Publications

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A reliability and availability model has been developed for a portion of the 4.6 megawatt (MWdc) photovoltaic system operated by Tucson Electric Power (TEP) at Springerville, Arizona using a commercially available software tool, GoldSim{trademark}. This reliability model has been populated with life distributions and repair distributions derived from data accumulated during five years of operation of this system. This reliability and availability model was incorporated into another model that simulated daily and seasonal solar irradiance and photovoltaic module performance. The resulting combined model allows prediction of kilowatt hour (kWh) energy output of the system based on availability of components of the system, solar irradiance, and module and inverter performance. This model was then used to study the sensitivity of energy output as a function of photovoltaic (PV) module degradation at different rates and the effect of location (solar irradiance). Plots of cumulative energy output versus time for a 30 year period are provided for each of these cases.

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This paper develops Classical and Bayesian methods for quantifying the uncertainty in reliability for a system of mixed series and parallel components for which both go/no-go and variables data are available. Classical methods focus on uncertainty due to sampling error. Bayesian methods can explore both sampling error and other knowledge-based uncertainties. To date, the reliability community has focused on qualitative statements about uncertainty because there was no consensus on how to quantify them. This paper provides a proof of concept that workable, meaningful quantification methods can be constructed. In addition, the application of the methods demonstrated that the results from the two fundamentally different approaches can be quite comparable. In both approaches, results are sensitive to the details of how one handles components for which no failures have been seen in relatively few tests.

Abstract not provided.