Browsing by Author "Scholz, Teresa"
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- A cyclic time-dependent Markov process to model daily patterns in wind turbine power productionPublication . Scholz, Teresa; Lopes, Vitor V.; Estanqueiro, AnaWind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating nature of its source. To achieve an adequate reserve commitment which mitigates wind integration costs as well as to promote market participation, it is necessary to provide models that can capture daily patterns in wind power production. This paper presents a cyclic inhomogeneous Markov process, which is based on a three-dimensional state-space partition of the wind power, speed and direction variables. Each transition probability is a time-dependent function, expressed as a Bernstein polynomial. The model parameters are estimated by solving a constrained optimization problem: The objective function combines two maximum likelihood estimators, one to ensure that the Markov process long-term behavior reproduces the data accurately and, another to capture daily fluctuations. The paper presents a convex formulation for the overall optimization problem and demonstrates its applicability through the analysis of a case-study. The proposed model is capable of reproducing the diurnal patterns of a three-year dataset collected from a wind turbine located in a mountainous region in Portugal. In addition, it is shown how to compute persistence statistics directly from the Markov process transition matrices. Based on the case-study, the power production persistence through the daily cycle is analysed and discussed.
- Monitoring bacterial processes by Fourier transform infrared spectroscopy : Helicobacter pylori drug inactivation and plasmid bioproduction in recombinant Escherichia coli culturesPublication . Scholz, Teresa; Lopes, Vitor V.; Calado, Cecília R. C.Fourier transform infrared (FTIR) spectroscopy is evaluated as a tool to monitor two bacterial processes: strain discrimination and drug inactivation studies with the gastric pathogen Helicobacter pylori and the plasmid production process based on high-density cultures of recombinant Escherichia coli. Results show, that after evaluation of different incubation conditions of H.pylori with the drug model, the application of principal component analysis to the FTIR spectra assembles the samples into clusters which can be related with the minimal inhibitory concentration. Morever, the same methodology applied to FTIR spectra from 12 different strains can be used to distinguish them. For the E.coli cultures it is possible to estimate the concentration of relevant bioprocess monitoring variables, such as plasmid, biomass, and carbon sources (glucose, glycerol, acetate) by using partial least squares (PLS) models based on FTIR spectra.
- On the use of Markov chain models for the analysis of wind power time-seriesPublication . Lopes, Vitor V.; Scholz, Teresa; Estanqueiro, Ana; Novais, Augusto Q.Wind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating and intermittent nature of its source. This paper explores the use of Markov chain models for the analysis of wind power time-series. The proposed Markov chain model is based on a 2yr dataset collected from a wind turbine located in Portugal. The wind speed, direction and power variables are used to define the states and the transition matrix is determined using a maximum likelihood estimator based on multi-step transition data. The Markov chain model is analyzed by comparing the theoretically derived properties with their empirically determined analogues. Results show that the proposed model is capable of describing the observed statistics, such as wind speed and power probability density as well as the persistence statistics. It is demonstrated how the application of the Markov chain model can be used for the short-term prediction of wind power.