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Automating Component-Level Stress Measurements for Inverter Reliability Estimation

Author

Listed:
  • Jack Flicker

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Jay Johnson

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Peter Hacke

    (National Renewable Energy Laboratory, Lakewood, CO 80401, USA)

  • Ramanathan Thiagarajan

    (National Renewable Energy Laboratory, Lakewood, CO 80401, USA)

Abstract

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Suggested Citation

  • Jack Flicker & Jay Johnson & Peter Hacke & Ramanathan Thiagarajan, 2022. "Automating Component-Level Stress Measurements for Inverter Reliability Estimation," Energies, MDPI, vol. 15(13), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4828-:d:853631
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    References listed on IDEAS

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    1. Adam Summers & Jay Johnson & Rachid Darbali-Zamora & Clifford Hansen & Jithendar Anandan & Chad Showalter, 2020. "A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations," Energies, MDPI, vol. 13(14), pages 1-26, July.
    2. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    3. Hani Albalawi & Sherif Ahmed Zaid, 2018. "An H5 Transformerless Inverter for Grid Connected PV Systems with Improved Utilization Factor and a Simple Maximum Power Point Algorithm," Energies, MDPI, vol. 11(11), pages 1-17, October.
    4. Talha, Muhammad & Raihan, S.R.S. & Rahim, N Abd, 2020. "PV inverter with decoupled active and reactive power control to mitigate grid faults," Renewable Energy, Elsevier, vol. 162(C), pages 877-892.
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    1. Yunting Liu & Leon M. Tolbert & Paychuda Kritprajun & Jiaojiao Dong & Lin Zhu & Thomas Ben Ollis & Kevin P. Schneider & Kumaraguru Prabakar, 2022. "Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile," Energies, MDPI, vol. 15(23), pages 1-24, December.

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