WO2023020524A1 - 一种风电机组切入/切出风速调优方法、系统及设备介质 - Google Patents

一种风电机组切入/切出风速调优方法、系统及设备介质 Download PDF

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WO2023020524A1
WO2023020524A1 PCT/CN2022/112933 CN2022112933W WO2023020524A1 WO 2023020524 A1 WO2023020524 A1 WO 2023020524A1 CN 2022112933 W CN2022112933 W CN 2022112933W WO 2023020524 A1 WO2023020524 A1 WO 2023020524A1
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cut
wind speed
wind
power generation
unit
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PCT/CN2022/112933
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English (en)
French (fr)
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万芳
童彤
曹朔
唐云
任鑫
王恩民
王�华
赵鹏程
杜静宇
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华能华家岭风力发电有限公司
中国华能集团清洁能源技术研究院有限公司
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Publication of WO2023020524A1 publication Critical patent/WO2023020524A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/30—Wind power
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the invention belongs to the field of wind turbine design and debugging, and relates to a wind turbine cut-in/cut-out wind speed tuning method, system and equipment medium.
  • the frequency of wind speed around the cut-in wind speed often accounts for a large proportion, and the cut-out wind speed will also be affected by the environmental conditions of the wind farm.
  • the cut-in wind speed affects the start and stop of the wind turbine. If the cut-in wind speed is set too small, it will cause frequent start and stop of the wind turbine; if the cut-in wind speed is set too high, it will cause the wind turbine to start abnormally even at a high wind speed, directly affecting the generating capacity of the unit. .
  • the cut-out wind speed will directly affect the fatigue life and power generation of the wind turbine.
  • the purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art, and provide a wind turbine cut-in/cut-out wind speed tuning method, system and equipment medium, which can obtain a better cut-in/cut-out wind speed of the wind turbine, so that the wind turbine is normally high-efficiency running power generation.
  • the present invention adopts the following technical solutions to achieve:
  • a wind turbine cut-in/cut-out wind speed tuning method comprising the following process:
  • S1 conduct governance analysis on the big data of wind turbine operation, identify and eliminate abnormal data points, and use multiple curve functions to achieve full and smooth fitting of the wind turbine power curve, thereby establishing the power generation capacity curve function of the wind turbine;
  • the demanded generation capacity of the unit is obtained, and the cut-in wind speed and cut-out wind speed are taken as independent variables, and the demanded unit power generation capacity and fatigue impact load are taken as targets to obtain the required cut-in wind speed value and cut-in wind speed value.
  • Wind speed; or take the required power generation as the target, and the fatigue damage value is less than the design value constraint preference, and transform the two objectives into a single objective function for the required power generation to seek an optimal solution.
  • the wind data in the area of the wind turbine is acquired, and after removing the abnormal data points corresponding to the simultaneous operation data to obtain discrete power data points, a curve fitting method is used to establish the power generation capacity curve function of the wind turbine.
  • the process of identifying and eliminating abnormal data points is as follows: first, the time stamp in the operating data is used as a time series feature, the power and the speed of the wind rotor are used as physical features, and the cut-in wind speed and cut-out wind speed are used as wind speed judgment features. Secondly, an unsupervised model is used to filter out abnormal data points outside the threshold range of power cuts, shutdowns, power limits, and variables.
  • the wind speed and frequency of the wind farm are calculated according to the wind measurement data at the wind farm site, and if the wind measurement data is less than a certain period of time, the wind resource calculation method is used to supplement and correct the wind speed distribution function model.
  • the calculation process of the fatigue load relationship function is: under the current cut-in wind speed and cut-out wind speed conditions, according to the wind frequency function, calculate the equivalent fatigue load value of the whole wind turbine and the key position in the key direction, and compare with the Comparing the equivalent fatigue design value of the unit, if the former is less than the latter, then the cut-in wind speed is a feasible solution; If the value is less than 1, the whole wind turbine and key positions are safe, otherwise, it is not safe.
  • one way to cut out the wind speed is to cut out the power of the unit directly to zero when the cut out wind speed meets a certain standard, and another way is to cut out the unit in the form of a power reduction function.
