Using data-mining approaches for wind turbine power curve monitoring: A comparative study

IEEE Transactions on Sustainable Energy

Volumn 4, Issue 3, 2013, Pages 671-679

  Schlechtingen, Meik ^a   Santos, Ilmar Ferreira ^b   Achiche, Sofiane ^c  

^a EnBW Erneuerbare Energien GmbH   (Germany)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^c ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

Condition monitoring; data mining; fuzzy neural networks; machine learning; neural networks; power generation; power system faults; signal analysis; wind energy

Indexed keywords

COMPARATIVE STUDIES; K-NEAREST NEIGHBORS; MEAN ABSOLUTE ERROR; MEAN ABSOLUTE PERCENTAGE ERROR; PITCH-REGULATED TURBINES; POWER SYSTEM FAULT; ROOT MEAN SQUARED ERRORS; STANDARD DEVIATION;

AIR POLLUTION CONTROL; CONDITION MONITORING; FUZZY LOGIC; FUZZY NEURAL NETWORKS; LEARNING ALGORITHMS; LEARNING SYSTEMS; LOSSES; NEURAL NETWORKS; POWER GENERATION; SIGNAL ANALYSIS; TEMPERATURE; THERMOELECTRIC POWER; WIND POWER; WIND TURBINES;

DATA MINING;

EID: 84880273710     PISSN: 19493029     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSTE.2013.2241797     Document Type: Article

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