Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

Applied Soft Computing Journal

Volumn 13, Issue 1, 2013, Pages 259-270

  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

ANFIS models; Condition monitoring; Normal behavior models; SCADA data; Wind turbine

Indexed keywords

ABNORMAL BEHAVIOR; ANFIS MODEL; ANOMALY DETECTION; APPLICATION EXAMPLES; COMPUTATIONAL TIME; CONDITION MONITORING SYSTEMS; EFFICIENCY EVALUATION; EXPERT KNOWLEDGE; FAULT PATTERNS; FUZZY INTERFERENCE SYSTEMS; MODEL TRAINING; NEURAL NETWORK MODEL; NEURO-FUZZY; NORMAL BEHAVIOR; PREDICTION ERRORS; ROOT CAUSE; SCADA DATA; STANDARD DEVIATION; SUPERVISORY CONTROL AND DATA ACQUISITION; SYSTEM DESCRIPTION; TRAINING SPEED;

CONDITION MONITORING; ERRORS; FACE RECOGNITION; FORECASTING;

WIND TURBINES;

EID: 84869380569     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2012.08.033     Document Type: Article

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