  • the cut-in wind speed and the cut-out wind speed as the independent variables and the larger power generation and less fatigue damage as the objective function
  • a cut-in/cut-out wind speed tuning system for wind turbines comprising:
  • Wind turbine generating capacity power curve function building module which is used to manage and analyze wind turbine operation big data, identify and eliminate abnormal data points, and use multiple curve functions to fully and smoothly fit the wind turbine power curve, thereby establishing wind turbine power generation Capability power curve function;
  • the annual power generation capacity calculation module of the unit is used to calculate the wind speed and frequency of the wind farm, and further fit the wind speed distribution function model; then obtain the cut-in wind speed and cut-out wind speed of the wind turbine according to the wind speed distribution function model, and use the power generation capacity curve function of the wind turbine , to calculate the annual generating capacity of the unit;
  • Fatigue load relationship function calculation module used to count the big data of the actual operating state of the wind speed of the unit at a certain cut-in wind speed and a certain cut-out wind speed, through the big data of the actual operating state, according to the time series, occurrence times and working conditions, through the fatigue load calculation method Calculate the cut-in/cut-out wind speed and the fatigue load function of the unit and main components;
  • the cut-in and cut-out wind speed calculation module is used to obtain the required power generation capacity of the unit according to the annual power generation capacity of the unit.
  • the cut-in wind speed and the cut-out wind speed are taken as independent variables, and the required power generation capacity of the unit and the fatigue impact load are taken as targets to obtain Cut-in wind speed and cut-out wind speed that meet the demand; or take the required power generation as the target, and the fatigue damage value is less than the design value of the constraint preference, transform the two objectives into a single objective function for the demanded power generation to seek an optimal solution.
  • a computer device including a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, the wind turbine generator set as described in any one of the above is realized Steps to cut in/out the wind speed tuning method.
  • a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it realizes the steps of the wind turbine cut-in/cut-out wind speed tuning method described in any one of the above.
  • the present invention has the following beneficial effects:
  • the present invention is based on the big data of wind turbine operation.
  • it uses an unsupervised algorithm to complete the management and elimination of the big data of the wind turbine operation, and obtains the power curve of the wind turbine according to the power fitting of the wind measuring equipment data and the operating data; Data or/and wind resource calculation method, and then get the wind speed distribution model.
  • the annual power generation capacity function relation of the unit is calculated; on the other hand, the fatigue damage of the unit and its main components is obtained by using the fatigue load simulation calculation method based on the actual state data of the unit operation. function relationship.
  • the method of the present invention at least helps to achieve the following goals: one is to reduce and control the fatigue load of the frequent start-stop and cut-out phase of the unit; the other is to effectively increase the power generation of wind turbines, especially wind turbines installed in low wind speed areas
  • the third is to effectively simulate and analyze the load of the unit and its main components in combination with the actual operating state; the fourth is to analyze and verify the adaptability of the wind turbine to a specific wind farm.
  • Fig. 1 is a flow chart of the cut-in/cut-out wind speed tuning method of the present invention.
  • Step 1 Govern and analyze the big data of wind turbine operation, identify and eliminate abnormal data points, and use multiple curve functions to fully and smoothly fit the power curve of wind turbines, thereby establishing a high-precision wind turbine power generation capacity power curve function.
  • the time stamp in the operation data is used as the time series feature
  • the power and the speed of the wind rotor are used as the physical feature
  • the cut-in wind speed and cut-out wind speed are used as the wind speed judgment feature.
  • an unsupervised model based on clustering algorithm or isolation forest algorithm, vector machine algorithm, Gaussian distribution and other algorithms to filter out abnormal data points such as power cuts, shutdowns, power limits, and variables outside the threshold range.
  • Step 2 According to the wind measurement data of the wind farm site, such as wind speed and corresponding duration, the wind speed frequency of the wind farm will be counted, and the wind speed distribution function model will be further fitted. Secondly, according to the cut-in and cut-out wind speeds of the wind turbines, the annual power generation capacity of the wind turbines is calculated. If the wind measurement data is not enough for a certain period of time, the wind resource calculation method will be used to supplement and correct the wind speed distribution function model.
