Open student thesis
This work aims to improve the accuracy of temperature estimation in power semiconductor devices using Temperature-Sensitive Electrical Parameters (TSEPs), which are often affected by noise, anomalies, and drift due to device degradation. The project will investigate advanced techniques for denoising, anomaly detection, and drift correction to enhance estimation accuracy with minimal sensor inputs. Key tasks include selecting and evaluating suitable denoising and anomaly detection algorithms for TSEP-based temperature estimation, comparing aspects such as accuracy, response time, and algorithm complexity. An additional goal is to minimize the number of TSEPs needed for accurate junction temperature estimation using machine learning methods.
Relevant Experience:
- MATLAB or Python
- Understanding of power electronics
- Knowledge in signal processing and machine learning
Contact:
Increasing switching frequencies are placing ever greater demands on signal generation and processing for power electronics applications.Higher switching frequencies allow the use of smaller passive components, but require faster control systems. In order to realize such a control system, a control and actuation system is required that can cope with the increased requirements. In order to be able to to operate at these elevated frequencies, microcontrollers no longer offer sufficient performance. Thus, an FPGA based alternative is to be developed based on the Diligent Cmod A7 35T evaluation board.
Type of Thesis:
BA ✅ FA ✅ MA ✅
Relevant Experience:
- Control Therory
- Modulation schemes for power electronics
- Programming skills ideally in connection with FPGAs desirable
Contact:
Contact
Dominik Koch
M.Sc.Group Leader Power Electronics / Research Assistant
Benjamin Schoch
M.Sc.Research Assistant