Yaw control method and related device

Abstract

The invention discloses a yaw control method and a related device, and relates to the field of wind power generation, and the yaw control method comprises the following steps: obtaining an original wind speed time sequence data set, an original wind direction time sequence data set and design parameters of a wind turbine generator, and calculating a yaw rate of the wind turbine generator based on the original wind speed time sequence data set and the original wind direction time sequence data set; obtaining a wind speed probability density function, a wind direction deviation probability density function and yaw control input data, determining initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters, and performing analogue simulation according to the yaw control input data and the initial yaw control parameters to obtain yaw control parameters. And determining a target yaw control parameter based on the simulation result and a preset multi-target constraint condition. According to the method, the difference of wind regimes of different wind power plants and the difference of wind turbine generators are considered, the two kinds of environmental condition data of the wind speed and the wind direction of the wind power plants are combined, the design parameters of the wind turbine generators are combined, and the target yaw control parameters are more adaptive to the wind turbine generators and the actual wind regimes.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a yaw control method and related devices. Background Art

[0002] The wind turbine can adjust to the wind in real time through yaw control, which can ensure the best utilization of wind energy on the one hand, and on the other hand, it can ensure the safe operation of the wind turbine and avoid unbalanced load on the left and right of the wind rotor.

[0003] At present, almost all wind power projects in the industry that generate electricity based on wind turbines adopt a unified yaw control strategy. However, there are obvious differences in wind conditions in different wind farms, and there are also differences between wind turbines, resulting in increasingly obvious differences in yaw efficiency brought about by the unified yaw control strategy.

[0004] Therefore, there is an urgent need for a yaw control method that is compatible with the actual wind conditions of wind turbines and wind farms. Summary of the invention

[0005] In view of the above problems, the present application provides a yaw control method and related devices to achieve the purpose of providing different yaw control strategies for different wind turbines and actual wind conditions in wind farms. The specific scheme is as follows:

[0006] A first aspect of the present application provides a yaw control method, comprising:

[0007] Acquire an original wind speed time series data set, an original wind direction time series data set, and design parameters of a wind turbine generator set, wherein the sampling frequencies of the original wind speed time series data set and the original wind direction time series data set are the same;

[0008] Based on the original wind speed time series data set and the original wind direction time series data set, a wind speed probability density function, a wind direction deviation probability density function and yaw control input data are obtained;

[0009] Determining initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters;

[0010] A simulation is performed according to the yaw control input data and the initial yaw control parameters to determine target yaw control parameters based on simulation results and preset multi-objective constraint conditions.

[0011] In a possible implementation, obtaining the wind speed probability density function and the wind direction deviation probability density function based on the original wind speed time series data set and the original wind direction time series data set includes:

[0012] Based on the first time, the original wind speed time series data set and the original wind direction time series data set are respectively divided into bins to obtain original wind speed time series data subsets of multiple time periods and original wind direction time series data subsets of the multiple time periods;

[0013] Performing mean calculations on the original wind speed time series data subset of each time period and the original wind direction time series data subset of each time period, respectively, to obtain the wind speed average value of each time period and the wind direction average value of each time period;

[0014] For the wind speed average values ​​of the multiple time periods, the average value of the wind speed of each two adjacent time periods is calculated to obtain a preprocessed wind speed time series data set;

[0015] Based on the preprocessed wind speed time series data set, obtaining the wind speed probability density function;

[0016] For the wind direction average values ​​of the multiple time periods, the difference between the wind direction average values ​​of each two adjacent time periods is calculated to obtain a preprocessed wind direction time series data set;

[0017] Taking an absolute value of each wind direction deviation included in the preprocessed wind direction time series data set to obtain a wind direction deviation absolute value time series data set;

[0018] Based on the wind direction deviation absolute value time series data set, the wind direction deviation probability density function is obtained.

[0019] In a possible implementation, obtaining the yaw control input data based on the original wind speed time series data set and the original wind direction time series data set includes:

[0020] Performing filtering processing on the original wind speed time series data set based on the second time to obtain a filtered wind speed;

[0021] Performing filtering processing on the original wind direction time series data set based on a third time to obtain a filtered wind direction;

[0022] The filtered wind speed and the filtered wind direction are determined as the yaw control input data.

[0023] In a possible implementation, determining the initial yaw control parameter based on the wind speed probability density function, the wind direction deviation probability density function and the design parameter includes:

[0024] Determining the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, cut-in wind speed and aircraft model design power curve in the design parameters;

[0025] Based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed and the yaw mode switching wind speed, determine a first wind direction deviation angle threshold and a second wind direction deviation angle threshold corresponding to a first wind speed interval, and a third wind direction deviation angle threshold and a fourth wind direction deviation angle threshold corresponding to a second wind speed interval, wherein the first wind speed interval refers to a wind speed interval formed by the yaw mode switching wind speed and the rated wind speed, and the second wind speed interval refers to a wind speed interval formed by the cut-in wind speed and the yaw mode switching wind speed;

[0026] Acquire a first duration corresponding to the first wind direction deviation angle threshold and the third wind direction deviation angle threshold, a second duration corresponding to the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold, a yaw rate, a yaw deviation threshold, and a yaw deviation stabilization time, wherein the yaw deviation threshold and the yaw deviation stabilization time are used to obtain a yaw stop condition;

[0027] The yaw mode switching wind speed, the first wind direction deviation angle threshold, the third wind direction deviation angle threshold, the first duration, the second wind direction deviation angle threshold, the fourth wind direction deviation angle threshold, the second duration, the yaw rate, the yaw deviation threshold and the yaw deviation stabilization time are determined as the initial yaw control parameters.

[0028] In a possible implementation, determining the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, the cut-in wind speed, and the model design power curve in the design parameters includes:

[0029] Based on the wind speed probability density function, the rated wind speed and the cut-in wind speed in the design parameters, determining the preprocessed wind speed time series data that falls into the third wind speed interval in the preprocessed wind speed time series data set as the target preprocessed wind speed time series data, wherein the third wind speed interval refers to the wind speed interval formed by the cut-in wind speed and the rated wind speed;

[0030] Based on the target pre-processed wind speed time series data, the wind speed probability density function and the model design power curve in the design parameters, determine the power generation corresponding to the multiple sub-intervals included in the third wind speed interval, wherein the multiple sub-intervals included in the third wind speed interval are sub-intervals obtained by dividing the third wind speed interval according to the first wind speed interval;

[0031] The yaw mode switching wind speed is determined according to the power generation amounts respectively corresponding to a plurality of sub-intervals included in the third wind speed interval and a preset power generation proportion.

[0032] In a possible implementation, determining a first wind direction deviation angle threshold and a second wind direction deviation angle threshold corresponding to a first wind speed interval, and a third wind direction deviation angle threshold and a fourth wind direction deviation angle threshold corresponding to a second wind speed interval based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed, and the yaw mode switching wind speed includes:

[0033] Obtaining a joint probability distribution function based on the wind speed probability density function and the wind direction deviation probability density function;

[0034] Determine a wind direction deviation cumulative probability density function based on the wind direction deviation probability density function;

[0035] Based on the joint probability distribution function and the cumulative probability density function of the wind direction deviation, determine the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the first wind speed interval, and the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the second wind speed interval, wherein the multiple sub-intervals included in the first wind speed interval are sub-intervals obtained by dividing the first wind speed interval according to the second wind speed interval, and the multiple sub-intervals included in the second wind speed interval are sub-intervals obtained by dividing the second wind speed interval according to the first wind speed interval;

[0036] Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the first wind speed interval, determine a first wind direction deviation absolute value with a cumulative probability of a first probability and a second wind direction deviation absolute value with a cumulative probability of a second probability as the first wind direction deviation absolute value and the second wind direction deviation absolute value corresponding to each subinterval included in the first wind speed interval;

[0037] Calculating an average of first wind direction deviation absolute values ​​corresponding to all subintervals included in the first wind speed interval as the first wind direction deviation angle threshold;

[0038] Calculating an average of the absolute values ​​of the second wind direction deviations corresponding to all subintervals included in the first wind speed interval as the second wind direction deviation angle threshold;

[0039] Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the second wind speed interval, determine a third wind direction deviation absolute value with a cumulative probability of a third probability and a fourth wind direction deviation absolute value with a cumulative probability of a fourth probability as the third wind direction deviation absolute value and the fourth wind direction deviation absolute value corresponding to each subinterval included in the second wind speed interval;

[0040] Calculating an average of the absolute values ​​of the third wind direction deviations corresponding to all sub-intervals included in the second wind speed interval as the third wind direction deviation angle threshold;

[0041] An average of the fourth wind direction deviation absolute values ​​corresponding to all sub-intervals included in the second wind speed interval is calculated as the fourth wind direction deviation angle threshold.

