Gradient-Based Predictive Pulse Pattern Control of Induction Machine Drives

In recent years, market trends in industrial drive applications, electrification, and renewable power generation have significantly boosted the annual growth of medium-voltage drives. These drives typically operate at switching frequencies of a few hundred hertz to minimize switching power losses. However, this leads to increased stator current harmonics, which in turn cause higher thermal losses in the machine. To mitigate these issues, optimized pulse patterns (OPPs) can be employed, as they produce very low current distortions at low switching frequencies. However, using OPPs with a fast controller is challenging due to the lack of a symmetric modulation cycle of fixed length.

This thesis addresses these challenges by developing a control and modulation strategy for medium-voltage drives that utilizes a three-level neutral point clamped inverter driving an induction machine, ensuring both excellent steady-state and transient performance. Specifically, OPPs and direct model predictive control are employed so that the associated advantages of both, such as minimum stator current total demand distortion (TDD) and fast transients, respectively, are fully exploited. To do so, the current reference trajectory tracking and modulation problems are addressed in a coordinated manner in the form of a constrained optimization problem that utilizes the knowledge of the stator current gradients within the prediction horizon. Solving this problem yields the optimal real time modifications of the offline-computed patterns, ensuring superior performance.

The results demonstrate that the proposed method reduces the stator current TDD by 40% at low switching frequencies compared to established control methods such as field oriented control with space vector modulation, while also achieving more than 20% shorter settling times during transients. Furthermore, with minor refinements, the control method can address multiple control objectives in a single computational stage, highlighting its multipleinput multiple-output capabilities. Specifically, the controller is extended to include neutral point potential balancing as part of the overall control problem. The effectiveness of the proposed approach is validated through simulation, hardware-in-the-loop, and experimental results.