An Interference Optimization – Induced Electrical Turbine Fault Prediction and Analysis Method

Abstract

Predicting electrical turbine faults is decisive for consistent operation and power generation output. Based on the operative cycles of the electrical turbine, the faults are predicted to prevent power generation interruptions. This paper introduces an Interference Optimization-based Fault Prediction Method (IO-FPM) for serving smooth operation purposes. In this method, the inferred optimization using classifier tree learning is induced for segregating the operating cycles of the turbine. The maximum and minimum threshold conditions for turbine operation using resistance and magnitude of the blades are accounted for each operation cycle. The classifier performs segregation based on low and high thresholds for predicting failure cycles. Such cycles are altered using pre-maintenance intervals and mechanical fault diagnosis at an early stage. This prevents turbine failure regardless of external influencing factors.