[0042] A second aspect of the present application provides a yaw control device, comprising:

[0043] A data acquisition module, used to acquire an original wind speed time series data set, an original wind direction time series data set and design parameters of a wind turbine generator set, wherein the sampling frequency of the original wind speed time series data set and the original wind direction time series data set is the same;

[0044] A first data processing module, configured to obtain a wind speed probability density function, a wind direction deviation probability density function and yaw control input data based on the original wind speed time series data set and the original wind direction time series data set;

[0045] A second data processing module, configured to determine an initial yaw control parameter based on the wind speed probability density function, the wind direction deviation probability density function and the design parameter;

[0046] The yaw simulation module is used to perform simulation according to the yaw control input data and the initial yaw control parameters, so as to determine the target yaw control parameters based on the simulation results and the preset multi-objective constraint conditions.

[0047] A third aspect of the present application provides a computer program product, comprising computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the yaw control method of the first aspect or any implementation of the first aspect.

[0048] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0049] The memory is used to store computer programs;

[0050] The processor is used to execute the computer program so that the electronic device can implement the yaw control method of the first aspect or any implementation manner of the first aspect.

[0051] A fifth aspect of the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the yaw control method of the first aspect or any implementation method of the first aspect.

[0052] By means of the above technical scheme, the yaw control method provided by the present application obtains the original wind speed time series data set, the original wind direction time series data set and the design parameters of the wind turbine, obtains the wind speed probability density function, the wind direction deviation probability density function and the yaw control input data based on the original wind speed time series data set and the original wind direction time series data set, determines the initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters, performs simulation according to the yaw control input data and the initial yaw control parameters, and determines the target yaw control parameters based on the simulation results and the preset multi-objective constraints. It can be seen that the present application takes into account the differences in wind conditions of different wind farms and the differences in wind turbines, combines the two types of environmental condition data of wind speed and wind direction of the wind farm, and combines the design parameters of the wind turbine, so that the final target yaw control parameters are more adapted to the actual wind conditions of the wind turbine and the wind farm, thereby improving the yaw efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0054] Figure 1 A schematic diagram of a system architecture provided for this application;

[0055] Figure 2 A schematic diagram of an optional hardware structure of the terminal 100 provided in this application;

[0056] Figure 3 A schematic diagram of the structure of a server 200 provided in this application;

[0057] Figure 4 A schematic diagram of a flow chart of a yaw control method provided in this application;

[0058] Figure 5 A schematic diagram of the structure of a yaw control device provided in this application;

[0059] Figure 6 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0061] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0062] 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 need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0063] See also Figure 1 , Figure 1 A schematic diagram of a system architecture is shown. The system may include a terminal 100 and a server 200. The server 200 may include one or more servers ( Figure 1 In the example, a server is included, and the server 200 can provide the method provided in the embodiment of the present application for one or more terminals.

[0064] Among them, an application can be installed on the terminal 100, and the above application and web page can provide an interface. The terminal 100 can receive relevant parameters entered by the user on the interface and send the above parameters to the server 200. The server 200 can obtain processing results based on the received parameters and return the processing results to the terminal 100.

[0065] It should be understood that in some optional implementations, the terminal 100 can also complete the action of obtaining the processing result based on the received parameters by itself without the cooperation of the server, and the embodiments of the present application are not limited to this.

[0066] Next describe Figure 1 The product form of the mid-terminal 100;

[0067] The terminal 100 in the embodiment of the present application can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc., and the embodiment of the present application does not impose any restrictions on this.

[0068] Figure 2 An optional hardware structure diagram of the terminal 100 is shown.

[0069] refer to Figure 2 As shown, the terminal 100 may include a radio frequency unit 110, a memory 120, an input unit 130, a display unit 140, a camera 150 (optional), an audio circuit 160 (optional), a speaker 161 (optional), a microphone 162 (optional), an earphone jack 163 (optional), a processor 170, an external interface 180, a power supply 190 and other components. Those skilled in the art will appreciate that Figure 2 These are merely examples of terminals or multi-function devices and do not constitute limitations on the terminals or multi-function devices, which may include more or fewer components than those shown in the figures, or combinations of certain components, or different components.

[0070] The input unit 130 can be used to receive input digital or character information, and generate key signal input related to the user settings and function control of the portable multifunctional device. Specifically, the input unit 130 may include a touch screen 131 (optional) and / or other input devices 132. The touch screen 131 can collect the user's touch operations on or near it (such as the user's operation on or near the touch screen using any suitable object such as fingers, joints, stylus, etc.), and drive the corresponding connection device according to a pre-set program. The touch screen can detect the user's touch action on the touch screen, convert the touch action into a touch signal and send it to the processor 170, and can receive and execute the command sent by the processor 170; the touch signal at least includes the touch point coordinate information. The touch screen 131 can provide an input interface and an output interface between the terminal 100 and the user. In addition, the touch screen can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic wave. In addition to the touch screen 131, the input unit 130 can also include other input devices. Specifically, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0071] Among them, the input device 132 can receive input data and the like.

[0072] The display unit 140 may be used to display information input by a user or provided to a user, various menus of the terminal 100, an interactive interface, file display, and / or playback of any multimedia file.

[0073] The memory 120 can be used to store instructions and data. The memory 120 can mainly include an instruction storage area and a data storage area. The data storage area can store various data, such as multimedia files, texts, etc.; the instruction storage area can store software units such as operating systems, applications, instructions required for at least one function, or their subsets and extensions. It can also include a non-volatile random access memory; provide the processor 170 with hardware, software and data resources including management of computing and processing equipment, and support control software and applications. It is also used for the storage of multimedia files, and the storage of running programs and applications.

[0074] The processor 170 is the control center of the terminal 100. It uses various interfaces and lines to connect various parts of the entire terminal 100. By running or executing instructions stored in the memory 120 and calling data stored in the memory 120, it executes various functions of the terminal 100 and processes data, thereby controlling the terminal device as a whole. Optionally, the processor 170 may include one or more processing units; preferably, the processor 170 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application program, and the modem processor mainly processes wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 170. In some embodiments, the processor and the memory may be implemented on a single chip, and in some embodiments, they may also be implemented separately on separate chips. The processor 170 may also be used to generate corresponding operation control signals, send them to corresponding components of the computing and processing device, read and process data in the software, especially read and process data and programs in the memory 120, so that each functional module therein performs corresponding functions, thereby controlling the corresponding components to act according to the requirements of the instructions.

[0075] The memory 120 may be used to store software codes related to the yaw control method, the processor 170 may execute the steps of the yaw control method, and may also schedule other units (such as the above-mentioned input unit 130 and display unit 140) to implement corresponding functions.

[0076] The radio frequency unit 110 (optional) can be used for receiving and sending information or receiving and sending signals during a call, for example, after receiving the downlink information of the base station, it is sent to the processor 170 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, LNA), a duplexer, etc. In addition, the radio frequency unit 110 can also communicate with network devices and other devices through wireless communication. The wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile Communication (Global System of Mobile Communication, GSM), General Packet Radio Service (General Packet Radio Service, GPRS), Code Division Multiple Access (Code Division Multiple Access, CDMA), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA), Long Term Evolution (Long Term Evolution, LTE), email, Short Messaging Service (SMS), etc.

