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Raw Wind Data Preprocessing: A Data-Mining Approach
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DOI：10.1109/TSTE.2014.2355837.png)
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Abstract
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Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. The data input is highly sensitive to data quality. To address this problem, this paper presents an empirical methodology that can efficiently preprocess and filter the raw wind data using only aggregated active power output and the corresponding wind speed values at the wind farm. First, raw wind data properties are analyzed, and all the data are divided into six categories according to their attribute magnitudes from a statistical perspective. Next, the weighted distance, a novel concept of the degree of similarity between the individual objects in the wind database and the local outlier factor (LOF) algorithm, is incorporated to compute the outlier factor of every individual object, and this outlier factor is then used to assess which category an object belongs to. Finally, the methodology was tested successfully on the data collected from a large wind farm in northwest China.
Keywords:
Data mining
data preprocessing
local outlier factor (LOF)
unsupervised learning
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