Adaptive Estimation-Based Leakage Detection for a Wind Turbine Hydraulic Pitching System

Abstract

Operation and maintenance (OM) cost has contributed a major share in the cost of energy for wind power generation. Condition monitoring can help reduce the OM cost of wind turbine. Among the wind turbine components, the fault diagnosis of the hydraulic pitching system is investigated in this study. The hydraulic pitching system is critical for energy capture, load reduction, and aerodynamic braking. The fault detection of internal and external leakages in the hydraulic pitching system is studied in this paper. Based on the dynamic model of the hydraulic pitching system, an adaptive parameter estimation algorithm has been developed in order to identify the internal and external leakages under the time-varying load on the pitch axis. This scheme can detect and isolate individual faults in spite of their strong coupling in the hydraulic model. A scale-down setup has been developed as the hydraulic pitch emulator, with which the proposed method is verified through experiments. The pitching-axis load input is obtained from simulation of a 1.5-MW variable-speed-variable-pitch turbine model under turbulent wind profiles on the FAST (fatigue, aerodynamics, structural, and tower) software developed by the National Renewable Energy Laboratory. With the experimental data, the leakage and leakage coefficients can be predicted via the proposed method with good performance.