[0077] In this embodiment of the present application, the RF unit 110 can send data to the server 200 and receive processing results sent by the server 200.

[0078] It should be understood that the radio frequency unit 110 is optional and can be replaced by other communication interfaces, such as a network port.

[0079] The terminal 100 also includes a power supply 190 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 170 through a power management system, so that the power management system can manage functions such as charging, discharging, and power consumption.

[0080] The terminal 100 further includes an external interface 180 , which may be a standard Micro USB interface or a multi-pin connector, and may be used to connect the terminal 100 to communicate with other devices, or to connect a charger to charge the terminal 100 .

[0081] Although not shown, the terminal 100 may also include a flashlight, a wireless fidelity (WiFi) module, a Bluetooth module, sensors with different functions, etc., which are not described in detail here. Some or all of the methods described below may be applied in the following embodiments. Figure 2 In the terminal 100 shown.

[0082] Next describe Figure 1 The product form of the server 200;

[0083] Figure 3 A schematic diagram of the structure of a server 200 is provided, such as Figure 3 As shown, the server 200 includes a bus 201, a processor 202, a communication interface 203, and a memory 204. The processor 202, the memory 204, and the communication interface 203 communicate with each other via the bus 201.

[0084] The bus 201 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0085] The processor 202 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0086] The memory 204 may include a volatile memory, such as a random access memory (RAM). The memory 204 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0087] The memory 204 may be used to store software codes related to the yaw control method, and the processor 202 may execute the steps of the yaw control method of the chip, and may also schedule other units to implement corresponding functions.

[0088] It should be understood that the above-mentioned terminal 100 and server 200 can be centralized or distributed devices, and the processors in the above-mentioned terminal 100 and server 200 (such as processor 170 and processor 202) can be hardware circuits (such as application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), general-purpose processors, DSPs, microprocessors or microcontrollers, etc.), or a combination of these hardware circuits. For example, the processor can be a hardware system with an instruction execution function, such as a CPU, DSP, etc., or a hardware system without an instruction execution function, such as an ASIC, FPGA, etc., or a combination of the above-mentioned hardware systems without an instruction execution function and hardware systems with an instruction execution function.

[0089] In order to enable those skilled in the art to better understand the present application, the relevant terms used in the present application are explained below.

[0090] Wind turbine: Equipment used to convert wind energy into electrical energy.

[0091] Yaw control: The wind turbine rotates within 360° according to the change of wind direction to ensure that the incoming wind is perpendicular to the plane of the wind turbine rotor or there is a small angle deviation. The purpose of yaw control is to maintain the optimal utilization of wind energy. Yaw control determines the yaw start and yaw direction based on wind speed and yaw deviation. At the same time, during the yaw process, the wind direction deviation is used to determine whether the yaw should be stopped.

[0092] Wind conditions: This application mainly refers to the wind speed and direction data of the wind farm. Before the construction of the wind farm, a wind tower will be installed to measure the wind speed and direction for more than one year, and the data is required to be in seconds.

[0093] Yaw characteristic indicators: refers to the average daily yaw frequency, cumulative yaw duration, etc.

[0094] Wind speed: refers to the flow velocity of the atmosphere or air. In this application, it refers to the flow velocity of the flow field entering the wind turbine rotor plane.

[0095] Wind direction: Here it refers to the angle between the air flow direction and the north direction.

[0096] Yaw deviation: usually refers to the angle between the airflow direction and the direction directly in front of the wind turbine.

[0097] Wind direction deviation: This application refers to the difference in wind direction between adjacent sampling time points.

[0098] The present application provides a yaw control method. The yaw control method of an embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0099] Reference Figure 4 , Figure 4 A schematic flow chart of a yaw control method provided in an embodiment of the present application, the method may include:

[0100] Step S401, obtaining an original wind speed time series data set, an original wind direction time series data set and design parameters of a wind turbine.

[0101] Those skilled in the art should understand that, in actual applications, when a wind farm needs to be built in a certain place, preliminary micro-selection measurement data is usually carried out, for example, a wind tower or a ground-based lidar is installed to measure and collect environmental condition data of the proposed wind farm for a long time (usually more than 1 year), and the sampling frequency is required to be above 1 Hz and stored in real time.

[0102] Then, this embodiment can obtain the original wind speed time series data set and the original wind direction time series data set in the environmental condition data.

[0103] For example, the original wind speed time series dataset is: ,in, represents the original wind speed time series dataset, Indicates the total number of original wind speed time series data contained in the original wind speed time series dataset.

[0104] It is worth noting that each original wind speed time series data in the original wind speed time series data set (for example, , , etc.) are all greater than or equal to 0m / s.

[0105] The original wind direction time series dataset is: ,in, Represents the original wind direction time series dataset.

[0106] It is worth noting that each original wind direction time series data set contains original wind direction time series data (for example, , , etc.) are all greater than or equal to 0° and less than or equal to 360°.

[0107] It should be noted that the sampling frequency of the above original wind speed time series data set and the original wind direction time series data set is the same. In a possible implementation, the sampling interval can be Second.

[0108] Above and All data are collected at the first sampling time. and are all data collected at the second sampling time, and so on. The time interval between the first sampling time and the second sampling time is Second.

[0109] Considering that different wind power projects may use different wind turbines and there are differences between different wind turbines, in order to make the final generated target yaw control parameters more compatible with the wind turbines, the present application can also obtain the design parameters of the wind turbines for subsequent generation of the final target yaw control parameters.

[0110] Step S402: Based on the original wind speed time series data set and the original wind direction time series data set, a wind speed probability density function, a wind direction deviation probability density function and yaw control input data are obtained.

[0111] In this embodiment, a wind speed probability density function can be obtained based on the original wind speed time series data set, where the wind speed probability density function can reflect the wind speed distribution of the original wind speed time series data set. Through the wind speed probability density function, the wind speed distribution characteristics can be better understood, which helps to understand the concentration trend, dispersion degree and distribution shape of the wind speed, thereby providing an important reference for the design and operation of the wind turbine, and can more accurately predict the future change trend of the wind speed based on the wind speed distribution probability density function, thereby formulating a more reasonable yaw control strategy.

[0112] Similarly, this embodiment can obtain a wind direction deviation probability density function based on the original wind direction time series data set, and the wind direction deviation probability density function can reflect the distribution of the wind direction deviation of the original wind direction time series data set. Through the wind direction deviation probability density function, the main distribution direction and change mode of the wind direction can be more accurately identified, which helps to understand the wind direction characteristics of the environment in which the wind turbine is located, and further provide more accurate reference information for yaw control.

[0113] This embodiment can also obtain yaw control input data based on the original wind speed time series data set and the original wind direction time series data set for use in subsequent yaw control simulation.

[0114] Step S403: determining initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters.

[0115] Specifically, this embodiment may comprehensively consider the wind speed probability density function, the wind direction deviation probability density function, and the design parameters of the wind turbine generator set to obtain initial yaw control parameters that are suitable for both the environmental conditions of the wind farm and the wind turbine generator set.

[0116] Step S404: performing simulation according to the yaw control input data and the initial yaw control parameters, so as to determine the target yaw control parameters based on the simulation results and the preset multi-objective constraint conditions.

[0117] In this embodiment, the yaw control input data and the initial yaw control parameters can be input into the simulation software to perform a simulation of the yaw control and obtain a simulation result.

[0118] Optionally, the simulation results include but are not limited to the following information: average daily frequency of yaw, cumulative yaw time and power generation loss rate.

[0119] In this embodiment, preset multi-objective constraint conditions are pre-configured. For example, a set of optional multi-objective constraint conditions is:

[0120] Formula (1)

[0121] in, represents the daily average frequency of deviation, Indicates the accumulated yaw time. represents the power generation loss rate, Represents the life cycle of a wind turbine.

