In the last decade thermophotovoltaic (TPV) generator has gained an increasing attention as cogeneration system for the distributed generation sector. Nevertheless, these systems are not fully developed and studied: several aspects need to be further investigated and completely understood.\ud
The aim of this study is to give a complete overview and the status of the art of thermophotovoltaic generation considering both the research developments and the experiences field. More in details, in this study, the characteristics of a TPV generator are analyzed with a particular attention to the physical relationships which govern the behavior of its main components. Moreover, the current technologies regarding the combustor, the emitter, the optical filter and the photovoltaic cells are investigated by taking into account both the role of each component and also their integration in the whole system. Finally, a critical review of the realized prototypes is presented and discussed
To optimize both production and maintenance, from both a technical and an economical
point of view, it would be advisable to predict the future health condition of a system and
of its components, starting from field measurements taken in the past. For this purpose,
this paper presents a methodology, based on the Monte Carlo statistical method, which
aims to determine the future operating state of a gas turbine. The methodology allows the
system future availability to be estimated, to support a prognostic process based on past
historical data trends. One of the most innovative features is that the prognostic methodology
can be applied to both global and local performance parameters, as, for instance,
machine specific fuel consumption or local temperatures. First, the theoretical background
for developing the prognostic methodology is outlined. Then, the procedure for
implementing the methodology is developed and a simulation model is set up. Finally, different
degradation-over-time scenarios for a gas turbine are simulated and a sensitivity
analysis on methodology response is carried out, to assess the capability and the reliability
of the prognostic methodology. The methodology proves robust and reliable, with a
prediction error lower than 2%, for the availability associated with the next future data
trend
Abstract-In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation.
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