A Support Vector Machine-based Fault Diagnosis and Prognosis Model for Wind Turbines
Current matrix for fault diagnosis and prognosis models of wind turbines is generally unit based,and the scope of warning is limited,resulting in an extension of warning response time.Therefore this paper proposes design and valida-tion analysis of a support vector machine-based wind turbine fault diagnosis and prognosis model.Based on current meas-urement requirements and standards,fault diagnosis and warning feature quantities are extracted.By adopting a multi-ob-jective approach and breaking the limitations of warning range,a multi-objective diagnosis and warning matrix are de-signed.And based on this,and SVM fault diagnosis and prognosis structure is constructed and order analysis is used to a-chieve fault diagnosis and prognosis processing.The test results show that compared with conventional machine learning model,conventional MSK-CNN,and multi-source electromechanical information fusion,the designed model has a better warning response time controlled below 0.25 second,indicating that the assistance of support vector machine facilitate high efficiency of fault diagnosis and prognosis,with stronger specificity and practical application value.
support vector machinewind generationgenerator setfault diagnosisprognosis modeldiagnostic identi-fication
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