[0122] In this embodiment, the target yaw control parameter can be determined based on the simulation result and the preset multi-objective constraint condition. Optionally, the process may include: inputting the simulation result into the multi-objective constraint condition to determine whether the multi-objective constraint condition is true, and if so, determining that the simulation result satisfies the multi-objective constraint condition, then the initial yaw control parameter used in this simulation is the target yaw control parameter that best matches the wind farm wind condition and the wind turbine; if the multi-objective constraint condition is false, then determining that the simulation result does not satisfy the multi-objective constraint condition, then the initial yaw control parameter can be adjusted to obtain the adjusted yaw control parameter, and then the simulation is performed based on the yaw control input data and the adjusted yaw control parameter to obtain the simulation result, and then the simulation result is input into the multi-objective constraint condition, and so on, until the simulation result satisfies the multi-objective constraint condition, the corresponding adjusted yaw control parameter is output, and the adjusted yaw control parameter is the target yaw control parameter.

[0123] The yaw control method provided in the present application obtains the original wind speed time series data set, the original wind direction time series data set and the design parameters of the wind turbine, obtains the wind speed probability density function, the wind direction deviation probability density function and the yaw control input data based on the original wind speed time series data set and the original wind direction time series data set, determines the initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters, performs simulation according to the yaw control input data and the initial yaw control parameters, and determines the target yaw control parameters based on the simulation results and the preset multi-objective constraints. It can be seen that the present application takes into account the differences in wind conditions of different wind farms and the differences in wind turbines, combines both the two types of environmental condition data of wind speed and wind direction of the wind farm, and combines the design parameters of the wind turbine, so that the final target yaw control parameters are more adapted to the actual wind conditions of the wind turbine and the wind farm, thereby improving the yaw efficiency.

[0124] In some embodiments of the present application, the process of obtaining the wind speed probability density function and the wind direction deviation probability density function based on the original wind speed time series data set and the original wind direction time series data set in step S402 is introduced.

[0125] First, based on the first time For the original wind speed time series dataset and the original wind direction time series dataset The data are divided into bins respectively to obtain subsets of original wind speed time series data for multiple time periods and subsets of original wind direction time series data for multiple time periods.

[0126] Take the binning process of the original wind speed time series dataset as an example. Assume that the original wind speed time series dataset Contains 100 original wind speed time series data, is 1 second, If the time series data is 10 seconds, the subset of original wind speed time series data in the first period includes: The first to tenth original wind speed time series data in the second period; the original wind speed time series data subset of the second period includes: The 11th original wind speed time series data to the 20th original wind speed time series data; and so on.

[0127] In this embodiment, if If the time is too short, the subsequent wind speed filtering will cause large fluctuations in the wind speed after filtering, and the yaw control will start and stop frequently and easily switch between multiple modes; If it is too long, the filtered wind speed will be too stable, which will have a great impact on the timeliness of yaw control. Therefore, it is necessary to set a suitable first time.

[0128] Optional, The range is 20 seconds to 100 seconds. This range can achieve smooth and undistorted wind speed filtering in the subsequent process.

[0129] Next, the mean values ​​of the original wind speed time series data subsets in each time period and the original wind direction time series data subsets in each time period can be calculated respectively to obtain the average wind speed value in each time period and the average wind direction value in each time period.

[0130] Formula (2)

[0131] Formula (3)

[0132] in, Represents the average wind speed in each period, where each element represents the average wind speed in a period; Represents the average wind direction value for each period, where each element represents the average wind direction value for a period; Indicates the total number of time slots.

[0133] For example, the original wind speed time series dataset Contains 100 original wind speed time series data, is 1 second, is 10 seconds, then is 10, then means The average value of the 1st to 10th original wind speed time series data in , means The average value of the 11th original wind speed time series data to the 20th original wind speed time series data, and so on.

[0134] Next, this embodiment can calculate the mean of the wind speed average values ​​of each two adjacent time periods among the wind speed average values ​​of multiple time periods to obtain a preprocessed wind speed time series data set; and calculate the difference of the wind direction average values ​​of each two adjacent time periods among the wind direction average values ​​of multiple time periods to obtain a preprocessed wind direction time series data set.

[0135] Formula (4)

[0136] Formula (5)

[0137] in, Represents a preprocessed wind speed time series data set, where each element represents a preprocessed wind speed time series data. , , and so on; Represents the preprocessed wind direction time series dataset, where each element represents a wind direction deviation, optional, , , and so on.

[0138] After preprocessing, the wind speed time series data set is obtained. After that, this embodiment can obtain the wind speed probability density function based on the preprocessed wind speed time series data set. More specifically, the wind speed boundary can be determined based on the preprocessed wind speed time series data set, that is, the maximum value in the preprocessed wind speed time series data set. and minimum value Then, based on the wind speed boundary and combined with each element in the preprocessed wind speed time series data set, the wind speed probability density function is calculated.

[0139] Formula (6)

[0140] in, represents the wind speed probability density function, express The elements, .

[0141] After obtaining the preprocessed wind direction time series data set, in order to obtain the characteristic information of wind direction changes in the wind farm and prepare for the subsequent determination of yaw control parameters, the present embodiment can first take the absolute value of each wind direction deviation contained in the preprocessed wind direction time series data set to obtain a wind direction deviation absolute value time series data set, and then obtain the wind direction deviation probability density function based on the wind direction deviation absolute value time series data set.

[0142] Formula (7)

[0143] Formula (8)

[0144] in, represents the absolute value time series data set of wind direction deviation, represents the probability density function of wind direction deviation, express The elements (i.e., the absolute value of wind direction deviation), .

[0145] Optionally, to facilitate subsequent calculations, this embodiment may also calculate a wind speed cumulative probability density function based on the above-mentioned wind speed probability density function; similarly, a wind direction deviation cumulative probability density function may be calculated based on the above-mentioned wind direction deviation probability density function.

[0146] Formula (9)

[0147] Formula (10)

[0148] in, represents the cumulative probability density function of wind speed, represents the cumulative probability density function of wind direction deviation, Represents the minimum wind direction deviation absolute value boundary determined based on the wind direction deviation absolute value time series data set, that is, the minimum value in the wind direction deviation absolute value time series data set.

[0149] The embodiment of the present application provides a method for determining a wind speed probability density function and a wind direction deviation probability density function, which can better reflect the distribution characteristics of the absolute values ​​of wind speed and wind direction deviation, so that yaw control can be performed more accurately in the subsequent process.

[0150] In some other embodiments of the present application, the process of obtaining the yaw control input data based on the original wind speed time series data set and the original wind direction time series data set in step S402 is introduced.

[0151] In this embodiment, the yaw control input data can be obtained by filtering. Specifically, based on the second time For the original wind speed time series dataset Filtering is performed to obtain the filtered wind speed, based on the third time For the original wind direction time series dataset Filtering is performed to obtain a filtered wind direction, and the filtered wind speed and filtered wind direction are determined as yaw control input data.

[0152] Optionally, the formula used for filtering may refer to the following formula (11) and formula (12).

[0153] Formula (11)

[0154] Formula (12)

[0155] in, Represents the wind speed after filtering, which is the input wind speed used for yaw control judgment and represents the average wind speed over a period of time; Indicates the wind direction after filtering, which is used for yaw deviation calculation for yaw control judgment and represents the average wind direction over a period of time.

[0156] Optional, the second time The value range is between 60 seconds and 300 seconds. The value range is between 10 seconds and 60 seconds.

[0157] This embodiment can effectively remove noise interference in the original wind speed time series data set and the original wind direction time series data set through filtering. The filtered wind speed and filtered wind direction can provide more reliable wind condition information, so that the yaw control process can predict the wind change trend in advance and make corresponding responses, which helps to reduce the frequent action of the yaw mechanism and improve the operating efficiency of the wind turbine.

[0158] In some other embodiments of the present application, the process of the above step S403 "determining initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters" is explained.

[0159] Step 1: Determine the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, cut-in wind speed and model design power curve in the design parameters.

