LAPSE:2023.7546
Published Article

LAPSE:2023.7546
Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile
February 24, 2023
Abstract
Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept.
Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept.
Record ID
Keywords
aging, fatigue, inverters, solar power generation, systems simulation
Subject
Suggested Citation
Liu Y, Tolbert LM, Kritprajun P, Dong J, Zhu L, Ollis TB, Schneider KP, Prabakar K. Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile. (2023). LAPSE:2023.7546
Author Affiliations
Liu Y: Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA [ORCID]
Tolbert LM: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Kritprajun P: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Dong J: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Zhu L: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Ollis TB: Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA [ORCID]
Schneider KP: Pacific Northwest National Laboratory, Richland, WA 99354, USA
Prabakar K: National Renewable Energy Laboratory, Golden, CO 80401, USA [ORCID]
Tolbert LM: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Kritprajun P: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Dong J: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Zhu L: Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
Ollis TB: Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA [ORCID]
Schneider KP: Pacific Northwest National Laboratory, Richland, WA 99354, USA
Prabakar K: National Renewable Energy Laboratory, Golden, CO 80401, USA [ORCID]
Journal Name
Energies
Volume
15
Issue
23
First Page
9104
Year
2022
Publication Date
2022-12-01
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15239104, Publication Type: Journal Article
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LAPSE:2023.7546
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https://doi.org/10.3390/en15239104
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Feb 24, 2023
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