Development of a wind turbine system toolbox for parameter estimation application

A literature review was performed to determine which wind turbine system topology to use as the model for this study. The following topologies were considered.

 Fixed-speed generator topology

 Two-speed induction generator topology

 Variable rotor resistance generator topology

 Generator with fully-rated converter topology

 Generator with direct drive and fully-rated converter topology

 Double-fed induction generator topology

 Directly coupled synchronous generator with variable gearbox topology

From the review it was concluded that the market is appearing to be moving in the direction of the generator with fully-rated converter topology and especially the direct drive generator

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with fully-rated converter. The Double-Fed Induction Generator (DFIG) topology is, however, currently the most common topology when considering the wind turbine systems available from the leading manufacturers. The decision was made to model the components of a fix-speed generator topology, namely the turbine blades, gearbox and generator, for this study. With future studies in mind, the generator component was modelled as a DFIG, but was used as an induction generator for this study by applying 0 V voltages to the rotor windings.

Since the models were developed for use with parameter estimation processes, they were required to be efficient, as they are simulated numerous times during these processes. The mathematical models were derived for each of the components. In an attempt to minimise the simulation time, these models were then implemented as C-code S-function models. The DFIG was initially modelled in the ABC reference frame, but to improve the simulation time it was also modelled in the DQ reference frame.

All four models, i.e., the turbine blade, gearbox, ABC DFIG and DQ DFIG, were validated and their performance evaluated by comparing them to existing Simulink block models developed by the Institute of Energy Technology at the University of Aalborg. By individually supplying the models with identical test input signals and parameter values and comparing the outputs obtained, all four models were proven to be accurate. The components were then connected to form a wind turbine system and compared to the corresponding existing block model system, again using identical test input signals and parameters for both models. The results obtained from this comparison further verified the accuracy of the models.

The performances of the models were evaluated by performing test simulations on the models and comparing the simulation times to that of the corresponding existing block models. This was done for the individual models, as well as the complete system. The test simulations performed on the turbine blade model were done using both generated wind speed data and real wind speed data. The derived turbine blade model showed a reduction in simulation time of about a factor 3.

The gearbox test simulation was performed with generated turbine blade torque and generator torque input signals. This test simulation showed a reduction in simulation time varying between 30% and 40%.

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To evaluate the performance of the generator models, the models were simulated with different input angular velocities ranging from 0 rpm to 3000 rpm and the simulation times recorded. The simulation times of each model were averaged and compared to the average simulation times of the existing models. A reduction of 76% was obtained when comparing the ABC models and a reduction of about 68% was obtained when comparing the DQ models. When comparing the DQ model to the ABC model, a reduction of about 92% was obtained.

These results show that the simulation times of all the individual derived models are significantly shorter than that of the existing Simulink block models, with the gearbox model showing the smallest reduction. This small reduction in simulation time is attributable to the fact that Simulink is very well optimised for solving differential equations and the model of the two-mass gearbox simply consists of two differential equations.

The individual models were then connected to form wind turbine system models and test simulations were performed to compare their simulation times. These systems were simulated with two sets of wind input data, i.e., generated wind speed and real wind speed as well as for two maximum simulation step sizes, i.e., 1 ms and 0.5 ms. Comparing the simulation times of the system with the ABC DFIG as generating element showed a reduction of about 60% for both maximum step sizes. The system with the DQ DFIG as generating element and configured for a maximum step size of 0.5 ms showed a reduction in simulation time of about 50%. With the maximum step configured as 1 ms, the reduction in simulation time was only 29%. This relatively low reduction in simulation time was attributable to an oscillation in the DQ DFIG model caused by a numerical instability problem. The results obtained from the performance evaluation of the wind turbine system model showed a significant overall reduction, especially when considering that these models are simulated numerous times during the parameter estimation process.

With the models shown to be accurate and performing efficiently, masks were created for the models to make them more user-friendly. These masked models were all compiled into a Simulink library.

From this it is concluded that the objective of developing a wind turbine system toolbox for parameter estimation application was achieved.

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6.2.3 Introductory study to determine which parameters of the wind turbine