CN106704103B - 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
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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
技术领域
本发明涉及到清洁能源风能管控领域,特别涉及一种基于叶片参数自学习的风电机组功率曲线获取方法。
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
本发明的主要目的在于提供一种基于叶片参数自学习的风电机组功率曲线获取方法,可以有效解决背景技术中的问题。
为实现上述目的,本发明采取的技术方案为:
一种基于叶片参数自学习的风电机组功率曲线获取方法,其特征在于,该方法包括以下步骤:
S1:获取风电机组运行数据;
S2:建立风电机组单元功率模型Pi=(Θ,Ξ,ρ);其中:Θ-叶片参数,叶片参数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,风速湍流由单机安装场地的风况条件决定;ρ-空气密度,空气密度由风电场地理位置和环境条件决定;风电机组单元功率模型由叶片参数自学习模型、自学习风湍流模型和空气密度模型组成;
所述叶片参数自学习模型是基于现场机组运行的风轮输出的数据,采用时间序列模型自学习的方法,推断拟合得到的;叶片参数包括Cp和λ,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;
所述自学习风湍流模型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,在所述叶片参数自学习模型的基础上,确定实际风速的湍流强度,从而获取自学习风湍流模型;
所述空气密度模型由空气密度与气压、温度、湿度的关系获得;
S3:校核风电机组发电模拟数据;
S4:对比标准功率曲线数据库,得到功率曲线数据;
S5:所述功率曲线数据的修正计算:获取现场的气压、温度和湿度数据,根据所述空气密度模型得到现场的空气密度;获取机组的测风数据,根据所述自学习风湍流模型得到风湍流强度;利用所述现场的空气密度和所述风湍流强度修正所述功率曲线数据,得到修正后的功率曲线数据点;
S6:根据所述修正后的功率曲线数据点在功率-风速坐标系中进行数据点连线,得到标定的功率曲线。
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维单元功率模型-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。单元功率模型—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;单机标准功率曲线由单元功率模型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为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法的风电机组功率曲线标定模型。
图2为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中叶片参数自学习模型和湍流自学习模型实现方法示意图。
图3为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中空气密度模型的功率修正方法示意图。
图4为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中现场功率曲线获取方法示意图。
图5为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中不同厂址的湍流强度所对应的功率曲线及标定功率曲线示意图。
图6为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中根据叶片参数自学习方法得到的功率曲线图。
图7为本发明一种基于叶片参数自学习的风电机组功率曲线获取方法中根据现场空气密度修正得到的功率曲线图。
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)。
以上显示和描述了本发明的基本原理和主要特征和本发明的优点。本行业的技术人员应该了解,本发明不受上述实施例的限制,上述实施例和说明书中描述的只是说明本发明的原理,在不脱离本发明精神和范围的前提下,本发明还会有各种变化和改进,这些变化和改进都落入要求保护的本发明范围内。本发明要求保护范围由所附的权利要求书及其等效物界定。
Claims (1)
1.一种基于叶片参数自学习的风电机组功率曲线获取方法,其特征在于,该方法包括以下步骤:
S1:获取风电机组运行数据;
S2:建立风电机组单元功率模型Pi=(Θ,Ξ,ρ);其中:Θ-叶片参数,叶片参数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,风速湍流由单机安装场地的风况条件决定;ρ-空气密度,空气密度由风电场地理位置和环境条件决定;风电机组单元功率模型由叶片参数自学习模型、自学习风湍流模型和空气密度模型组成;
所述叶片参数自学习模型是基于现场机组运行的风轮输出的数据,采用时间序列模型自学习的方法,推断拟合得到的;叶片参数包括Cp和λ,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;
所述自学习风湍流模型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,在所述叶片参数自学习模型的基础上,确定实际风速的湍流强度,从而获取自学习风湍流模型;
所述空气密度模型由空气密度与气压、温度、湿度的关系获得;
S3:校核风电机组发电模拟数据;
S4:对比标准功率曲线数据库,得到功率曲线数据;
S5:所述功率曲线数据的修正计算:获取现场的气压、温度和湿度数据,根据所述空气密度模型得到现场的空气密度;获取机组的测风数据,根据所述自学习风湍流模型得到风湍流强度;利用所述现场的空气密度和所述风湍流强度修正所述功率曲线数据,得到修正后的功率曲线数据点;
S6:根据所述修正后的功率曲线数据点在功率-风速坐标系中进行数据点连线,得到标定的功率曲线。
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CN101858311B (zh) * | 2010-05-10 | 2012-04-18 | 三一电气有限责任公司 | 获得风电设备功率曲线以及风电设备控制的方法和装置 |
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CN104747367B (zh) * | 2013-12-31 | 2017-08-11 | 华能新能源股份有限公司 | 风力发电机组功率曲线特性检测系统 |
CN103927695B (zh) * | 2014-04-22 | 2017-11-24 | 国家电网公司 | 基于自学习复合数据源的风电功率超短期预测方法 |
CN105275742B (zh) * | 2015-11-09 | 2018-03-16 | 国家电网公司 | 一种风电机组自适应环境的控制方法 |
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