Performance analysis of electrical signature analysis-based diagnostics using an electromechanical model of wind turbine

Abstract Electrical signature analysis-based (ESA-based) diagnostics of powertrain faults in wind turbines (WTs) is a promising alternative to the more traditional vibration-based condition monitoring. However, the attempt to identify mechanical faults in electrical signals requires the consideration of the complex electromechanical dynamics of the WT. This paper investigates the potential masking effect of power electronic switching and wind-induced speed fluctuations on the electrical signatures of typical powertrain mechanical faults (i.e. rotor imbalance, gear cracks and other localised faults). To identify the conditions in which these masking effects arise and their severity, an innovative full electromechanical model of a WT has been developed, based on the integration of previously proposed models of WT sub-systems, and with the addition of powertrain fault models. This numerical controlled environment allows assessing the impact of power electronics and wind-speed fluctuation on the detectability of powertrain faults by ESA. The results show the criticality of switching-induced noise over the whole range of simulated faults, whereas turbulence-induced noise is mainly affecting the detectability of low frequency signatures. An order-of-magnitude sensitivity analysis is provided for the selected faults and their interaction with the two masking effects, thus providing valuable indications for the development of WT ESA-based condition monitoring systems.

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