Data-driven fault detection and isolation scheme for a wind turbine benchmark

Renewable Energy

Volumn 87, Issue , 2016, Pages 634-645

  de Bessa, Iury Valente ^a   Palhares, Reinaldo Martinez ^b   D'Angelo, Marcos Flávio Silveira Vasconcelos ^c   Chaves Filho, João Edgar ^d  

^a Federal Institute of Amazonas (IFAM)   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^c STATE UNIVERSITY OF MONTES CLAROS   (Brazil)

^d FEDERAL UNIVERSITY OF AMAZONAS   (Brazil)

Author keywords

Fault detection and isolation (FDI); Fuzzy Bayesian networks; Gibbs sampling; Wind turbines

Indexed keywords

BAYESIAN NETWORKS; BENCHMARKING; FUZZY LOGIC; INFERENCE ENGINES; TIME SERIES; TIME SERIES ANALYSIS; WIND TURBINES;

BAYESIAN INFERENCE; CHANGE POINT DETECTION; DETECTION AND DIAGNOSTICS; DYNAMICAL MODEL; FAULT DETECTION AND ISOLATION; FAULT DETECTION AND ISOLATION SCHEMES; GIBBS SAMPLING; RENEWABLE ENERGIES;

FAULT DETECTION;

ALGORITHM; BAYESIAN ANALYSIS; BENCHMARKING; DATA INTERPRETATION; ENERGY RESOURCE; FUZZY MATHEMATICS; RENEWABLE RESOURCE; SENSOR; WIND TURBINE;

EID: 84946594647     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2015.10.061     Document Type: Article

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