20 August 2009 Using probabilistic methods to define reliability requirements for high power inverters
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The solar energy industry has reached the point where utilities are employing large multi-megawatt photovoltaic projects to provide power to the utility grid in a variety of configurations. The ability to deliver or connect solar energy to a commercial power grid with high efficiency and over a broad spectrum of environments is critical to the success of the solar utility industry. These analyses aid in evaluating the potential profitability of a project and aid in optimizing a power plant design by understanding the real life attributes of the components and subsystems. This paper presents a methodology for modeling system-level power production as a function of time based on concurrent reliability simulation of individual subsystems such as power inverters. Each subsystem model is also time-dependent and depends on understanding the probabilistic nature of the actual failure rates of the components and on endurance testing or field data for the subsystems. As an example of a detailed time-dependent predictive reliability model, the power inverter is used. The power inverter reliability and availability is essential for continuing improvement of solar power plant efficiency and cost effectiveness over the life of the installation. The authors discuss some of the common failure probability distributions, their application to components and how these affect such areas as the maintenance intervals and the number of expected spares needed. This affects the Levelized Cost of Energy (LCOE) of the system and the potential for profitable operation.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Russell W. Morris, Russell W. Morris, John M. Fife, John M. Fife, "Using probabilistic methods to define reliability requirements for high power inverters", Proc. SPIE 7412, Reliability of Photovoltaic Cells, Modules, Components, and Systems II, 74120G (20 August 2009); doi: 10.1117/12.826528; https://doi.org/10.1117/12.826528


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