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Data cleaning method and device and server
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A data cleaning and data technology, applied in electrical digital data processing, special data processing applications, digital data information retrieval, etc. Effectiveness and the effect of data volume
Active Publication Date: 2019-05-21
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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At present, the identification of wind power anomaly data is mostly carried out for accumulated anomaly data, but there are few recognition algorithms for sparse anomaly data, which makes it difficult to process sparse anomaly data
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Embodiment 1
[0030] An embodiment of the present invention provides a data cleaning method, which can be applied to a computing server to perform data cleaning on sparse data of a wind farm. Such as figure 1 Shown is a flow chart of a data cleaning method, the method comprising the following steps:
[0031] Step S102, obtaining measured data samples, wherein the measured data samples are measured operating data of wind speed and power of wind turbines in the wind farm;
[0032]Considering that the data cleaning method provided by the embodiment of the present invention is mainly for data cleaning of sparse data, the measured data samples obtained above are usually wind speed and power data without significant accumulation abnormal data.
[0033] Step S104, establishing a joint probability distribution of wind speed and power in Copula space;
[0034] Wherein, the value range of the above-mentioned joint probability distribution is [0, 1] interval;
[0058] On the basis of the above embodiments, the embodiment of the present invention also provides a data cleaning device, such as image 3 A schematic diagram of the structure of a data cleaning device shown, the device includes:
[0059] An acquisition module 30, configured to acquire measured data samples, wherein the measured data samples are measured operating data of wind speed and power of wind turbines in the wind farm;
[0060] Build module 32, be used for establishing the joint probability distribution of wind speed and power in Copula space, wherein, the value range of joint probability distribution is [0,1] interval;
[0061]A division module 34, configured to evenly divide the value range of the joint probability distribution into a plurality of probability intervals;
[0062] The cleaning module 36 is configured to perform data cleaning on the measured data samples in each probability interval along the dimensions of wind speed and power respectively.
[0063]...
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Abstract
The invention provides a data cleaning method and device and a server, and relates to the technical field of wind powerplantdata processing, and the method comprises the steps: obtaining an actual measurement data sample; establishing joint probability distribution of wind speed and power in the Copula space; uniformly dividing the value range of the joint probability distribution into a plurality of probability intervals; and performing data cleaning on the actually measured data sample in each probability interval along the dimensions of the wind speed and the power. According to the datacleaning method and device and the server provided by the invention, the actually measured sample data can be cleaned based on the probability space to obtain a finer data cleaning result, meanwhile,the effectiveness and the data size of the remaining data can be ensured, and a solid foundation is laid for subsequent data analysis.
Description
technical field [0001] The invention relates to the technical field of wind farm data processing, in particular to a data cleaning method, device and server. Background technique [0002] For the operation data of the SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control system) system of the wind farm, abnormal data widely exists. From the perspective of the formation reasons and scale of abnormal data, it is mainly divided into accumulation abnormal data and sparse abnormal data. The former is usually caused by long-term abnormal operation, such as long-term power operation, sudden or slow-changing faults, and performance degradation. The latter is usually caused by environmental interference, measurement noise and operation control, such as transmission signal interference, fan mechanical vibration, fan controller switching, etc. In addition, the distortion of SCADAsystemdata records will also form some abnormal data. Therefore, fro...
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