Condition monitoring and fault diagnosis of tidal stream turbines subjected to rotor imbalance faults

The main focus of the work presented within this thesis was the testing and development of condition monitoring procedures for detection and diagnosis of HATT rotor imbalance faults. The condition monitoring processes were developed via Matlab with the goal of exploiting generator measurements for rotor fault monitoring. Suitable methods of turbine simulation and testing were developed in order to test the proposed CM processes. The algorithms were applied to both simulation based and experimental data sets which related to both steady-state and non-steady-state turbine operation. The work showed that development of condition monitoring practices based on analysis of data sets generated via CFD modelling was feasible. This could serve as a useful process for turbine developers. The work specifically showed that consideration of the torsional spectra observed in CFD datasets was useful in developing a, ‘rotor imbalance criteria’ which was sensitive to rotor imbalance conditions. Furthermore, based on the CFD datasets acquired it was possible to develop a parametric rotor model which was used to develop rotor torque time series under more general flow conditions. To further test condition monitoring processes and to develop the parametric rotor model developed based on CFD data a scale model turbine was developed. All aspects of data capture and test rig control was developed by the researcher. The test rig utilised data capture within the turbine nose cone which was synchronised with the global data capture clock source. Within the nose cone thrust and moment about one of the turbine blades was measured as well as acceleration at the turbine nose cone. The results of the flume testing showed that rotor imbalance criteria was suitable for rotor imbalance faults as applied to 4 generator quadrature axis current measurements as an analogue for drive train torque measurements. It was further found that feature fusion of the rotor imbalance criterion calculated with power coefficient monitoring was successful for imbalance fault diagnosis. The final part of the work presented was to develop drive train simulation processes which could be calculated in real-time and could be utilised to generate representative datasets under non-steady-state conditions. The parametric rotor model was developed, based on the data captured during flume testing, to allow for non-steady state operation. A number of simulations were then undertaken with various rotor faults simulated. The condition monitoring processes were then applied to the data sets generated. Condition monitoring based on operational surfaces was successful and normalised calculation of the surfaces was outlined. The rotor imbalance criterion was found to be less sensitive to the fault cases under non-steady state condition but could well be suitable for imbalance fault detection rather than diagnosis

Tidal Steam Turbine blade fault diagnosis using time-frequency analyses

Tidal Stream Turbines are developing renewable energy devices, for which proof of concept commercial devices are been deployed. The optimisation of such devices is supported by research activities. Operation within selected marine environments will lead to extreme dynamic loading and other problems. Further, such environments emphasise the need for condition monitoring and prognostics to support difficult maintenance activities. This paper considers flow and structural simulation research and condition monitoring evaluations. In particular, reduced turbine blade functionality will result in reduced energy production, long down times and potential damage to other critical turbine sub-assemblies. Local sea conditions and cyclic tidal variations along with shorter timescale dynamic fluctuations lead to the consideration of time-frequency methods. This paper initially reports on simulation and scale-model experimental testing of blade-structure interactions observed in the total axial thrust signal. The assessment is then extended to monitoring turbine blade and rotor condition, via drive shaft torque measurements. Parametric models are utilised and reported and a motor-drive train-generator test rig is described. The parametric models allow the generation of realistic time series used to drive this test rig and hence to evaluate the applicability of various time-frequency algorithms to the diagnosis of blade faults

Performance and condition monitoring of tidal stream turbines

Research within the Cardiff Marine Energy Research Group (CMERG) has considered the integrated mathematical modelling of Tidal Stream Turbines (TST). The modelling studies are briefly reviewed. This paper concentrates on the experimental validation testing of small TST models in a water flume facility. The dataset of results, and in particular the measured axial thrust signals are analysed via timefrequency methods. For the 0.5 m diameter TST the recorded angular velocity typically varies by ± 2.5% during the 90 second test durations. Modelling results confirm the expectations for the thrust signal spectrums, for both optimum and deliberately offset blade results. A discussion of the need to consider operating conditions, condition monitoring sub-system refinements and the direction of prognostic methods development, is provided

Performance and condition monitoring of tidal stream turbines

Research within the Cardiff Marine Energy Research Group (CMERG) has considered the integrated mathematical modelling of Tidal Stream Turbines (TST). The modelling studies are briefly reviewed. This paper concentrates on the experimental validation testing of small TST models in a water flume facility. The dataset of results, and in particular the measured axial thrust signals are analysed via timefrequency methods. For the 0.5 m diameter TST the recorded angular velocity typically varies by ± 2.5% during the 90 second test durations. Modelling results confirm the expectations for the thrust signal spectrums, for both optimum and deliberately offset blade results. A discussion of the need to consider operating conditions, condition monitoring sub-system refinements and the direction of prognostic methods development, is provided

Effects of wave-current interactions on the performance of tidal stream turbines

The main objective of this paper is to analyse extreme cases of wave-current interactions on tidal stream energy converters. Experiments were undertaken in the INSEAN tow tank facility where flow velocities of 0.5 and 1m/s were used with and without waves. The wave variations studied in this testing campaign were between wave heights of 0.2 to 0.4m with a 2s wave period. These wave conditions were considered extreme cases considering the use of a turbine with a rotor diameter of 0.5m. The turbine was equipped with a torque transducer, an encoder and a strain gauge to measure power coefficients and forces on a single blade root. Therefore, the results of this experiment are used to improve the understanding of wave effects on tidal stream rotors by analysing not only the temporal variations of power and blade loading but also the peak variations of them

Power variability of tidal-stream energy and implications for electricity supply

Temporal variability in renewable energy presents a major challenge for electrical grid systems. Tides are considered predictable due to their regular periodicity; however, the persistence and quality of tidal-stream generated electricity is unknown. This paper is the first study that attempts to address this knowledge gap through direct measurements of rotor-shaft power and shore-side voltage from a 1 MW, rated at grid-connection, tidal turbine (Orkney Islands, UK). Tidal asymmetry in turbulence parameters, flow speed and power variability were observed. Variability in the power at 0.5 Hz, associated with the 10-min running mean, was low (standard deviation 10–12% of rated power), with lower variability associated with higher flow speed and reduced turbulence intensity. Variability of shore-side measured voltage was well within acceptable levels (∼0.3% at 0.5 Hz). Variability in turbine power had <1% difference in energy yield calculation, even with a skewed power variability distribution. Finally, using a “t-location” distribution of observed fine-scale power variability, in combination with an idealised power curve, a synthetic power variability model reliably downscaled 30 min tidal velocity simulations to power at 0.5 Hz (R2 = 85% and ∼14% error). Therefore, the predictability and quality of tidal-stream energy may be undervalued in a future, high-penetration renewable energy, electricity grid