Predicting the Performance of Soft Sensors as a Route to Low Cost Automation
Abstract Control improvements often result from the development of new (hardware) sensors to facilitate the measurement of key process variables. However, the development of new sensors is often a prohibitively costly exercise. Hence, in the context of low cost automation, it is of great interest to consider the use of model-based (or soft) sensors in lieu of hardware sensors. In principle, this is generically possible. However, there are unavoidable performance penalties associated with the use of soft-sensors. In this paper, we will describe procedures for predicting the performance of soft-sensors so that their use can be appropriately evaluated as a route to low cost automation.
Abstract Wind is one of the fastest-growing renewable sources of electricity. As with most renewables, the economic viability relies on public subsidies. Even among renewables, the economic evaluation of wind projects is particularly challenging due to the unpredictability of the energy source, wind, and the high sensitivity of the project profitability to changes in a number of parameters. This makes the uncertainty analysis an important topic of research in the wind power sector. This study presents a method of combining two uncertainty analysis methods, sensitivity study and the Monte Carlo method, together with a technical and economic model of a wind farm, in an effort to improve the understanding of the practical effects of the uncertainties, and how they affect the financial risks of wind projects.