device data of the target device in the first time period through the data acquisition system, and to measure second wind speed distribution data of the target area in the first time period through the lidar system, wherein the first device data and the third device data are of the same category; and perform data preprocessing on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0070] At the beginning, it is necessary to collect basic information of the wind farm (target area), including the location, quantity, model, power curve, etc. of wind turbines. Collect the wind speed distribution data of the atmospheric boundary layer measured by the LiDAR system (laser radar system) and the operating data of each turbine recorded by the SCADA system (i.e., data acquisition system) (such as power production, nacelle position, blade angle, etc.). Then, it is necessary to pre-process the obtained third device data and second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0071] Optionally, the above-mentioned acquisition module 42 is also used to perform a data integrity check on the third device data and the second wind speed distribution data, and to complete the third device data and the second wind speed distribution data through a data interpolation method according to the inspection results to obtain the completed third device data and the completed second wind speed distribution data; to perform outlier detection on the completed third device data and the completed second wind speed distribution data, and to correct the detected outliers to obtain the processed third device data and the processed second wind speed distribution data; to perform data normalization on the processed third device data and the processed second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

[0072] Optionally, the above-mentioned planning module 44 is also used to obtain fourth equipment data of the target equipment in a third time period, and to obtain third wind speed distribution data of the target area in the third time period, wherein the fourth equipment data is of the same category as the first equipment data; a plurality of physical models of the target equipment are established based on the fourth equipment data and the third wind speed distribution data, wherein the physical models include: a Gaussian vortex model and a secondary guide model; and a plurality of physical models are optimized respectively by a Bayesian optimization algorithm based on the fourth equipment data and the third wind speed distribution data to obtain the control strategy model.

[0073] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0074] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0075] S1, collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data;

[0076] S2, planning a control strategy for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device;

[0077] S3: Control the target device according to the first control strategy.

[0078] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0079] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0080] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0081] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0082] S1, collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data;

[0083] S2, planning a control strategy for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device;

[0084] S3: Control the target device according to the first control strategy.

[0085] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0086] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0087] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0088] S1, collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data;

[0089] S2, planning a control strategy for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device;

[0090] S3: Control the target device according to the first control strategy.

[0091] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0092] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0093] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for controlling a target device, characterized in that: include: Collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data; By using a control strategy model, a control strategy is planned for the target device according to the first device data and the first wind speed distribution data to obtain a first control strategy, wherein the first control strategy is used to adjust the device output performance of the target device; The target device is controlled according to the first control strategy.

2. The method according to claim 1, characterized in that After controlling the target device according to the first control strategy, the method further includes: Collecting second device data of the target device in a second time period, wherein the first device data and the second device data are of the same category; Performing a strategy evaluation on the first control strategy according to the second device data to obtain a strategy evaluation result; The control strategy model is parameter-adjusted according to the first control strategy and the strategy evaluation result, and the control strategy of the target device is replanned through the adjusted control strategy model to obtain a second control strategy.

3. The method according to claim 2, characterized in that Adjusting parameters of the control strategy model according to the first control strategy and the strategy evaluation result includes: Determine whether the strategy evaluation result is greater than a preset threshold; When it is determined that the strategy evaluation result is greater than or equal to the preset threshold, prohibiting parameter adjustment of the control strategy model; When it is determined that the strategy evaluation result is less than the preset threshold, parameters of the control strategy model are adjusted according to the first control strategy and the strategy evaluation result.

4. The method according to claim 1, characterized in that Collecting first device data of a target device in a target area in a first time period, and collecting first wind speed distribution data of the target area in the first time period, includes: Collecting third device data of the target device in the first time period through a data acquisition system, and measuring second wind speed distribution data of the target area in the first time period through a laser radar system, wherein the first device data and the third device data are of the same category; Data preprocessing is performed on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data.

5. The method according to claim 4, characterized in that Performing data preprocessing on the third device data and the second wind speed distribution data to obtain the first device data and the first wind speed distribution data includes: Performing a data integrity check on the third device data and the second wind speed distribution data, and completing the third device data and the second wind speed distribution data by a data interpolation method according to the obtained inspection result, to obtain completed third device data and completed second wind speed distribution data; Performing outlier detection on the completed third device data and the completed second wind speed distribution data, and performing correction processing on the detected outliers to obtain processed third device data and processed second wind speed distribution data; The processed third device data and the processed second wind speed distribution data are subjected to data normalization processing to obtain the first device data and the first wind speed distribution data.

6. The method according to claim 1, characterized in that By using a control strategy model, a control strategy is planned for the target device according to the device data and the wind speed distribution data, and before obtaining a first control strategy, the method further includes: Acquire fourth device data of the target device in a third time period, and acquire third wind speed distribution data of the target area in the third time period, wherein the fourth device data is of the same category as the first device data; Establishing a plurality of physical models of the target devices according to the fourth device data and the third wind speed distribution data, wherein the physical models include: a Gaussian vortex model and a secondary guide model; According to the fourth device data and the third wind speed distribution data, the plurality of physical models are optimized respectively by using a Bayesian optimization algorithm to obtain the control strategy model.

7. A control device for a target device, characterized in that: include: A collection module, used to collect first device data of a target device in a target area in a first time period, and to collect first wind speed distribution data of the target area in the first time period, wherein the first device data includes: device operation data and device model data; a planning module, configured to perform control strategy planning for the target device according to the first device data and the first wind speed distribution data through a control strategy model to obtain a first control strategy, wherein the first control strategy is used to adjust device output performance of the target device; A control module is used to control the target device according to the first control strategy.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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