Circuit-Based Rainflow Counting Algorithm in Application of Power Device Lifetime Estimation
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Energies, 2022, 15, (14)
- Issue Date:
- 2022-07-01
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The software-assisted reliability assessment of power electronic converters is increasingly important due to its multi-domain nature and extensive parametric calculations. The rainflow counting algorithm is gaining popularity for its low relative error in device lifetime estimation. Nevertheless, the offline operation of the algorithm prevents most simulation software packages considering other parameters for the device under study, such as aging and the current state of health in the estimation, as it requires a complete loading profile to run recursive comparison. This also brings difficulties in realization in circuit simulators such as SPICE. To tackle the issue, an in-the-loop circuit-based rainflow counting algorithm is proposed in this paper, and applied to estimate the consumed lifetime of the MOSFET in a boost converter for illustration. Instantaneous electrical and thermal performances, and the accumulated stress of the device can be monitored. Not only does this assist in evaluating the state of health of a device, but also allows the possibility of integrating the aging into the lifetime evaluation. The method follows the four-point rainflow counting algorithms, which continuously compares three adjacent temperature fluctuations (Formula presented.) to select full cycles for two rounds, and the remaining cycles are counted as half cycles. To validate the performance, a comparative analysis in terms of counting accuracy and simulation speed was performed alongside the proposed method, MATLAB® and also with a well-accepted half-cycle counting method. Reported results show that the proposed method has an improved counting accuracy compared to the half-cycle counting from 24% to 3.5% on average under different load stresses and length conditions. The accuracy can be effectively improved by a further 1.3–2% by adding an extra comparison round.
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