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    METHOD FOR WINDMILL FARM MONITORING
    2.
    发明申请
    METHOD FOR WINDMILL FARM MONITORING 审中-公开
    WINDMILL农场监测方法

    公开(公告)号：WO2016192786A1

    公开(公告)日：2016-12-08

    申请号：PCT/EP2015/062389

    申请日：2015-06-03

    Applicant: ABB SCHWEIZ AG

    Inventor： CHIOUA, Moncef ,  CHEN, Ni Ya ,  YU, RongRong ,  ZHOU, Yingya ,  CHEN, Yao

    IPC: F03D17/00

    CPC classification number: F03D17/00 ,  F05B2240/96 ,  G06F11/30 ,  Y02B10/30

    Abstract: The invention is related to a method for monitoring turbines (122, 124) of a windmill farm (120), comprising the following steps: providing a global nominal dataset containing frame data of the turbines (122, 124) of the windmill farm (120) and continuous reference monitoring data of the turbines (122, 124) for a first period in the fault free state, wherein the reference monitoring data comprise at least two same monitoring variables for each turbine (122, 124); building a nominal global model based on the global nominal dataset which describes the relationship inbetween the wind-mill turbines (122, 124) and clustering (22, 24, 26, 126, 128, 130) the turbines (122, 24) according thereto; assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering (22, 24, 26, 126, 128, 130); building a nominal local model for the turbines of each cluster (22, 24, 26, 126, 128, 130) based on the respective assigned nominal local datasets, wherein the nominal local model is built in that way, that a nonconformity index (NC) is provideable which is indicating the degree of nonconformity between data projected on the local model and the model itself; providing a test dataset with continuous test monitoring data of the turbines of the windmill farm for a further period, wherein those continuous test monitoring data are structured in the same way than the continuous reference monitoring data in the nominal global dataset and wherein the clustering (22, 24, 26, 126, 28, 130) of the nominal global dataset is also applied on the test dataset; cluster wise (22, 24, 26, 126, 128, 130) projection of continuous test monitoring data of the test dataset on the respective assigned nominal local models of the turbines and deriving a nonconformity index (NC) for each respective turbine therefrom; indicating a turbine (122, 124) as critical in case that the respective related nonconformity index exceeds a given limit.

    Abstract translation: 本发明涉及一种用于监测风车场（120）的涡轮机（122,124）的方法，包括以下步骤：提供包含风车场（120,122）的涡轮机（122,124）的帧数据的全局标称数据集 ）和在无故障状态下的第一周期的涡轮机（122,124）的连续参考监测数据，其中所述参考监测数据包括用于每个涡轮机（122,124）的至少两个相同的监测变量; 基于全球标称数据集建立名义全局模型，其描述风轮机（122,124）和聚集（22,24,26,126,128,130）之间的风力涡轮机（122,24）的关系。 ; 根据聚类（22,24,26,126,128,130）将全局标称数据集的数据分配给相应的标称本地数据集; 基于相应分配的标称本地数据集，为每个集群（22,24,26,126,128,130）的涡轮机建立标称本地模型，其中标称本地模型以这种方式构建，不符合指数（NC ）是可以提供的，这表明在本地模型上预测的数据与模型本身之间的不符合程度; 提供具有风车场的涡轮机的连续测试监测数据的测试数据集，其中这些连续测试监视数据以与标称全局数据集中的连续参考监视数据相同的方式构造，并且其中聚类（22 ，24,26,126,28,130）也被应用于测试数据集; 对各个分配的涡轮机的标称局部模型的测试数据集的连续测试监视数据进行集群（22,24,26,126,112,130）的投影，并为每个相应的涡轮机导出不合格指数（NC）; 指示涡轮机（122,124）在相应的相关不合格指数超过给定极限的情况下是至关重要的。

    METHOD FOR WINDMILL FARM MONITORING
    4.
    发明公开
    METHOD FOR WINDMILL FARM MONITORING 审中-公开
    WINDMILL农场监测方法

    公开(公告)号：EP3303835A1

    公开(公告)日：2018-04-11

    申请号：EP15726178.5

    申请日：2015-06-03

    Applicant: ABB Schweiz AG

    Inventor： CHIOUA, Moncef ,  CHEN, Ni Ya ,  YU, RongRong ,  ZHOU, Yingya ,  CHEN, Yao

    IPC: F03D17/00

    CPC classification number: F03D17/00 ,  F05B2240/96 ,  G06F11/30 ,  Y02B10/30

    Abstract: A method for monitoring turbines of a windmill farm includes: providing a global nominal dataset containing frame data of the turbines of the windmill farm and continuous reference monitoring data of the turbines for a first period in a fault free state, the reference monitoring data including at least two same monitoring variables for each turbine; building a nominal global model based on the global nominal dataset which describes the relationship in between the windmill turbines and clustering the turbines according thereto; assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering; and building a nominal local model for the turbines of each cluster based on the respective assigned nominal local datasets, the nominal local model being built such that a nonconformity index is provideable which indicates a degree of nonconformity between data projected on the local model and the model itself.

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