  • wind measuring equipment such as laser radar, wind measuring tower or wind speed and direction equipment on the nacelle
  • apply the screening method described above and remove the abnormal data points corresponding to the simultaneous operation data to obtain discrete
  • a high-precision relationship curve between the wind speed and the generating capacity of the unit is established by using an appropriate curve fitting method.
  • the wind resource calculation method will be based on the measurement-correlation-prediction algorithm (MCP), the numerical weather forecast model (WRF) and the micro-scale coupling algorithm in computational fluid dynamics (CFD) , considering the existing wind measurement data, average wind speed and energy density comprehensively, a more reliable wind speed distribution function model is obtained through supplementary correction.
  • MCP measurement-correlation-prediction algorithm
  • WRF numerical weather forecast model
  • CCD computational fluid dynamics
  • Step 3 According to the big data of wind turbine operation, accumulate statistics and evaluate the wind speed of the unit at a certain cut-in wind speed and a certain cut-out wind speed.
  • the environmental conditions of the location calculate and simulate the operating state of the unit under this working condition, and simulate and supplement the performance of the operating state of the unit when the actual working condition is insufficient.
  • Step 4 Calculate the fatigue damage of the unit and main components through the fatigue load calculation method based on the big data of the actual operating state (such as the number of starts and stops, unit vibration, load, etc.), according to the time series, occurrence times and working conditions value.
  • the big data of the actual operating state such as the number of starts and stops, unit vibration, load, etc.
  • the wind turbine fatigue load calculation method is based on the wind speed, wind direction, incoming flow angle, turbulence intensity, temperature and other environmental conditions, and on the basis of the established wind turbine dynamic model, using the wind turbine simulation analysis software or program to calculate the current cut-in wind speed and Under the condition of cutting out the wind speed, according to the wind frequency function, calculate the equivalent fatigue load value of the whole machine and key positions of the wind turbine in the key direction, and compare it with the equivalent fatigue design value of the unit. If the former is smaller than the latter, then the The cut-in wind speed is a feasible solution; if the former is about the latter, it needs to be further checked on the basis of the finite element analysis method to obtain its damage value. If the damage value is less than 1, the whole machine and key positions of the wind turbine are safe. not safe.
  • Step 5 Based on the optimization method, take the cut-in wind speed and the cut-out wind speed as independent variables, and aim at both the greater power generation capacity of the unit and the smaller fatigue impact load, and obtain better values of the cut-in wind speed and the cut-out wind speed.
  • the cut-out wind speed can be either a value or a function.
  • the optimization method will use a global optimization method to solve, and intelligent algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm or neural network can be used to solve the problem.
  • intelligent algorithms such as genetic algorithm, simulated annealing algorithm, particle swarm or neural network can be used to solve the problem.
  • the cut-out wind speed is generally based on the fact that when the cut-out wind speed meets a certain standard, the power of the unit is directly reduced to zero when the unit cuts out. Another way is that the unit is cut out in the form of a power reduction function.
  • the function form can be a negative linear function with a slope, a curve function, Functional forms such as step-decreasing functions.
  • the design conditions of the unit such as the model of the unit itself does not change (that is, the rated power generation, hub height, drive chain, blades, etc. remain unchanged), to obtain a better cut-in Wind speed and cut-out wind speed.
  • Step 1 Manage and analyze the big data of wind turbine operation, and collect wind speed, wind direction, temperature, air pressure and other meteorological quantities at the hub height of the wind turbine, as well as the active power of the wind turbine itself, and the operating status (grid connection, fault) data, and identify and eliminate abnormal data point, the power curve of the wind turbine is fitted with a multi-curve function.
  • the relationship between the power generated by the wind turbine and the wind speed is
  • v i represents the wind speed in m/s
  • P i represents the power generated by the wind turbine at the corresponding wind speed v i , kW.
  • Step 2 According to the wind measurement data of the wind farm site and the supplementary correction method of wind resource calculation, the relational expression of the wind speed distribution function model of the wind farm is obtained
  • v i represents the wind speed in m/s
  • a i represents the frequency corresponding to the wind speed v i
  • f((7) represents a function, which can be a function such as Weibull distribution model, Rayleigh distribution model and corresponding normal distribution model.
