Renewable distributed generation is rapidly growing as a crucial topic in modern power system's generation design. This paper deals with the photovoltaic inverter system, focusing on the problem of its reliability predictions: these have indeed a paramount importance in helping the engineer to optimize the system design, in order to choose the most suitable inverter configuration. In particular, the problem of data uncertainty, due to a scarce knowledge of the components' reliabilities, is taken into account. This problem is crucial for new technology systems and is faced within a Bayesian framework: components' reliability and availability parameters - hazard rate (HR) and repair rate (RR) - are considered as random variables, characterized in the paper by Gamma or distributions. Such methodology allows expressing the system availability uncertainty as a function of component uncertain data. Then, by means of a Bayesian inference methodology, it is shown how even scarce data can efficiently update the system performances evaluation in its operating life. In the final part of the paper, a numerical application is presented to show the feasibility of the approach.

Bayes assessment of photovoltaic inverter system reliability and availability

BATTISTELLI, LUIGI;CHIODO, ELIO;LAURIA, DAVIDE
2010

Abstract

Renewable distributed generation is rapidly growing as a crucial topic in modern power system's generation design. This paper deals with the photovoltaic inverter system, focusing on the problem of its reliability predictions: these have indeed a paramount importance in helping the engineer to optimize the system design, in order to choose the most suitable inverter configuration. In particular, the problem of data uncertainty, due to a scarce knowledge of the components' reliabilities, is taken into account. This problem is crucial for new technology systems and is faced within a Bayesian framework: components' reliability and availability parameters - hazard rate (HR) and repair rate (RR) - are considered as random variables, characterized in the paper by Gamma or distributions. Such methodology allows expressing the system availability uncertainty as a function of component uncertain data. Then, by means of a Bayesian inference methodology, it is shown how even scarce data can efficiently update the system performances evaluation in its operating life. In the final part of the paper, a numerical application is presented to show the feasibility of the approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/373543
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