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Optimization of Electrical Drives in Transient for Traction Applications



Giampaolo Torrisi, Damian Frick

The optimization of the operations of electrical drives, made of a motor and an inverter, is a topic of paramount importance when considering energy efficiency of industrial plants. They are responsible for around 45% of the total global electric power consumption, thus any improvement of their efficiency can result to be extremely benefi cial in terms of power savings and emissions reduction.

Power losses in Induction Motors consist of two components: the copper losses, which can be calculated from the known stator currents, and the iron losses, which are due to magnetic effects and are in general hard to estimate. Previous research work delivered a experimental method to identify iron losses in drives, thus a nonlinear model for such losses is available. Considering such model in steady-state conditions gives benefits in terms of reduced power losses at low speeds.

The improvement comes at the expense of a reduced flexibility in changing the operating point. Hence, the question that we pose is whether any improvement is obtainable in transient conditions, when the operating point continuously changes. As an example for typical transient operations, required torque\speed profiles coming from traction applications are considered.

In this project, we are looking for a motivated and independent student analyzing potential benefits of transient optimization in drives, in particular:

  • Write down a well-posed optimization problem to describe the transient phenomena
  • Deal efficiently with non-linearities of the system by convex relaxation
  • Solve the optimization problem in several case study
  • Assess the benefit compared to state of the art techniques

The research activity is completely theoretical and no previous knowledge of electrical drives is required. The technical prerequisites are competence in convex optimization and basic MATLAB programming

Weitere Informationen

Roy Smith

Art der Arbeit:
  • convex optimization
  • basic MATLAB programming
Anzahl StudentInnen:
Status: done
Semester: Spring Semester 2016