  • Step 3 According to the big data of wind turbine operation, the actual operating status of the wind turbine (such as the number of start-up and stop times, vibration, load, etc.) The environmental conditions at the unit position and the operating state of the unit under the operating conditions are calculated and simulated.
  • Step 4 In the case of a certain cut-in wind speed v cut-in and cut-out wind speed v cut-out , the operation status big data (such as the number of start-up and stop times, unit vibration, load, etc.) obtained through step 3, according to the time series, The number of occurrences and working conditions, the fatigue load D relationship function of the unit and related components under the cut-in wind speed v cut-in and cut-out wind speed v cut-out is calculated by the fatigue load calculation method:
  • the cut-in wind speed can be set within the range of 3m/s (for models with small unit capacity and large wind rotor diameter, the lower limit can be set lower) to 5m/s,
  • the cut-out wind speed is generally set at 20m/s (for wind turbines in higher wind speed areas, the lower limit can be set lower) to 25m/s (for offshore wind turbines and units in low wind speed areas, the value can be adjusted appropriately) raised higher).
  • the cut-out wind speed may not be a constant value, and the cut-out wind speed may be expressed as a function.
  • the cut-in wind speed v cut-in and the cut-out wind speed v cut-out are taken as independent variables, and the value range of the independent variables is roughly within the constraint conditions mentioned above. As the goal, find the better cut-in wind speed value and cut-out wind speed value.
  • MinD f 4 (v cut-in , v cut-out )
  • the cut-in/cut-out wind speed tuning system for wind turbines includes:
  • Wind turbine generating capacity power curve function building module which is used to manage and analyze wind turbine operation big data, identify and eliminate abnormal data points, and use multiple curve functions to fully and smoothly fit the wind turbine power curve, thereby establishing wind turbine power generation Capability power curve function.
  • the annual power generation capacity calculation module of the unit is used to calculate the wind speed and frequency of the wind farm, and further fit the wind speed distribution function model; then obtain the cut-in wind speed and cut-out wind speed of the wind turbine according to the wind speed distribution function model, and use the power generation capacity curve function of the wind turbine , to calculate the annual generating capacity of the unit.
  • Fatigue load relationship function calculation module used to count the big data of the actual operating state of the wind speed of the unit at a certain cut-in wind speed and a certain cut-out wind speed, through the big data of the actual operating state, according to the time series, occurrence times and working conditions, through the fatigue load calculation method Calculate the cut-in/cut-out wind speed and the fatigue load function of the unit and main components.
  • the cut-in and cut-out wind speed calculation module is used to obtain the required power generation capacity of the unit according to the annual power generation capacity of the unit.
  • the cut-in wind speed and the cut-out wind speed are taken as independent variables, and the required power generation capacity of the unit and the fatigue impact load are taken as targets to obtain Cut-in wind speed and cut-out wind speed that meet the demand; or take the required power generation as the target, and the fatigue damage value is less than the design value of the constraint preference, transform the two objectives into a single objective function for the demanded power generation to seek an optimal solution.
  • the computer equipment described in the present invention includes a memory, a processor, and a computer program stored in the memory and operable on the processor. Steps to cut out the wind speed tuning method.
  • the computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned wind turbine cut-in/cut-out wind speed tuning method are realized.

