Abstract
The ability to detect faults and predict loads on a wind turbine drivetrain's mechanical components cost-effectively is critical to making the cost of wind energy competitive. In order to investigate whether this is possible using the readily available power converter current signals, an existing permanent magnet synchronous generator based wind energy conversion system computer model was modified to include a grid-side converter (GSC) for an improved converter model and a gearbox. The GSC maintains a constant DC link voltage via vector control. The gearbox was modelled as a 3-mass model to allow faults to be included. Gusts and gearbox faults were introduced to investigate the ability of the machine side converter (MSC) current (Iq) to detect and quantify loads on the mechanical components. In this model, gearbox faults were not detectable in the Iq signal due to shaft stiffness and damping interaction. However, a model that predicts the load change on mechanical wind turbine components using Iq was developed and verified using synthetic and real wind data.
Highlights
Extreme wind conditions such as gusts can lead to very large loads on the turbine that cause fatigue, shut-downs and damage to components such as the gearbox [1]
Condition monitoring (CM) of wind turbine components allows appropriate action to be taken to minimise the impact of developing faults but currently requires expensive sensors and data acquisition devices
This paper investigates whether converter signals, which are already monitored by turbine controllers, can be used for CM
Summary
Extreme wind conditions such as gusts can lead to very large loads on the turbine that cause fatigue, shut-downs and damage to components such as the gearbox [1]. The torque and speed across the rotor and generator are related through the gearbox ratio, NGB using equation (7). The effect of tooth wear in the gearbox was modelled as a reduction in the total stiffness every time there is contact with a worn gear tooth as shown in figure 4.
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