[0160] Here, the cut-in wind speed is the starting wind speed for the wind turbine to be connected to the grid for power generation. When the wind speed is lower than the cut-in wind speed, the wind turbine will not start or be connected to the grid for power generation. The rated wind speed is the wind speed corresponding to the wind turbine working normally and running at rated power under matching power band. The model design power curve refers to the curve of the wind turbine's power generation per unit time changing with wind speed.

[0161] For yaw control, the yaw mode usually includes the following three modes: normal upwind mode, constant power yaw mode and yaw stop mode. In the normal upwind mode, the wind turbine rotor will automatically adjust its direction to keep it consistent with the wind direction, so as to maximize the capture of wind energy; when the wind speed is too high or too low, causing the output power of the wind turbine to deviate from the rated power, the system will enter the constant power yaw mode. In this mode, the wind turbine will adjust the angle and speed of the wind rotor to keep the output power fluctuating near the rated power, thereby protecting the wind turbine from overload or underload damage; when the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the wind turbine may enter the yaw stop mode, in which the wind rotor will stop rotating to avoid unnecessary wear and energy consumption.

[0162] The yaw mode switching wind speed is the wind speed at which the normal windward mode is switched to the constant power yaw mode. To this end, it is necessary to determine the power generation per unit time in the yaw control, and then determine the yaw mode switching wind speed based on the change in power generation.

[0163] In one possible implementation, “based on the wind speed probability density function and the rated wind speed in the design parameters , Cut-in wind speed and the model design power curve in the design parameters, determine the yaw mode switching wind speed” may include: based on the wind speed probability density function, the rated wind speed and the cut-in wind speed in the design parameters, determine the preprocessed wind speed time series data that falls into the third wind speed interval in the preprocessed wind speed time series data set as the target preprocessed wind speed time series data, wherein the third wind speed interval refers to the wind speed interval formed by the cut-in wind speed and the rated wind speed; based on the target preprocessed wind speed time series data, the wind speed probability density function and the model design power curve in the design parameters, determine the power generation corresponding to multiple sub-intervals included in the third wind speed interval, wherein the multiple sub-intervals included in the third wind speed interval are sub-intervals obtained by dividing the third wind speed interval according to the first wind speed interval; determine the yaw mode switching wind speed according to the power generation corresponding to the multiple sub-intervals included in the third wind speed interval and the preset power generation ratio.

[0164] The above-mentioned “third wind speed interval” is to be distinguished from the “first wind speed interval” and “second wind speed interval” below, and the order of the wind speed intervals is not limited.

[0165] Specifically, as described above about the wind speed probability density function, the wind speed probability density function refers to a function of all preprocessed wind speed time series data in the preprocessed wind speed time series data set and the corresponding wind speed probability density. Since only the preprocessed wind speed time series data falling in the third wind speed interval will affect the change in power generation, this embodiment can first determine the preprocessed wind speed time series data that falls in the third wind speed interval in the preprocessed wind speed time series data set based on the wind speed probability density function, the rated wind speed and the cut-in wind speed in the design parameters. For the convenience of subsequent introduction, the preprocessed wind speed time series data that falls in the third wind speed interval is defined as the target preprocessed wind speed time series data.

[0166] Optionally, this embodiment may determine the target pre-processed wind speed time series data based on the wind speed probability density function, the wind speed cumulative probability density function, the rated wind speed and the cut-in wind speed.

[0167] Next, the third wind speed interval may be divided according to the first wind speed interval to obtain a plurality of sub-intervals included in the third wind speed interval. Optionally, the first wind speed interval is 1 m / s.

[0168] Then, this embodiment can determine the power generation corresponding to the multiple sub-intervals included in the third wind speed interval based on the model design power curve in the design parameters and the target pre-processed wind speed time series data. Specifically, the target pre-processed wind speed time series data can be first corresponded to the multiple sub-intervals included in the third wind speed interval. Taking any sub-interval included in the third wind speed interval as an example, this embodiment can query the model power design curve based on the target pre-processed wind speed time series data in the sub-interval to obtain the power generation per unit time corresponding to the target pre-processed wind speed time series data in the sub-interval, and then based on the queried power generation per unit time, the total duration (which can be determined according to the specific scenario, for example, the total duration is 8760 hours included in a year), and the wind speed probability density corresponding to the target pre-processed wind speed time series data in the sub-interval (determined based on the wind speed probability density function), the power generation corresponding to the sub-interval is obtained.

[0169] For example, assuming that the queried power generation power per unit time is 20 kilowatts / hour, the total duration is 8760 hours, and the wind speed probability density corresponding to the target preprocessed wind speed time series data in the sub-interval is 1%, then the power generation corresponding to the sub-interval is: 20×8760×1%=1752 (kilowatts).

[0170] Finally, the yaw mode switching wind speed is determined based on the power generation corresponding to the multiple sub-intervals included in the third wind speed interval and the preset power generation ratio. Specifically, the total power generation is calculated based on the power generation corresponding to the multiple sub-intervals included in the third wind speed interval, and then the total power generation is multiplied by the preset power generation ratio. , get the target power generation, and then determine the wind speed corresponding to the target power generation, which is the yaw mode switching wind speed .

[0171] Optionally, this embodiment can calculate the cumulative power generation sequence according to the power generation corresponding to the multiple sub-intervals included in the third wind speed interval, and then determine the total power generation. For example, if the multiple sub-intervals included in the third wind speed interval are [3,4), [4,5), [5,6), [6,7), [7,8), then the cumulative power generation sequence is: the power generation corresponding to [3,4), the power generation corresponding to [3,5), the power generation corresponding to [3,6), the power generation corresponding to [3,7), and the power generation corresponding to [3,8).

[0172] Optional, preset power generation ratio The value can be [20%-40%].

[0173] Optionally, the process of "determining the wind speed corresponding to the target power generation" can be implemented based on the model design power curve. Of course, it can also be implemented by other methods, which is not limited in this application.

[0174] Step 2: Based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed and the yaw mode switching wind speed, determine the first wind direction deviation angle threshold and the second wind direction deviation angle threshold corresponding to the first wind speed interval, and the third wind direction deviation angle threshold and the fourth wind direction deviation angle threshold corresponding to the second wind speed interval.

[0175] The first wind speed interval refers to a wind speed interval formed by the yaw mode switching wind speed and the rated wind speed, and the second wind speed interval refers to a wind speed interval formed by the cut-in wind speed and the yaw mode switching wind speed.

[0176] In a possible implementation, this step may first obtain a joint probability distribution function based on the wind speed probability density function and the wind direction deviation probability density function.

[0177] Formula (13)

[0178] in, represents the joint probability distribution function.

[0179] Next, the cumulative probability density function of the wind direction deviation can be determined based on the probability density function of the wind direction deviation (such as the above formula (10)), and the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the first wind speed interval and the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the second wind speed interval can be determined based on the joint probability distribution function and the cumulative probability density function of the wind direction deviation. Here, the multiple sub-intervals included in the first wind speed interval are the sub-intervals obtained by dividing the first wind speed interval according to the second wind speed interval, and the multiple sub-intervals included in the second wind speed interval are the sub-intervals obtained by dividing the second wind speed interval according to the first wind speed interval.

[0180] For example, if the second wind speed interval is 1m / s, both the first wind speed interval and the second wind speed interval can be divided into multiple sub-intervals. Taking any sub-interval contained in the first wind speed interval as an example for explanation, this embodiment can determine the preprocessed wind speed time series data that falls into the sub-interval in the preprocessed wind speed time series data set in the previous text based on the joint probability distribution function, and then determine the wind direction deviation (i.e., the elements in the preprocessed wind direction time series data set) with the same sampling frequency as these preprocessed wind speed time series data, and then determine the corresponding wind direction deviation absolute value as the wind direction deviation absolute value of the sub-interval, and finally determine the probability distribution function of the wind direction deviation absolute value of the sub-interval based on the joint probability distribution function and the wind direction deviation cumulative probability density function.

