Vertical axis wind turbine case study : costs and losses associated with variable torque and speed strategies

This poster describes a generator case study for large offshore vertical axis wind turbine (VAWT). This work was presented as part of a 3 year PhD into VAWT Drivetrains

Parallel wind turbine powertrains and their design for high availability

Conventional wind turbine powertrains tend to use single-input-single-output topologies (i.e. one gearbox coupled to a generator with a power converter). Here powertrains with single-input-multiple-output subsystems are analyzed with Markov state space models in order to quantify any improvements in availability. A baseline powertrain's availability and that of different parallel powertrains are evaluated using wind turbine powertrain failure and repair rate data. The results show that an increase in the number of parallel systems, N, does not automatically lead to a higher availability for a wind turbine powertrain; however when failure and repair rates scale with module power ratings then there is an improvement. The designer can further improve availability by over-rating each parallel module. The net benefit of parallel powertrains depends both on the turbine and the type of powertrain technology

Optimisation and comparison of generators with different magnet materials for a 6MW offshore direct drive wind turbine

In the past few years interest in the use of low speed permanent magnet generators for direct-drive wind turbine generator applications has increased significantly. The significant fluctuations in NdFeB magnet prices has encouraged designers to optimise magnet utilisation and to look at alternative magnet materials for wind turbine electrical generators. In this paper an analytical design model is developed for 6 MW offshore direct-drive wind turbine generators using different magnet materials (one with surface mounted NdFeB and another with flux concentrating ferrite magnet). Finite element method models are used to check key dependent variables calculated by the analytical models. The generator designs are optimised using a hybrid optimisation method incorporating a Genetic Algorithm and Pattern Search approaches. This is applied for four different objective functions, the first two which concentrate on maximising rated torque per unit magnet mass or unit of generator active material cost. They are simple and quick to execute but prioritise cost reduction and ignore lower efficiencies leading to lower turbine energy yields and hence poor cost of energy. A third objective function which seeks to minimise the sum of the generator active material cost and the costs of lost revenue over a finite number of operational years. This gives similar results to a fourth objective function which is an explicit turbine cost of energy calculation. The cost of NdFeB magnets affect the cost of energy of the surface mounted generator which tested with different cost €40/kg, €60/kg and €80/kg. The ferrite magnet generator being better when the NdFeB magnet price rises to €80/kg

Wind turbine intelligent gear fault identification

This paper aims to present the development of a framework for monitoring of wind turbine gearboxes and prognosis of gear fracture faults, using vibration data and machine learning techniques. The proposed methodology analyses gear vibration signals in the order domain, using a shaft tachometer pulse. Indicators that represent the health state of the gear are algorithmically extracted. Those indicators are used as features to train diagnostic models that predict the health status of the gear. The efficacy of the proposed methodology is demonstrated with a case study using real wind turbine vibration data. Data is collected for a wind turbine at various time steps prior to failure and according to the maintenance reports there is enough data to form a healthy baseline. The data is classified according to the time before failure that the signal was collected.The learning algorithms used are discussed and their results are compared. The case study results indicate that this data driven model can lay the groundwork for a robust framework for the early detection of emerging gear tooth fracture faults. This can lead to minimisation of wind turbine downtime and revenue increase

Reliability comparison of wind turbines with DFIG and PMG drive trains

Modern wind turbines vary greatly in their drive train configurations. With the variety of options available, it can be difficult to determine which type is most suitable for on and offshore applications. A large percentage of modern drive trains consist of either doubly fed induction generators with partially rated converters or permanent magnet generators with fully rated converters. These configurations are the focus of this empirical reliability comparison. The turbine population for this analysis contains over 1800 doubly fed induction generators, partially rated converter wind turbines, and 400 permanent magnet generator fully rated converter wind turbines. The turbines analyzed are identical except for their drive train configurations and are modern MW scale turbines making this population the largest and most modern encountered in the literature review. Results of the analysis include overall failure rates, failure rates per operational year, failure rates per failure mode, and failure rates per failure cost category for the two drive train configurations. These results contribute toward deciding on the most suitable turbine type for a particular site, as well as toward cost of energy comparisons for different drive train types. A comparison between failure rates from this analysis and failure rates from similar analyses is also shown in this paper

Vertical axis wind turbines : minimising generator losses by torque control

Vertical Axis Wind Turbines (VAWTs) tend to produce mechanical torque which varies significantly with rotor azimuth angle. The cost of the electrical generator is related to the peak generator torque which in turn depends on the choice of torque/speed control strategy. This choice of control strategy also affects the copper and iron losses of the generator; these can be modelled using harmonic analysis. The paper demonstrates how an optimal control strategy (from a generator loss point of view) can be achieved without significant extra cost

Optimisation of design and operation of generators for offshore vertical axis wind turbines

A process for optimising both the design and operation of the generator for a large offshore vertical axis wind turbine (VAWT) is developed. The objectives of the optimisation process are to minimise additional costs and losses in the generator to allow for a fair evaluation of the impact of the VAWT environment on the powertrain. A spectrum of torque control strategies was tested based on the ratio, q, of the allowed electrical torque variation to the inherent mechanical torque variation. Equations relating q to the generator losses were established. The effect of q on the energy extracted by the rotor was also investigated and incorporated into the optimisation process. This work shows that a variable q strategy with respect to wind speed can improve turbine performance across the range of operational wind speeds depending on the torque loading from the rotor blades. In turn, this also allows for the torque rating of the generator to be reduced from the peak torque rating that would otherwise be expected, creating an opportunity to downscale the generator size, reducing costs. The optimisation of powertrain design and operation should be carried out at as high level as is possible, ideally using the fully factored Cost of Energy (COE) to guard against unexpected losses due to excessive focus in one COE factor (for example reducing upfront cost but in turn reducing availability)

Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

Reducing downtime through predictive or condition-based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single-class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation

Enhancing PV modules efficiency and power output using multi-concept cooling technique

The efficiency and power output of a PV module decrease at the peak of sunlight due to energy loss as heat energyand this reduces the module power output. Multi-concept cooling technique, a concept that involves three types of passive cooling, namely conductive cooling, air passive cooling and water passive cooling has the potential to tackle this challenge. The experiment was set up using two solar panels of 250 watts each with both modules mounted at a height of 37 cm to create room for air-cooling, with the application of water-cooling at the surface of one of the PV modules to reduce the surface temperature to 20 ∘C. The rear of the same module attached to an aluminium, Al heat sink. The other module also mounted was without water-cooling and Al heat sink attachment. The Al heat sink comprises aluminium plate attached with aluminium fins to aid cooling, and water at a reduced temperature achieved with the introduction blocks of ice facilitated the module surface cooling. Analysis of the power output achieved, carried out with the help of the equation for PV array power output with a derating factor of 80%. The experiment recorded an increase in output power of 20.96 watts, and an increase in efficiency of not less than 3% achieved thus making the module more efficient and productive