CN106704103A - 一种基于叶片参数自学习的风电机组功率曲线获取方法 - Google Patents

一种基于叶片参数自学习的风电机组功率曲线获取方法 Download PDF

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CN106704103A
CN106704103A CN201710006787.6A CN201710006787A CN106704103A CN 106704103 A CN106704103 A CN 106704103A CN 201710006787 A CN201710006787 A CN 201710006787A CN 106704103 A CN106704103 A CN 106704103A
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邓英
田德
庞辉庆
杨志伟
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North China Electric Power University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • 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 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
    • 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 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • 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 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract

本发明公布了一种基于叶片参数自学习的风电机组功率曲线获取方法。该方法是利用实际风电机组叶片输出特性的响应参数、场址风湍流和空气密度，建立了3维功率单元模型‑1(如图1)，风电机组叶片参数决定机组捕获功率的能力，当机组的型号确定后，其基本的功率曲线就确定了，方法采用现场自学习叶片参数和风场的风湍流模型，当测得现场的密度参数后，根据IEC61400‑12标准规定的方法，设计的功率曲线计算程序，获取现场风电机组的功率曲线。

Description

一种基于叶片参数自学习的风电机组功率曲线获取方法
技术领域：
本发明涉及到清洁能源风能管控领域，主要涉及风力发电机功率曲线获取方式。
发明背景：
大型风电机组的发电功率特性受到机组的型号、安装厂址风况、密度等物理参数的影响，使得风电机组输出的功率曲线偏离机组测定标准功率曲线很多，造成业主运维管理得不到参照的标准功率曲线，无法确定标准的发电量考核数据；特别是机组出现了不明原因或管理不当的发电量下降，从监控管理上找不到发电量下降的原因。因此，我们发明了一种切实可行之有效的功率曲线获取方法。
发明内容：
利用实际风电机组叶片输出特性的响应参数、场址风湍流和空气密度，建立了3维功率单元模型-1(如图1所示)，表示为Pi＝(Θ，Ξ，ρ)，其中：Θ-叶片参数，它决定风电机组捕获功率的能力，当机组的型号确定后，其基本的功率曲线就确定了；Ξ-风速湍流，它由单机安装场地的风况条件决定；ρ-空气密度，它由风电场地理位置和环境条件决定。功率单元模型—1由三个部分组成如图1所示，他们是叶片参数自学习模型-2、风湍流模型—3和空气密度模型—4；单机标准功率曲线-5由功率单元模型Pi＝(Θ，Ξ，ρ)及现场自学习得到的数据、功率数据和风速处理方法，进行计算确定功率曲线上的散点，使散点连线组成。
1、叶片参数(Cp-λ等)自学习模型-2计算功率的方法，它是基于现场机组运行的风轮输出的数据，采用时间推移模型自学习的方法，推断拟合得到叶片参数模型。其中Cp是风能转换效率，λ是风轮转速与风速的比值也叫尖速比。
2、自学习风湍流模型-3计算功率偏差的方法(如图2)，它是根据现场风电机组运行数据和控制算法自学习获取。根据现场测风数据的bin区间里，采用偏差-修正正态随机分布方法，计算确定现场的各风速bin区间的实际湍流强度，在上述1得到的单元功率模型的基础上，确定实际风速的湍流强度，自学习得到风湍流模型-3。
3、空气密度模型-4的功率修正方法(如图3所示)，密度通常与气压、温度、湿度有密切关系，它与功率的关系是经过现场获取数据计算获取。现场的空气密度由下图公式计算得出，在上述2得到的功率曲线基础上根据现场实际空气密度对功率曲线进行修正，如图3所示。
4、根据上述1、2、3，获得加权后的bin风速下的功率值，在功率-风速坐标系中进行数据点连线，得到标定的功率曲线，实现方法如图4所示。
附图说明：
图1：风电机组功率曲线标定模型。
图2：叶片参数自学习模型-2和湍流自学习模型实现方法。
图3：空气密度模型-4的功率修正方法。
图4：现场功率曲线获取方法。
图5：不同厂址的湍流强度所对应的功率曲线及标定功率曲线。
图6：根据叶片参数自学习方法得到的功率曲线。
图7：根据现场空气密度修正得到的功率曲线。
具体实施案例：
根据如图5所示的不同厂址的湍流强度所对应的功率曲线，采用时间推移模型自学习的方法，推断拟合得到叶片参数模型(结果如图6所示)，在此基础上根据图2方法确定实际风速的湍流强度，自学习得到风湍流模型，进而根据图3方法得到功率修正曲线(结果如图7所示)，最后根据上述结果，获得加权后的bin风速下的功率值，在功率-风速坐标系中进行数据点连线，得到标定的功率曲线(结果如图5所示)。

Claims (3)

1.叶片参数(Cp-λ等)自学习模型-2计算功率的方法，它是基于现场机组运行的风轮输出的数据，采用时间推移模型自学习的方法，推断拟合得到叶片参数模型；其中Cp是风能转换效率，λ是风轮转速与风速的比值也叫尖速比。
2.自学习风湍流模型-3计算功率偏差的方法(如图2)，它是根据现场风电机组运行数据和控制算法自学习获取。根据现场测风数据的bin区间里，采用偏差-修正正态随机分布方法，计算确定现场的各风速bin区间的实际湍流强度，在权利要求1得到的单元功率模型的基础上，确定实际风速的湍流强度，根据图2获取自学习风湍流模型-3。
3.空气密度模型-4的功率修正方法，密度通常与气压、温度、湿度有密切关系，它与功率的关系是经过现场数据鸦片和湍流自学习获取的数据修正得到；现场的空气密度由下图公式计算得出，在权利要求2得到的功率曲线基础上根据现场实际空气密度对功率曲线进行修正，如图3所示。
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CN108509705A (zh) * 2018-03-26 2018-09-07 北京鉴衡认证中心有限公司 计算特定场址风力发电机组功率曲线的方法及装置
CN108760773A (zh) * 2018-08-21 2018-11-06 上海东易电气有限公司 在线碳氢化合物含水率及纯油密度分析撬座系统
CN109751195A (zh) * 2017-11-01 2019-05-14 中国船舶重工集团海装风电股份有限公司 一种风力发电机功率曲线的获取方法及装置
CN111601969A (zh) * 2018-01-15 2020-08-28 乌本产权有限公司 风能设备和用于控制风能设备的方法
CN111911352A (zh) * 2020-05-11 2020-11-10 宁波大学 一种气流发生风力发电方法
EP3862561B1 (en) 2020-02-07 2023-04-19 General Electric Company Systems and methods for continuous machine learning based control of wind turbines
US11761425B2 (en) * 2020-02-03 2023-09-19 General Electric Renovables Espana, S.L. Turbulence intensity estimation

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CN108509705A (zh) * 2018-03-26 2018-09-07 北京鉴衡认证中心有限公司 计算特定场址风力发电机组功率曲线的方法及装置
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CN108760773A (zh) * 2018-08-21 2018-11-06 上海东易电气有限公司 在线碳氢化合物含水率及纯油密度分析撬座系统
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US11761425B2 (en) * 2020-02-03 2023-09-19 General Electric Renovables Espana, S.L. Turbulence intensity estimation
EP3862561B1 (en) 2020-02-07 2023-04-19 General Electric Company Systems and methods for continuous machine learning based control of wind turbines
CN111911352A (zh) * 2020-05-11 2020-11-10 宁波大学 一种气流发生风力发电方法
CN111911352B (zh) * 2020-05-11 2023-02-28 宁波大学 一种气流发生风力发电方法

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