[0181] For the first wind speed interval, this step will also perform the following processing:

[0182] Based on the probability distribution function of the absolute value of the wind direction deviation of each sub-interval contained in the first wind speed interval, the cumulative probability is determined as the first probability The absolute value and cumulative probability of the first wind direction deviation are the second probability The second wind direction deviation absolute value is used as the first wind direction deviation absolute value and the second wind direction deviation absolute value corresponding to each sub-interval included in the first wind speed interval. Here, the first probability is less than the second probability.

[0183] The average value of the absolute values ​​of the first wind direction deviations corresponding to all subintervals included in the first wind speed interval is calculated as the first wind direction deviation angle threshold, and the average value of the absolute values ​​of the second wind direction deviations corresponding to all subintervals included in the first wind speed interval is calculated as the second wind direction deviation angle threshold.

[0184] Optionally, the absolute values ​​of the first wind direction deviations corresponding to all sub-intervals included in the first wind speed interval form a first array , each element in the first array represents the absolute value of the first wind direction deviation corresponding to a subinterval included in the first wind speed interval; similarly, the absolute values ​​of the second wind direction deviations corresponding to all subintervals included in the first wind speed interval form the second array , each element in the second array represents an absolute value of a second wind direction deviation corresponding to a sub-interval included in the first wind speed interval.

[0185] Then, this embodiment can calculate the mean of all elements in the first array to obtain the first wind direction deviation angle threshold value: ; Calculate the mean of all elements in the second array to obtain the second wind direction deviation angle threshold .

[0186] For the second wind speed range, this step will also perform the following processing:

[0187] Based on the probability distribution function of the absolute value of the wind direction deviation of each sub-interval contained in the second wind speed interval, the cumulative probability is determined to be the third probability The absolute value and cumulative probability of the third wind direction deviation is the fourth probability The fourth wind direction deviation absolute value is used as the third wind direction deviation absolute value and the fourth wind direction deviation absolute value corresponding to each sub-interval included in the second wind speed interval. Here, the third probability is less than the fourth probability.

[0188] The average value of the absolute values ​​of the third wind direction deviations corresponding to all subintervals included in the second wind speed interval is calculated as the third wind direction deviation angle threshold, and the average value of the absolute values ​​of the fourth wind direction deviations corresponding to all subintervals included in the second wind speed interval is calculated as the fourth wind direction deviation angle threshold.

[0189] Optionally, the absolute values ​​of the third wind direction deviations corresponding to all sub-intervals included in the second wind speed interval form a third array , each element in the third array represents the absolute value of the third wind direction deviation corresponding to a sub-interval included in the second wind speed interval; similarly, the absolute values ​​of the fourth wind direction deviations corresponding to all sub-intervals included in the second wind speed interval form the fourth array , each element in the fourth array represents an absolute value of a fourth wind direction deviation corresponding to a sub-interval included in the second wind speed interval.

[0190] Then, this embodiment can calculate the mean of all elements in the third array to obtain the third wind direction deviation angle threshold value: ; Calculate the mean of all elements in the fourth array to obtain the fourth wind direction deviation angle threshold .

[0191] In this step, the first probability is less than the second probability, so that the first wind direction deviation angle threshold for ~ The small wind direction deviation angle threshold between the first and second wind direction deviation angle thresholds for ~ The large wind direction deviation angle threshold between < ; The third probability is less than the fourth probability, so that the third wind direction deviation angle threshold for ~ The small wind direction deviation angle threshold between the fourth wind direction deviation angle threshold for ~ The large wind direction deviation angle threshold between < .

[0192] Optionally, the value range of the first probability is 30%~40%, the value range of the second probability is 50%~60%, the value range of the third probability is 50%~60%, and the value range of the fourth probability is 80%~90%.

[0193] It should be noted that the value ranges of the above four probabilities are only examples and are not intended to limit the present application.

[0194] Step three: obtain the first duration corresponding to the first wind direction deviation angle threshold and the third wind direction deviation angle threshold, the second duration corresponding to the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold, the yaw rate, the yaw deviation threshold and the yaw deviation stabilization time, wherein the yaw deviation threshold and the yaw deviation stabilization time are used to obtain the yaw stop condition.

[0195] Taking into account the randomness of wind direction changes and the effectiveness of the yaw system action, it is necessary to set the duration of wind direction deviation exceeding the threshold and the yaw rate for the yaw start judgment conditions under different wind direction deviation angle thresholds. In the specific implementation, the first wind direction deviation angle threshold can be used to determine the duration of wind direction deviation exceeding the threshold and the yaw rate. And the third wind direction deviation angle threshold (i.e. small angle wind direction deviation angle threshold) Set the first duration ; For the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold (i.e. large angle wind direction deviation angle threshold) Set the second duration , and set the yaw rate .

[0196] Those skilled in the art should understand that when the absolute value of the wind direction deviation is small and lasts for a long time, yaw control is required; when the absolute value of the wind direction deviation is large and lasts for a short time, yaw control is also required. and , you can set a larger first duration, for large angle wind direction deviation angle threshold and , a smaller first duration can be set, that is, > .

[0197] Then, this embodiment can obtain the first duration corresponding to the preset first wind direction deviation angle threshold and the third wind direction deviation angle threshold , the second duration corresponding to the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold and yaw rate .

[0198] Optional, first duration The value range is between 60 seconds and 300 seconds. The second duration The value range is between 10 seconds and 120 seconds.

[0199] Optional, yaw rate The value range is 0.1deg / s~0.5deg / s, which is usually specified by the component design engineer.

[0200] It should be understood that in yaw control, a yaw stop condition needs to be set. In a possible implementation, since the wind turbine will rotate during the yaw process to reduce the deviation between the wind rotor plane of the wind turbine and the incoming wind, and the wind direction will also change during the yaw process, the deviation between the wind rotor plane of the wind turbine and the wind direction can be obtained in real time to determine whether the wind direction is aligned. Then, this embodiment can be based on the real-time yaw deviation Determine the yaw stop condition.

[0201] Optionally, the real-time yaw deviation can be calculated based on the fourth time. Perform filtering to obtain the filtered yaw deviation.

[0202] Formula (14)

[0203] in, represents the yaw deviation after filtering, Indicates the fourth time.

[0204] Then, in this embodiment, the yaw deviation threshold can be set based on the filtered yaw deviation. and yaw deviation stabilization time Then, the yaw stop condition is: the yaw deviation after filtering is greater than the yaw deviation threshold The duration of the yaw deviation stabilization time .

[0205] When setting the yaw deviation threshold and yaw deviation stabilization time After that, this embodiment can obtain the preset yaw deviation threshold and yaw deviation stabilization time .

[0206] Step 4: Determine the yaw mode switching wind speed, the first wind direction deviation angle threshold, the third wind direction deviation angle threshold, the first duration, the second wind direction deviation angle threshold, the fourth wind direction deviation angle threshold, the second duration, the yaw rate, the yaw deviation threshold and the yaw deviation stabilization time as initial yaw control parameters.

[0207] In this embodiment, the above yaw mode can be switched to wind speed , First wind direction deviation angle threshold , The third wind direction deviation angle threshold , first duration , Second wind direction deviation angle threshold , Fourth wind direction deviation angle threshold , Second Duration , yaw rate , yaw deviation threshold and yaw deviation stabilization time Determined as the initial yaw control parameters.

[0208] In summary, this embodiment provides a process for determining initial yaw control parameters, which facilitates subsequent simulation.

[0209] The above-mentioned embodiments of the present application fully consider the yaw design requirements and the actual wind conditions of the wind farm, and achieve the best match between the wind farm power generation and the yaw characteristic index. For the installed wind farm, the power generation can be improved by optimizing the yaw control parameters, and for the planned wind farm, the yaw control strategy can be customized, which can ensure that the power generation loss rate caused by wind direction deviation is minimized at the beginning of installation.

[0210] A yaw control method provided by an embodiment of the present application is introduced above, and a device for executing the above yaw control method will be introduced below.

