Open AccessIdentifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data
Author(s) -
GallegoCastillo Cristobal,
GarciaBustamante Elena,
Cuerva Alvaro,
Navarro Jorge
Publication year - 2015
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2014.0457
Subject(s) - wind power , multivariate statistics , identification (biology) , computer science , environmental science , wind power forecasting , meteorology , data mining , power (physics) , electric power system , engineering , machine learning , geography , electrical engineering , botany , physics , quantum mechanics , biology
Forecasting abrupt variations in wind power generation (the so‐called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind‐to‐power conversion chain.