| Description |
Power converters are frequently exposed to electrical stresses such as over voltage, over current and switching impulses during their regular operations. These stresses may not result in immediate failure of a power converter. However, over longer periods they cause gradual degradation of critical components inside the converter, which ultimately leads to a complete failure of the converter. Failure of a power converter might disrupt the operation of the entire system, occasionally causing catastrophic outcomes. Estimating a converter's state of health and predicting the remaining life involves extensive research in semiconductor device physics and circuit theory, and is both important and challenging. There is always a dire need to determine the level of aging in power converters so that an approximate time to failure could be predicted. A reflectometry technique was applied to power converters to identify failure and aging associated to critical components inside a power converter. In addition, mechanisms for gradual shift in measurable electrical parameters of power converter components over long durations have been studied under the scope of the project. While there exist several other techniques for predicting reliability and aging of power converters, they are limited to characterizing isolated components only. Whereas using the proposed technique, estimating the component degradation in energized circuits is possible. Spread spectrum time domain reflectometry (SSTDR) has been commercially used for detecting aircraft wiring faults during the last decade, however, it was never applied to components in a power converter. During the preliminary stage of this project SSTDR was applied to a DC-DC converter circuit, and several key parameters such as MOSFETs ON resistance was extracted to characterize MOSFET aging. Later on, this technique was applied to different other components in an H-bridge AC-AC converter for failure rate estimation and reliability analysis. The MTTF (mean time to failure) was calculated based on the SSTDR generated data. The conducted research has initiated other SSTDR based prognostics and state of health measurement methods applicable to PV panels, electric machines and batteries. |