[0211] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of a yaw control device provided in an embodiment of the present application. Figure 5 As shown, the device may include:

[0212] The data acquisition module 501 is used to acquire an original wind speed time series data set, an original wind direction time series data set and design parameters of the wind turbine generator set, wherein the sampling frequency of the original wind speed time series data set and the original wind direction time series data set is the same;

[0213] A first data processing module 502 is used to obtain a wind speed probability density function, a wind direction deviation probability density function and yaw control input data based on an original wind speed time series data set and an original wind direction time series data set;

[0214] A second data processing module 503 is used to determine initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters;

[0215] The yaw simulation module 504 is used to perform simulation according to the yaw control input data and the initial yaw control parameters, so as to determine the target yaw control parameters based on the simulation results and the preset multi-objective constraint conditions.

[0216] In a possible implementation, when the first data processing module obtains the wind speed probability density function and the wind direction deviation probability density function based on the original wind speed time series data set and the original wind direction time series data set, it can be specifically used to:

[0217] Based on the first time, the original wind speed time series data set and the original wind direction time series data set are respectively divided into bins to obtain original wind speed time series data subsets of multiple time periods and original wind direction time series data subsets of multiple time periods;

[0218] The original wind speed time series data subset of each period and the original wind direction time series data subset of each period are respectively calculated to obtain the average wind speed value and the average wind direction value of each period;

[0219] For the wind speed average values ​​of multiple time periods, the average wind speed average values ​​of each two adjacent time periods are calculated to obtain a preprocessed wind speed time series data set;

[0220] Based on the preprocessed wind speed time series data set, the wind speed probability density function is obtained;

[0221] For the wind direction average values ​​of multiple time periods, the difference between the wind direction average values ​​of each two adjacent time periods is calculated to obtain a preprocessed wind direction time series data set;

[0222] Taking the absolute value of each wind direction deviation contained in the preprocessed wind direction time series data set, to obtain a wind direction deviation absolute value time series data set;

[0223] Based on the wind direction deviation absolute value time series data set, the wind direction deviation probability density function is obtained.

[0224] In a possible implementation, when the first data processing module obtains the yaw control input data based on the original wind speed time series data set and the original wind direction time series data set, it can be specifically used to:

[0225] Performing filtering processing on the original wind speed time series data set based on the second time to obtain a filtered wind speed;

[0226] Performing filtering processing on the original wind direction time series data set based on the third time to obtain a filtered wind direction;

[0227] The filtered wind speed and filtered wind direction are determined as yaw control input data.

[0228] In a possible implementation, when determining the initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters, the second data processing module may be specifically used to:

[0229] Based on the wind speed probability density function and the rated wind speed, cut-in wind speed and model design power curve in the design parameters, the yaw mode switching wind speed is determined;

[0230] Based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed and the yaw mode switching wind speed, determine a first wind direction deviation angle threshold and a second wind direction deviation angle threshold corresponding to the first wind speed interval, and a third wind direction deviation angle threshold and a fourth wind direction deviation angle threshold corresponding to the second wind speed interval, wherein the first wind speed interval refers to a wind speed interval formed by the yaw mode switching wind speed and the rated wind speed, and the second wind speed interval refers to a wind speed interval formed by the cut-in wind speed and the yaw mode switching wind speed;

[0231] Obtaining a first duration corresponding to the first wind direction deviation angle threshold and the third wind direction deviation angle threshold, a second duration corresponding to the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold, a yaw rate, a yaw deviation threshold, and a yaw deviation stabilization time, wherein the yaw deviation threshold and the yaw deviation stabilization time are used to obtain a yaw stop condition;

[0232] The yaw mode switching wind speed, the first wind direction deviation angle threshold, the third wind direction deviation angle threshold, the first duration, the second wind direction deviation angle threshold, the fourth wind direction deviation angle threshold, the second duration, the yaw rate, the yaw deviation threshold and the yaw deviation stabilization time are determined as initial yaw control parameters.

[0233] In a possible implementation, when determining the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, cut-in wind speed and model design power curve in the design parameters, the second data processing module may be specifically used to:

[0234] Based on the wind speed probability density function, the rated wind speed and the cut-in wind speed in the design parameters, the preprocessed wind speed time series data falling into the third wind speed interval in the preprocessed wind speed time series data set is determined as the target preprocessed wind speed time series data, wherein the third wind speed interval refers to the wind speed interval formed by the cut-in wind speed and the rated wind speed;

[0235] Based on the target pre-processed wind speed time series data, the wind speed probability density function and the model design power curve in the design parameters, determine the power generation corresponding to the multiple sub-intervals included in the third wind speed interval, wherein the multiple sub-intervals included in the third wind speed interval are sub-intervals obtained by dividing the third wind speed interval according to the first wind speed interval;

[0236] The yaw mode switching wind speed is determined according to the power generation amounts respectively corresponding to the plurality of sub-intervals included in the third wind speed interval and the preset power generation proportions.

[0237] In a possible implementation, the second data processing module determines the first wind direction deviation angle threshold and the second wind direction deviation angle threshold corresponding to the first wind speed interval, and the third wind direction deviation angle threshold and the fourth wind direction deviation angle threshold corresponding to the second wind speed interval based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed and the yaw mode switching wind speed. Specifically, it can be used to:

[0238] A joint probability distribution function is obtained based on the wind speed probability density function and the wind direction deviation probability density function;

[0239] Determine the cumulative probability density function of wind direction deviation based on the probability density function of wind direction deviation;

[0240] Based on the joint probability distribution function and the cumulative probability density function of the wind direction deviation, determine the probability distribution function of the absolute value of the wind direction deviation of each of the multiple subintervals included in the first wind speed interval, and the probability distribution function of the absolute value of the wind direction deviation of each of the multiple subintervals included in the second wind speed interval, wherein the multiple subintervals included in the first wind speed interval are subintervals obtained by dividing the first wind speed interval according to the second wind speed interval, and the multiple subintervals included in the second wind speed interval are subintervals obtained by dividing the second wind speed interval according to the first wind speed interval;

[0241] Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the first wind speed interval, determine a first wind direction deviation absolute value with a cumulative probability of a first probability and a second wind direction deviation absolute value with a cumulative probability of a second probability as the first wind direction deviation absolute value and the second wind direction deviation absolute value corresponding to each subinterval included in the first wind speed interval;

[0242] Calculate the average of the absolute values ​​of the first wind direction deviations corresponding to all sub-intervals included in the first wind speed interval as the first wind direction deviation angle threshold;

[0243] Calculate the average of the absolute values ​​of the second wind direction deviations corresponding to all sub-intervals included in the first wind speed interval as the second wind direction deviation angle threshold;

[0244] Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the second wind speed interval, determine a third wind direction deviation absolute value with a cumulative probability of a third probability and a fourth wind direction deviation absolute value with a cumulative probability of a fourth probability as the third wind direction deviation absolute value and the fourth wind direction deviation absolute value corresponding to each subinterval included in the second wind speed interval;

[0245] Calculate the average of the absolute values ​​of the third wind direction deviations corresponding to all sub-intervals included in the second wind speed interval as the third wind direction deviation angle threshold;

[0246] An average of the fourth wind direction deviation absolute values ​​corresponding to all sub-intervals included in the second wind speed interval is calculated as the fourth wind direction deviation angle threshold.

[0247] The yaw control device provided in the present application corresponds to the yaw control method described above, and details can be found in the above description, which will not be repeated here.

[0248] The present application also provides an electronic device in an embodiment. Figure 6 As shown, it shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present application. The electronic device in the embodiment of the present application may include but is not limited to fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 6The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0249] like Figure 6 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 to a random access memory (RAM) 603. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0250] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a memory card, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0251] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any one of the yaw control methods provided in the embodiments of the present application.

[0252] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any yaw control method provided in the embodiment of the present application.