Abstract

一种风电机组切入/切出风速调优方法、系统及设备介质，对风电机组运行大数据进行治理分析，建立风电机组发电能力功率曲线函数；得到风速分布函数模型；再根据风速分布函数模型和风电机组发电能力功率曲线函数，计算得出机组年度发电能力；统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数；以切入风速和切出风速为自变量，以需求的机组发电能力以及疲劳冲击载荷两者为目标，求取符合需求的切入/切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。

Description

一种风电机组切入/切出风速调优方法、系统及设备介质
本申请要求于2021年8月17日提交中国专利局、申请号为202110943739.6、发明名称为“一种风电机组切入/切出风速调优方法、系统及设备介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明属于风电机组设计及调试领域，涉及一种风电机组切入/切出风速调优方法、系统及设备介质。
背景技术
随着国内风电行业迅速发展，近几年来国内中东部区域建设的低风速风场数量也在日益增加。对这些低风速的风场资源而言，风速在切入风速左右频率往往占比较大，而切出风速也会因风电场环境条件受到影响。切入风速影响风电机组的启停，如切入风速设置过小，会导致风机频繁启停问题；如切入风速设置过大，又会导致风机在较大风速下仍不正常启动，直接影响机组发电能力。而切出风速大小会直接影响风电机组疲劳寿命和发电量，如切出风速设置过小，会损失机组发电量；如切入风速设置过大，尽管会带来发电量的收益，但又会导致机组疲劳寿命下降甚至是不能满足标准的设计要求。所以，选择合适的切入/切出风速的研究，已引起风电场开发企业以及风电机组设备制造厂家的重视。
发明内容
本发明的目的在于克服上述现有技术的缺点，提供一种风电机组切入/切出风速调优方法、系统及设备介质，能够获得风电机组较优切入/切出风速，使风电机组正常高效率的运行发电。
为达到上述目的，本发明采用以下技术方案予以实现：
一种风电机组切入/切出风速调优方法，包括以下过程：
S1，对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立风电机组发电能力功率曲线函数；
S2，统计出风电场风速频率，进一步拟合得风速分布函数模型；再根据风速分布函数模型得到风电机组的切入风速和切出风速，通过风电机组发电能力功率曲线函数，计算得出机组年度发电能力；
S3，统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数；
S4，根据机组年度发电能力得到需求的机组发电能力，以切入风速和切出风速为自变量，以需求的机组发电能力以及疲劳冲击载荷两者为目标，求取符合需求的切入风速值以及切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。
优选的，S1中，获取风电机组区域的风数据，将对应同时序运行数据异常数据点剔除后得到离散的功率数据点之后，采用曲线拟合方法建立风电机组发电能力功率曲线函数。
优选的，S1中，鉴别并剔除异常数据点的过程为：首先将运行数据中的时间戳作为时序特征、根据功率和风轮转速作为物理特征、根据切入风速、切出风速等作为风速判断特征。其次，采用无监督模型将限电、停机、限功率和变量阈值范围外的异常数据点进行过滤筛除。
优选的，S2中，风电场风速频率根据风电场现场的测风数据统计，若测风数据不足一定时间的水平，采用风资源计算方法补充修正风速分布函数模型。
优选的，疲劳载荷关系函数计算过程为：在当前切入风速和切出风速情况条件下，依据风频函数，计算风电机组整机及关键位置处在关键方向上等效疲劳载荷值，并且与该机组等效疲劳设计值比较，如若前者小于后者，则该切入风速为可行解；如若前者大约后者，则需要在有限元分析方法基础上再进行进一步校核得出其损伤值，如若损伤值小于1则风电机组整机及关键位置处安全，相反则不安全。
优选的，切出风速的一种方式为以切出风速满足一定标准时机组切出 时其功率直接降为零，另一种方式为机组是以降功率函数形式切出。
优选的，在以切入风速和切出风速为自变量，以发电量较大和疲劳损伤较小为目标函数中，设定基于特定风场条件为常量，在切入与切出风速范围内，机组设计条件不发生改变的约束条件下，求取需要的切入风速和切出风速。
一种风电机组切入/切出风速调优系统，包括：
风电机组发电能力功率曲线函数建立模块，用于对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立风电机组发电能力功率曲线函数；
机组年度发电能力计算模块，用于统计出风电场风速频率，进一步拟合得风速分布函数模型；再根据风速分布函数模型得到风电机组的切入风速和切出风速，通过风电机组发电能力功率曲线函数，计算得出机组年度发电能力；
