Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Abstract

Some large wind turbines use a low speed synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. This paper considers the condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. The application of wavelet transforms is investigated because of the disadvantages of conventional spectral techniques in processing instantaneous information in turbine signals derived from the wind, which is variable and noisy. A new condition monitoring technique is proposed which removes the negative influence of variable wind in machine condition monitoring. The technique has a versatile function to detect mechanical and electrical faults in the wind turbine. Its effectiveness is validated by experiments on a wind turbine condition monitoring test rig using a permanent-magnet synchronous generator, which can be driven by aerodynamic forces from a drive motor controlled by an external model, representing wind and turbine rotor behaviour. Within the technique wavelet transforms are employed for noise cancellation and are extended to diagnose faults by taking advantage of their powerful capabilities in analysing non-stationary signals. The diagnosis of wind turbine rotor imbalance in the will be used as an illustrative example, heralding the possibility of detecting a wind turbine mechanical faults by power signal analysis.

DFIG-based wind turbine fault diagnosis using a specific discrete wavelet transform

“…Current studies are more particularly related to methods adapted for non-stationary signals, such as time-frequency analysis and the wavelet decomposition [27][28][29][30]. Table 1 is given to illustrate faults effects (spectral fault signatures) on the stator current where f s is the supply frequency, m = 1, 2, 3…, s is the slip, p is the number of pole pairs, k = 1, 3, 5…, f r is the rotor frequency, n b is the bearing balls number, ƒ i,o is the inner and the outer frequencies depending on the bearing characteristics.…”

Research on Data Acquisition Technology of Wind Turbine Monitoring System

“…With the development of wind power generation, there are more and more accidents of wind turbines in recent years. It becomes necessary to install online monitoring system to detect the working states of wind turbines [1,2]. Data acquisition technology is one of key technologies in the monitoring systems.…”