Tags: Renewable Energy, Motor Test Bench, Motor Control, Rapid Control Prototyping, DFIG Contact us Print

About the project

Together with Triphase, the Universitat Politècnica de València realized a test bench for a doubly-fed induction generator (DFIG). This setup is used for development and test of DFIG control strategies and for emulating windmill energy patterns as injected into a grid. Triphase provided all power interfaces, measurements together with low-level control and data logging software. The system starts out of the box. However, the openness of the control software and the system-wide data accessibility enable researchers to modify the setups behavior down to the finest detail.

Highlights

  • Test bench containing both a DFIG and a load motor for emulating wind patterns
  • A four-quadrant motor drive for DFIG control and a two-quadrant motor drive for load motor control integrated into a single setup.
  • Additional sensors for monitoring DFIG stator voltages and currents
  • Open, Simulink-based controls for DFIG and load motor with built-in data logging
  • Test automation via the Python-scripting

The setup

The figure below outlines the test bench architecture. The setup consists of a mechanical test bed, with a DFIG facing an induction motor, coupled to a PM15 converter system for measurement and control. The converter system contains a PM15A30M30 four-quadrant motor drive for DFIG control and a PM15M30 two-quadrant motor drive for load motor control. The converter system is fully reprogrammable. It comes with Simulink-based control algorithms for both DFIG and load motor which are open for users to study and modify. Finally, the system features test automation through its Python and IPython scripting interfaces. This boosts productivity in dealing with repetitive and time consuming tasks.

The setup enables researchers to prototype algorithms for DFIG motor drive control. The algorithms for both the DFIG and the induction motor control are open for researchers to study and change. They can even develop their own algorithms from scratch.

At a higher level, researchers can explore algorithms and strategies for maximum power point tracking (MPPT). MPPT algorithms optimize DFIG rotor speed in order to get the most energy out of the wind. MPPT algorithms are prototyped either in Matlab/Simulnk or as a Python script.

Finally, via its PM15A30-based active grid coupling, the setup also allows researchers to study the impact of wind-mill setups on grid and micro-grid stability. To this account, they can develop and test energy management algorithms to optimize integration with the grid. The Python and IPython scripting interfaces lend themselves as an excellent tool for automating the lengthy tests that come with this kind of research and for automating the analysis of the measurement results stored on the controller’s hard drive.

The setup consists of:

  • A mechanical test bed with a DFIG facing an induction motor. The latter is used for load emulation.
  • A four-quadrant motor drive for DFIG control combined with extra sensors for measuring the DFIG stator currents.
  • A two-quadrant motor drive for driving the induction motor as to emulate wind patterns.

Scope of delivery and customer developments

Triphase Products

Triphase Services

Test bench integration

  • Parameter identification, parametrization and tuning of the built-in motor drives
  • Development of an iPython-script for automation of wind-emulation patterns

Customer Implementations

  • Maximum power point tracking algorithm
  • Energy Management algorithm
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