Dynamic temperature estimation of power electronics systems

Power electronics;Thermal modeling;Estimation;Resistor-capacitor (RC) models;Kalman filter;Structure-preserving model order reduction;Sensor optimization

Tannous, Pamela Joseph ; Alleyne ; Andrew G.

关键词: Power electronics;    Thermal modeling;    Estimation;    Resistor-capacitor (RC) models;    Kalman filter;    Structure-preserving model order reduction;    Sensor optimization;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/98135/TANNOUS-THESIS-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This thesis proposes a method for accurate temperature estimation of thermally-aware power electronics systems. The duality between electrical systems and thermal systems was considered for thermal modeling. High dimensional thermal models present a challenge for online estimation. RC (resistor-capacitor) circuits that create a tradeoff between accuracy and complexity were used to simulate the dynamic thermal behavior of power electronics. The complexity of the thermal network was further reduced by applying a structure-preserving model order reduction technique. The reduced order thermal model was an RC circuit with fewer capacitors. Preserving the physical correspondence between the reduced order model and the physical system allows the user to use the reduced order thermal model in the sensor placement optimization process. The accuracy of the thermal estimates can be easily increased by increasing the number of sensors in the system. However, a large number of sensors increases the cost and complexity of the system. It might also interfere with the circuit design and create packaging problems. An optimal number and optimal placement of temperature sensors was found. The optimal sensor placement problem was solved by maximizing the trace of observability Gramian. The optimal number of temperature sensors was based on the state estimation error obtained from a Kalman filter. The dynamic thermal behavior of the power electronics systems was represented by a linear state space model by applying the conservation of energy principle. Therefore, assuming Gaussian noise, it is well-known that a Kalman filter is an optimal estimator for such systems. A continuous-discrete Kalman filter was used to estimate the dynamic thermal behavior of power electronics systems using an optimal number of temperature sensors placed at optimal locations. The proposed method was applied on 2-D and 3-D power electronics systems. Theoretical results were validated experimentally using IR thermal imaging and thermocouples. It was shown that the proposed method can accurately reconstruct the dynamic temperature profile of power electronics systems using a small number of temperature sensors.