疲劳载荷关系函数计算模块，用于统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数；
切入和切出风速计算模块，用于根据机组年度发电能力得到需求的机组发电能力，以切入风速和切出风速为自变量，以需求的机组发电能力以及疲劳冲击载荷两者为目标，求取符合需求的切入风速值以及切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。
一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述任意一项所述风电机组切入/切出风速调优方法的步骤。
一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上述任意一项所述风电机组切入/切出风速调优方法的步骤。
与现有技术相比，本发明具有以下有益效果：
本发明是基于风电机组运行大数据，一方面采用无监督算法完成该机组运行大数据治理和剔除，根据测风设备数据与运行数据功率拟合得出机组功率曲线；根据风电场现场的测风数据或/和风资源计算方法，再得出风速分布模型。就该机组功率曲线和风速分布模型计算得出该机组的年度发电能力函数关系式；另一方面，通过机组运行实际状态数据，采用疲劳载荷仿真计算方法得出机组及主要部件所受的疲劳损伤函数关系式。基于上述两个函数关系式，以切入风速和切出风速为变量，建立以发电量较大和疲劳载荷较小两目标函数，通过寻优方法确定较优的切入/切出风速。本发明所述方法至少有助于实现以下目标：一是减少和控制机组频繁启停以及切出阶段运行状态的疲劳载荷；二是有效增加风电机组尤其是安装于低风速地区的风电机组的发电能力；三是结合实际运行状态有效模拟仿真计算分析机组及主要部件载荷；四是分析验证风电机组对特定风电场的适应性。
附图说明
图1为本发明切入/切出风速调优方法的流程图。
具体实施方式
下面结合附图对本发明做进一步详细描述：
如图1所示，为本发明所述的风电机组切入/切出风速调优方法，包括以下过程：
步骤1：对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立高精度风电机组发电能力功率曲线函数。
对风电机组运行大数据进行治理分析，首先将运行数据中的时间戳作为时序特征、根据功率和风轮转速作为物理特征、根据切入风速、切出风速等作为风速判断特征。其次，采用无监督模型基于聚类算法(或孤立森林算法、向量机算法、高斯分布等算法)将限电、停机、限功率、变量阈值范围外等异常数据点进行过滤筛除。
步骤2：根据风电场现场的测风数据，如风速以及对应的持续时间，将统计出风电场风速频率，进一步拟合得风速分布函数模型。其次，再根据风电机组的切入和切出风速，计算得出机组年度发电能力。如测风数据 不足一定时间的水平，将采用风资源计算方法补充修正风速分布函数模型。
根据测风设备(激光雷达、测风塔或机舱上风速风向仪备等测风设备)测得的风数据，应用上述描述的筛除方法，将对应同时序运行数据异常数据点剔除后得到离散的功率数据点之后，采用合适的曲线拟合方法建立风速与机组发电能力功率之间的高精度关系曲线。
针对测风数据不足一定时间的水平，将基于风资源计算方法，如基于测量-关联-预测算法(MCP)，采用数值天气预报模式(WRF)以及计算流体动力学(CFD)中微尺度耦合算法，综合考虑已有测风数据、平均风速和能量密度，经补充修正得出更为可靠的风速分布函数模型。
步骤3：根据风电机组运行大数据，累计统计评估该机组风速在某切入风速以及某切出风速下实际运行状态大数据(如机组启停次数、振动、载荷等数据)，研究分析该机组机位处的环境条件；计算仿真该工况下的机组运行状态，模拟补充实际工况不足情况下机组运行状态表现。
步骤4：通过运行实际状态大数据(如启停次数、机组振动、载荷等数据)，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出机组及主要部件所受的疲劳损伤值。
风电机组疲劳载荷计算方法在基于风速、风向、来流角度、湍流强度、温度等环境条件下，在建立的风电机组动力学模型基础上，采用风电机组仿真分析软件或者程序计算在当前切入风速和切出风速情况条件下，依据风频函数，计算风电机组整机及关键位置处在关键方向上等效疲劳载荷值，并且与该机组等效疲劳设计值比较，如若前者小于后者，则该切入风速为可行解；如若前者大约后者，则需要在有限元分析方法基础上再进行进一步校核得出其损伤值，如若损伤值小于1则风电机组整机及关键位置处安全，相反则不安全。
步骤5：基于寻优方法，以切入风速和切出风速为自变量，以较大的机组发电能力以及较小疲劳冲击载荷两者为目标，求取较优的切入风速值以及切出风速。切出风速可以是数值也可以是函数形式。
