Health assessment of pitch and yaw systems for offshore wind turbines
Although pitch and yaw systems are often on the list of components with frequent failures, they have not been a top priority for condition monitoring in the onshore wind turbines. This is due to the fact that they are easily replaceable in the case of onshore wind. However, in case of offshore wind turbines, any unplanned maintenance activity is expensive due to their location as well as being challenging due to short weather windows. Therefore, it is worthwhile to evaluate the pitch and yaw systems for condition monitoring and remote health assessment. Through remote health assessment, the maintenance personnel can detect incipient faults and plan the maintenance ahead of failures. Such planning results cuts costs on logistics, inventory and resources. However, health assessment of these systems poses significant challenges as they operate intermittently and at low speeds.
At UiA, the electrically operated pitch & yaw systems are being evaluated for condition monitoring and health assessment. The research is focused on answering the following questions:
a. What is the nature of the pitch and yaw system operations in typical wind conditions? Can the incipient faults be reliably detected under such operating profiles?
b. What methods and techniques are suitable for detection? Are they suitable for farm-level implementation?
c. Is reliable failure prediction (prognosis), that is sensitive to operating conditions, feasible?
Figure 1: Laboratory setup with pitch drive (left) and load motor with bph gearbox (right)
In order to answer these questions a laboratory setup, shown in Figure 1, is built. It includes a multistage planetary gearbox and an induction motor controlled through a variable frequency drive while the blade root loads that are experienced by the pitch drive are generated using a load motor through a bevel-planetary-helical (BPH) gearbox. The objective of this setup is to simulate various seeded faults described in Table 1, in the pitch motor and the gearbox and evaluate detection capabilities. In order to assess the feasibility of diagnostics in realistic conditions, the 5MW reference wind turbine is simulated with the FAST tool to generate pitch speed profile and blade root load profiles. The motor faults are then diagnosed using motor current signature analysis (MCSA) and the planetary gearbox faults shall be diagnosed using vibration based methods.
Figure 2: Faulty rotor with 3 rotor bars partially broken
As a first step, the rotor on the pitch motor is replaced with a damaged one as shown in Figure 2, and the 3-phase motor currents are then analyzed using MCSA. The principle of MCSA is that any change in the motor’s electrical or magnetic circuit will produce a periodic disturbance in the electrical fields. This disturbance is then detected using Fourier analysis on the supply currents.
The Fourier analysis results for the machine with healthy rotor and with faulty rotor are shown in Figure 3. The analysis is performed on a motor operating at a speed greater than 1000 RPM and the current signals were analyzed for only 2 seconds. It can be seen that the broken rotor bar fault is clearly visible in that short period of constant speed operation.
Table 1: Failure modes that will be seeded in the lab setup
Figure 3: Comparison of Fourier spectrum of healthy motor (a) and faulty motor (b)
In the near future, the intention is to simulate the rest of the faults described in Table 1 and enhance diagnostic capabilities to detect faults in time-varying speed conditions.
Further on, methods will be developed to predict the damage progression and time to failure, in order to assist in maintenance planning.
Text and pictures: PhD student Surya Teja Kandukuri, University of Agder, Grimstad
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