[0253] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0254] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0255] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0256] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A yaw control method, characterized in that: include: Acquire an original wind speed time series data set, an original wind direction time series data set, and design parameters of a wind turbine generator set, wherein the sampling frequencies of the original wind speed time series data set and the original wind direction time series data set are the same; Based on the original wind speed time series data set and the original wind direction time series data set, a wind speed probability density function, a wind direction deviation probability density function and yaw control input data are obtained; Determining initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters; A simulation is performed according to the yaw control input data and the initial yaw control parameters to determine target yaw control parameters based on simulation results and preset multi-objective constraint conditions.

2. The yaw control method according to claim 1, characterized in that: Based on the original wind speed time series data set and the original wind direction time series data set, the wind speed probability density function and the wind direction deviation probability density function are obtained, including: Based on the first time, the original wind speed time series data set and the original wind direction time series data set are respectively divided into bins to obtain original wind speed time series data subsets of multiple time periods and original wind direction time series data subsets of the multiple time periods; Performing mean calculations on the original wind speed time series data subset of each time period and the original wind direction time series data subset of each time period, respectively, to obtain the wind speed average value of each time period and the wind direction average value of each time period; For the wind speed average values ​​of the multiple time periods, the average value of the wind speed of each two adjacent time periods is calculated to obtain a preprocessed wind speed time series data set; Based on the preprocessed wind speed time series data set, obtaining the wind speed probability density function; For the wind direction average values ​​of the multiple time periods, the difference between the wind direction average values ​​of each two adjacent time periods is calculated to obtain a preprocessed wind direction time series data set; Taking an absolute value of each wind direction deviation included in the preprocessed wind direction time series data set to obtain a wind direction deviation absolute value time series data set; Based on the wind direction deviation absolute value time series data set, the wind direction deviation probability density function is obtained.

3. The yaw control method according to claim 1, characterized in that: The yaw control input data is obtained based on the original wind speed time series data set and the original wind direction time series data set, including: Performing filtering processing on the original wind speed time series data set based on the second time to obtain a filtered wind speed; Performing filtering processing on the original wind direction time series data set based on a third time to obtain a filtered wind direction; The filtered wind speed and the filtered wind direction are determined as the yaw control input data.

4. The yaw control method according to claim 2, characterized in that: The determining of the initial yaw control parameters based on the wind speed probability density function, the wind direction deviation probability density function and the design parameters comprises: Determining the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, cut-in wind speed and aircraft model design power curve in the design parameters; Based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed and the yaw mode switching wind speed, determine a first wind direction deviation angle threshold and a second wind direction deviation angle threshold corresponding to a first wind speed interval, and a third wind direction deviation angle threshold and a fourth wind direction deviation angle threshold corresponding to a second wind speed interval, wherein the first wind speed interval refers to a wind speed interval formed by the yaw mode switching wind speed and the rated wind speed, and the second wind speed interval refers to a wind speed interval formed by the cut-in wind speed and the yaw mode switching wind speed; Acquire a first duration corresponding to the first wind direction deviation angle threshold and the third wind direction deviation angle threshold, a second duration corresponding to the second wind direction deviation angle threshold and the fourth wind direction deviation angle threshold, a yaw rate, a yaw deviation threshold, and a yaw deviation stabilization time, wherein the yaw deviation threshold and the yaw deviation stabilization time are used to obtain a yaw stop condition; The yaw mode switching wind speed, the first wind direction deviation angle threshold, the third wind direction deviation angle threshold, the first duration, the second wind direction deviation angle threshold, the fourth wind direction deviation angle threshold, the second duration, the yaw rate, the yaw deviation threshold and the yaw deviation stabilization time are determined as the initial yaw control parameters.

5. The yaw control method according to claim 4, characterized in that: The determining of the yaw mode switching wind speed based on the wind speed probability density function and the rated wind speed, the cut-in wind speed and the model design power curve in the design parameters includes: Based on the wind speed probability density function, the rated wind speed and the cut-in wind speed in the design parameters, determining the preprocessed wind speed time series data that falls into the third wind speed interval in the preprocessed wind speed time series data set as the target preprocessed wind speed time series data, wherein the third wind speed interval refers to the wind speed interval formed by the cut-in wind speed and the rated wind speed; Based on the target pre-processed wind speed time series data, the wind speed probability density function and the model design power curve in the design parameters, determine the power generation corresponding to the multiple sub-intervals included in the third wind speed interval, wherein the multiple sub-intervals included in the third wind speed interval are sub-intervals obtained by dividing the third wind speed interval according to the first wind speed interval; The yaw mode switching wind speed is determined according to the power generation amounts respectively corresponding to a plurality of sub-intervals included in the third wind speed interval and a preset power generation proportion.

6. The yaw control method according to claim 4, characterized in that: The determining, based on the wind speed probability density function, the wind direction deviation probability density function, the rated wind speed, the cut-in wind speed, and the yaw mode switching wind speed, a first wind direction deviation angle threshold and a second wind direction deviation angle threshold corresponding to a first wind speed interval, and a third wind direction deviation angle threshold and a fourth wind direction deviation angle threshold corresponding to a second wind speed interval, comprises: Obtaining a joint probability distribution function based on the wind speed probability density function and the wind direction deviation probability density function; Determine a wind direction deviation cumulative probability density function based on the wind direction deviation probability density function; Based on the joint probability distribution function and the cumulative probability density function of the wind direction deviation, determine the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the first wind speed interval, and the probability distribution function of the absolute value of the wind direction deviation of each of the multiple sub-intervals included in the second wind speed interval, wherein the multiple sub-intervals included in the first wind speed interval are sub-intervals obtained by dividing the first wind speed interval according to the second wind speed interval, and the multiple sub-intervals included in the second wind speed interval are sub-intervals obtained by dividing the second wind speed interval according to the first wind speed interval; Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the first wind speed interval, determine a first wind direction deviation absolute value with a cumulative probability of a first probability and a second wind direction deviation absolute value with a cumulative probability of a second probability as the first wind direction deviation absolute value and the second wind direction deviation absolute value corresponding to each subinterval included in the first wind speed interval; Calculating an average of first wind direction deviation absolute values ​​corresponding to all subintervals included in the first wind speed interval as the first wind direction deviation angle threshold; Calculating an average of the absolute values ​​of the second wind direction deviations corresponding to all subintervals included in the first wind speed interval as the second wind direction deviation angle threshold; Based on the probability distribution function of the absolute value of the wind direction deviation of each subinterval included in the second wind speed interval, determine a third wind direction deviation absolute value with a cumulative probability of a third probability and a fourth wind direction deviation absolute value with a cumulative probability of a fourth probability as the third wind direction deviation absolute value and the fourth wind direction deviation absolute value corresponding to each subinterval included in the second wind speed interval; Calculating an average of the absolute values ​​of the third wind direction deviations corresponding to all sub-intervals included in the second wind speed interval as the third wind direction deviation angle threshold; An average of the fourth wind direction deviation absolute values ​​corresponding to all sub-intervals included in the second wind speed interval is calculated as the fourth wind direction deviation angle threshold.

7. A yaw control device, characterized in that: include: A data acquisition module, used to acquire an original wind speed time series data set, an original wind direction time series data set and design parameters of a wind turbine generator set, wherein the sampling frequency of the original wind speed time series data set and the original wind direction time series data set is the same; A first data processing module, configured to obtain a wind speed probability density function, a wind direction deviation probability density function and yaw control input data based on the original wind speed time series data set and the original wind direction time series data set; A second data processing module, configured to determine an initial yaw control parameter based on the wind speed probability density function, the wind direction deviation probability density function and the design parameter; The yaw simulation module is used to perform simulation according to the yaw control input data and the initial yaw control parameters, so as to determine the target yaw control parameters based on the simulation results and the preset multi-objective constraint conditions.

8. A computer program product, characterized in that The method comprises computer-readable instructions, and when the computer-readable instructions are executed on an electronic device, the electronic device implements the yaw control method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the yaw control method according to any one of claims 1 to 6.

10. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the yaw control method as claimed in any one of claims 1 to 6.

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