寻优方法将采用全局的寻优方法来求解，可采用遗传算法、模拟退火算法、粒子群或神经网络等智能算法来求解。以切入风速和切出风速为自 变量，建立以发电量较大和疲劳损伤较小的两个目标函数进行寻优求解；或者，以对发电量较大为目标、疲劳损伤值小于设计值的约束偏好，则将两目标转化为求发电量较大的单目标函数来寻优求解。
切出风速一般是以切出风速满足一定标准时机组切出时其功率直接降为零，另一种方式为机组是以降功率函数形式切出，函数形式可为斜率为负线性函数，曲线函数、阶梯递减函数等函数形式。
在以切入风速和切出风速为自变量，以发电量较大和疲劳损伤较小为目标函数中，设定基于特定风场条件如极限风速值、湍流强度、来流角度、剪切指数等为常量，在切入与切出风速范围内，机组设计条件如机组本身模型不发生改变(即额定发电功率、轮毂高度、传动链、叶片等不变)诸多约束条件下，来求取较优的切入风速和切出风速。
本发明所述风电机组切入/切出风速调优方法的具体过程为：
步骤1：对风电机组运行大数据进行治理分析，同时采集风电机组轮毂高度风速、风向、温度、气压等气象量以及机组本身有功功率，运行状态(并网、故障)数据，鉴别并剔除异常数据点，采用多次曲线函数拟合风电机组功率曲线。该风电机组发电功率与风速之间的关系式为
P i＝f 1(v i)
其中，v i代表风速，单位m/s；P i代表对应风速v i下的风电机组发电功率，kW。
步骤2：根据风电场现场的测风数据以及风资源计算补充修正方法，得出风电场风速分布函数模型关系式
A i＝f 2(v i)
其中，v i代表风速，单位m/s；A i代表对应风速v i下的频率；f(...)代表函数，可为Weibull分布模型、Rayleigh分布模型及对应正态分布模型等函数。
一般情况下，在某切入风速v cut-in以及某切出风速v cut-out、风速频率A i以及对应的风电机组发电功率P i、折减系数θ，得出该风电机组年度发 电能力W关系函数为：
Figure PCTCN2022112933-appb-000001
步骤3：根据风电机组运行大数据，累计统计该风电机组风速在某切入风速以及某切出风速附近时其实际运行状态(如机组启停次数、振动、载荷等运行数据)，且评估该风电机组机位处的环境条件以及计算仿真该工况下的机组运行状态。
步骤4：在某切入风速v cut-in和切出风速v cut-out情况下，通过步骤3获取的运行状态大数据(如启停次数、机组振动、载荷等运行数据)，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出该切入风速v cut-in、切出风速v cut-out情况下机组及相关部件所受的疲劳载荷D关系函数：
Figure PCTCN2022112933-appb-000002
步骤5：根据机组设计及运行特性，切入风速可设定为3m/s(对于单机容量较小、风轮直径较大的机型其值下限可设置更低些)到5m/s范围内，切出风速一般设定为20m/s(对于较高风速地区的风电机组其值下限可设置更低些)到25m/s范围内(对于海上风电机组和低风速地区的机组其值可以适当的上调更高些)。切出风速也可以不是定值，切出风速可以表示为函数形式。基于寻优方法，以切入风速v cut-in和切出风速v cut-out为自变量，自变量取值范围大致在前面所述约束条件下，以机组发电能力较大和疲劳载荷较小两者为目标，求取较优的切入风速值和切出风速值。
MaxW＝f 3(v cut-in，v cut-out)
MinD＝f 4(v cut-in，v cut-out)
本发明所述的风电机组切入/切出风速调优系统，包括：
风电机组发电能力功率曲线函数建立模块，用于对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立风电机组发电能力功率曲线函数。
机组年度发电能力计算模块，用于统计出风电场风速频率，进一步拟合得风速分布函数模型；再根据风速分布函数模型得到风电机组的切入风速和切出风速，通过风电机组发电能力功率曲线函数，计算得出机组年度发电能力。
疲劳载荷关系函数计算模块，用于统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数。
切入和切出风速计算模块，用于根据机组年度发电能力得到需求的机组发电能力，以切入风速和切出风速为自变量，以需求的机组发电能力以及疲劳冲击载荷两者为目标，求取符合需求的切入风速值以及切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。
本发明所述的计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述风电机组切入/切出风速调优方法的步骤。
本发明所述的计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上述风电机组切入/切出风速调优方法的步骤。
