Advanced Primary Controllers for Inverter Based Power Sources: Microgrids and Wind Power Plants

The aim of this doctoral thesis is to present the research activity fulfilled during the Ph.D. studies. The research project of the candidate was focused on two main cores. The first core is centred in the microgrid area; in particular in islanded microgrid modelling and control. Firstly, the model was compared with experimental results collected in some facilities available at University of Genoa. Then traditional controllers for islanded microgrid are analysed and explored, proposing a new stability estimation procedure for droop controlled microgrid. Finally, a new control strategy based on Model Predictive Control (MPC) is proposed in order to collect many functionalities in just one control layer. MPC is widely used in MG environment, but just for power and energy management at tertiary level; instead here it is here proposed with an inedited use. Some experimental validations about this new methodology are obtained during a research period in Serbia and Denmark. The second core is related with synthetic inertia for wind turbine connected to the main grid, i.e. frequency support during under-frequency transients. This aspect is very important today because it represents a way to increase grid stability in low inertia power systems. The importance of this feature is shared by all the most important Transmitter System Operators (TSO) all over the world

Approximate characterization of large Photovoltaic power plants at the Point of Interconnection

The aim of the present article is that of proposing a calculation procedure to assess electric quantities at the Point of Interconnection (POI) of large PhotoVoltaic (PV) power plants on the basis of rated data and main design elements of the plant itself. The quantities of inters are active and reactive power available at the POI in order to extrapolate the power plant capability starting from the capabilities of PWM inverters. The procedure also allows evaluating the POI voltage variations, an important element due to the increasing requirements for renewable generation units to participate in voltage regulation. The main interest in such a methodology lays in its simplicity of application, that allows avoiding the usage of dedicated software for load flow calculations, and flexibility, that makes it suitable for the support of the bidding and pre-design phase of large photovoltaic power plants

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk

Modeling and Experimental Validation of an Islanded No-Inertia Microgrid Site

The paper proposes a simple but effective model for no-inertia microgrids suitable to represent the instantaneous values of its meaningful electric variables, becoming a useful platform to test innovative control logics and energy management systems. The proposed model is validated against a more detailed microgrid representation implemented in the electromagnetic simulator PSCAD-EMTDC and then against experimental data collected on the University of Genoa test bed facility. Recorded data highlight a good trade-off in matching the results of the proposed model, confirming its suitability to be used for the preliminary testing of new control logics for islanded microgrids

Misure integrate di conservazione di Calendula maritima Guss., specie rara e minacciata della costa occidentale della Sicilia

The last century witnessed a significant rise in human impact on Mediterranean coastal areas, leading to extensive habitat destruction and the extinction of numerous plant species. Among these, Calendula maritima Guss. (Asteraceae), a rare herbaceous plant endemic to western Sicily, faced a critical decline, with only 16 small, scattered populations remaining along the coast and surrounding islets. Classified as Critically Endangered by IUCN, it ranks among the Top 50 Mediterranean island plants at risk of extinction. To address this, the LIFE project CalMarSi (https://lifecalmarsi.eu/) initiated a comprehensive conservation strategy. Key actions included i) genetic characterisation of all populations to assess the genetic variability and prevent the risk of genetic pollution by the congeneric species C. fulgida Raf. in order to conserve the purest and most diverse lines; ii) in-vitro propagation of genetically selected lineages for in situ and ex situ conservation actions, and iii) population reinforcement/reintroduction following translocation principles. Additionally, efforts targeted invasive species removal, reducing mechanical disturbances, enacting legal protection laws, and raising awareness about the vulnerability of both habitat and species. This integrated approach aimed to secure the long-term conservation of C. maritima and mitigate the imminent threat of extinction

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

AbstractClinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.</jats:p

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients ( F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) ( χ 2 10.4; p &lt; 0.001 ), neutrophil-to-lymphocyte (NL) ratio ( χ 2 7.6; p = 0.006 ), and platelet count ( χ 2 5.39; p = 0.02 ), along with age ( χ 2 87.6; p &lt; 0.001 ) and gender ( χ 2 17.3; p &lt; 0.001 ), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL   ratio &gt; 4.68 was characterized by an odds ratio for in-hospital mortality   OR = 3.40 (2.40-4.82), while the OR for a RDW &gt; 13.7 % was 4.09 (2.87-5.83); a platelet   count &gt; 166,000 /μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.</jats:p