Mahima Susan Abraham and Jiby J Puthiyidam
College of Engineering, Poonjar, India

Wind energy integration research generally relieson complex sensors located at remote sites. The procedureforgenerating high-level synthetic information from databasescontaining large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. The datainput is highly sensitive to data quality. To address this problem,this paper presents an empirical methodology that can efficientlypreprocess and filter the raw wind data using only aggregatedactive power output and the corresponding wind speed valuesat the wind farm. First, raw wind data properties are analyzed, and all the data are divided into six categories according to theirattribute magnitudes from a statistical perspective. Next, theweighted distance, a novel concept of the degree of similaritybetween the individual objects in the wind database and the localoutlier factor (LOF) algorithm is incorporated to compute theoutlier factor of every individual object, and this outlier factor isthen used to assess which category an object belongs to.

Data mining, data preprocessing, local outlierfactor (LOF), unsupervised learning