以上内容仅为说明本发明的技术思想，不能以此限定本发明的保护范围，凡是按照本发明提出的技术思想，在技术方案基础上所做的任何改动，均落入本发明权利要求书的保护范围之内。

Claims (10)

  1. 一种风电机组切入/切出风速调优方法，其特征在于，包括以下过程：
    S1，对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立风电机组发电能力功率曲线函数；
    S2，统计出风电场风速频率，进一步拟合得风速分布函数模型；再根据风速分布函数模型得到风电机组的切入风速和切出风速，通过风电机组发电能力功率曲线函数，计算得出机组年度发电能力；
    S3，统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数；
    S4，根据机组年度发电能力得到需求的机组发电能力，以切入风速和切出风速为自变量，以需求的机组发电能力以及疲劳冲击载荷两者为目标，求取符合需求的切入风速值以及切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。
  2. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，S1中，获取风电机组区域的风数据，将对应同时序运行数据异常数据点剔除后得到离散的功率数据点之后，采用曲线拟合方法建立风电机组发电能力功率曲线函数。
  3. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，S1中，鉴别并剔除异常数据点的过程为：首先将运行数据中的时间戳作为时序特征、根据功率和风轮转速作为物理特征、根据切入风速、切出风速等作为风速判断特征。其次，采用无监督模型将限电、停机、限功率和变量阈值范围外的异常数据点进行过滤筛除。
  4. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，S2中，风电场风速频率根据风电场现场的测风数据统计，若测风数 据不足一定时间的水平，采用风资源计算方法补充修正风速分布函数模型。
  5. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，疲劳载荷关系函数计算过程为：在当前切入风速和切出风速情况条件下，依据风频函数，计算风电机组整机及关键位置处在关键方向上等效疲劳载荷值，并且与该机组等效疲劳设计值比较，如若前者小于后者，则该切入风速为可行解；如若前者大约后者，则需要在有限元分析方法基础上再进行进一步校核得出其损伤值，如若损伤值小于1则风电机组整机及关键位置处安全，相反则不安全。
  6. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，切出风速的一种方式为以切出风速满足一定标准时机组切出时其功率直接降为零，另一种方式为机组是以降功率函数形式切出。
  7. 根据权利要求1所述的风电机组切入/切出风速调优方法，其特征在于，在以切入风速和切出风速为自变量，以发电量较大和疲劳损伤较小为目标函数中，设定基于特定风场条件为常量，在切入与切出风速范围内，机组设计条件不发生改变的约束条件下，求取需要的切入风速和切出风速。
  8. 一种风电机组切入/切出风速调优系统，其特征在于，包括：
    风电机组发电能力功率曲线函数建立模块，用于对风电机组运行大数据进行治理分析，鉴别并剔除异常数据点，采用多次曲线函数实现对风电机组功率曲线充分光滑拟合，从而建立风电机组发电能力功率曲线函数；
    机组年度发电能力计算模块，用于统计出风电场风速频率，进一步拟合得风速分布函数模型；再根据风速分布函数模型得到风电机组的切入风速和切出风速，通过风电机组发电能力功率曲线函数，计算得出机组年度发电能力；
    疲劳载荷关系函数计算模块，用于统计机组风速在某切入风速以及某切出风速下实际运行状态大数据，通过运行实际状态大数据，根据时间序列、发生次数及工况，通过疲劳载荷计算方法计算得出切入/切出风速与机组及主要部件所受的疲劳载荷关系函数；
    切入和切出风速计算模块，用于根据机组年度发电能力得到需求的机组发电能力，以切入风速和切出风速为自变量，以需求的机组发电能力以 及疲劳冲击载荷两者为目标，求取符合需求的切入风速值以及切出风速；或者以需求的发电量为目标，疲劳损伤值小于设计值的约束偏好，将两目标转化为求需求发电量的单目标函数来寻优求解。
  9. 一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至7任意一项所述风电机组切入/切出风速调优方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至7任意一项所述风电机组切入/切出风速调优方法的步骤。
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