Study of the Time Response of a Simulated Hydroelectric System

This paper addresses the design of an advanced control strategy for a typical hydroelectric dynamic process, performed in the Matlab and Simulink environments. The hydraulic system consists of a high water head and a long penstock with upstream and downstream surge tanks, and is equipped with a Francis turbine. The nonlinear characteristics of hydraulic turbine and the inelastic water hammer effects were considered to calculate and simulate the hydraulic transients. With reference to the control solution, the proposed methodology relies on an adaptive control designed by means of the on–line identification of the system model under monitoring. Extensive simulations and comparison with respect to a classic hydraulic turbine speed PID regulator show the effectiveness of the proposed modelling and control tools

Gas Turbine Health State Determination: Methodology Approach and Field Application

A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5 MW gas turbine working in a natural gas compression plant is presented

Benchmarking of Advanced Control Strategies for a Simulated Hydroelectric System

This paper analyses and develops the design of advanced control strategies for a typical hydroelectric plant during unsteady conditions, performed in the Matlab and Simulink environments. The hydraulic system consists of a high water head and a long penstock with upstream and downstream surge tanks, and is equipped with a Francis turbine. The nonlinear characteristics of hydraulic turbine and the inelastic water hammer effects were considered to calculate and simulate the hydraulic transients. With reference to the control solutions addressed in this work, the proposed methodologies rely on data-driven and model-based approaches applied to the system under monitoring. Extensive simulations and comparisons serve to determine the best solution for the development of the most effective, robust and reliable control tool when applied to the considered hydraulic system

Optimal design of a hybrid energy plant by accounting for the cumulative energy demand

In this paper, the optimal design of a hybrid energy plant composed of a solar thermal collector, a photovoltaic panel, a combined heat and power system, an absorption chiller, an air source heat pump, a ground source heat pump and a thermal energy storage is studied. The size of each technology is optimized by applying a model implemented in Matlab® environment. The optimization goal is the minimization of the primary energy consumed throughout the life cycle of the hybrid energy plant by using a genetic algorithm. The primary energy consumed during the manufacturing phase of the hybrid energy plant is represented by the cumulative energy demand and is calculated by carrying out a cradle to gate life cycle assessment. The primary energy consumed during the operation phase is evaluated by simulating the system throughout one year. The cumulative energy demand of each system composing the hybrid energy plant is calculated as a function of the technology size. Therefore, the problem of life cycle assessment scaling of renewable and non-renewable energy systems is also taken into account in this paper. A tower located in the north of Italy is selected as a case study and two different approaches are evaluated. The first approach consists of solving the sizing optimization problem by minimizing the primary energy consumption only during the operation phase, while in the second approach the primary energy consumption is minimized throughout the life cycle of the plant by integrating the life cycle assessment into the optimization process. The results show that, if life cycle assessment is accounted for, the optimal hybrid energy plant configuration is different and a higher primary energy saving (approximately 12%) is achieved

Analysis of tripod supported offshore wind turbines under conditions of marine growth

Offshore wind turbines are now a mature technology to produce renewable energy on a vast scale, nonetheless several design and maintenance planning challenges remain. There have been attempts to investigate the impact of marine growth on fixed offshore wind turbine structures, but only few adopted a whole dynamics approach. This work presents a methodology to capture the influence of marine growth on the dynamic response of a tripod substructure, supporting the NREL 5 MW reference offshore wind turbine, under combined dynamic loads from wind and waves, and including soil-structure interaction by means of the spring-to-ground model. Marine growth is modelled as prescribed by DNV and API, evaluating the effects of variation of its thickness, roughness, and distribution. It is here demonstrated that marine growth thickness and roughness impact significantly on the loads acting on wind turbines' structures and its dynamic response, and that heterogeneity in marine growth thickness profiles vs depth available in literature lead to substantially different results. Tower top displacement becomes 24% higher when marine growth thickness grows from 0 to 200 mm. On the other hand, the changes in the natural frequencies of the support structure with an increase of marine growth's thickness are almost negligible (0.3%)

Harmonized and systematic assessment of microalgae energy potential for biodiesel production

With their fast growth rate and ability to accumulate a high percentage of their weight as lipid and carbohydrate, microalgae potentially represent an ideal feedstock for the production of biodiesel and bioethanol. In addition, microalgae offer several environmental benefits, and do not compete with food production for land, fresh water, and nutrients. Therefore, the main goal of this work is to provide a quantitative, systematic and harmonized assessment of current bio-energy potential. The analysis is conducted by considering all the main steps in detail, from cultivation to biodiesel production, and by deriving an overall estimation of energy consumption for biodiesel production. Energy consumption uncertainty is also quantified and discussed. A systematic review of all the main technologies available for all the main processing steps towards the production of biodiesel from microalgae is presented, focusing on the derivation of the Net Energy Ratio (NER) of each combination of technologies, complemented by an uncertainty analysis of the data used and those obtained in the present work. A wide scatter in the data available in the literature has been identified, highlighting the need for an uncertainty analysis. If the average overall energy consumption per unit of biodiesel mass is considered, all the routes adopting a raceway pond have a lower energy consumption, but if the uncertainty on the overall energy consumption is also considered, the minimum value of the range of NER values for some of the routes adopting a photobioreactor is comparable to the NER value obtainable by using raceway ponds. Thus, the present framework proposes a harmonized and comprehensive methodology to compare and contrast technologies for the production of biodiesel from microalgae, and is applied in this paper to identify, with an appreciation of the uncertainty, the most promising combinations of technologies

Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life

Espacios urbanos como escenarios de significación de la escuela secundaria en Caleta Olivia y Comodoro Rivadavia

En el artículo se aborda un estudio cuantitativo respecto de las percepciones que los/las estudiantes tienen acerca de la formación que reciben en escuelas secundarias públicas. Particularmente respecto de saberes vinculados a la preparación para el uso de técnicas de estudio, para la ciudadanía responsable y para la valoración de la cultura del trabajo. El análisis pone en relación las respuestas de los/las estudiantes con datos sociodemográficos de los espacios urbanos donde se sitúan las escuelas de la muestra en dos ciudades patagónicas. Ello atendiendo que las desigualdades sociales y educativas se enlazan con la desigualdad urbana y los niveles de pobreza estructural. Las principales conclusiones son que la escuela es valorada positivamente por la mayoría del estudiantado respecto de la formación que ofrece en los aspectos indagados, y que no existen patrones que permitan vincular en forma directa las valoraciones de los/las estudiantes con los niveles de pobreza estructural de los espacios urbanos donde se ubican las escuelas secundarias.The article is about a quantitative study on perceptions that students have about the preparation they receive in public high schools. Particularly, in regard to knowledge linked to the preparation for the use of studying skills, responsible citizenship and the valuation of work culture. The analysis links students’ answers with sociodemographic data of urban spaces where the sample schools are located, in two Patagonian cities. This response to the social and educational inequalities is related to urban inequality and structural poverty levels. The main conclusions are that the school is valued positively by a majority of students on the preparation offered in the researched aspects, and there are no patterns that allow direct linking appraisals of students with the levels of structural poverty of urban spaces where high schools are located.Fil: Guzmán, Mauro Victor. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia Austral. Unidad Académica Caleta Olivia; ArgentinaFil: Venturini, María Eugenia. Universidad Nacional de la Patagonia Austral; ArgentinaFil: Almada, Maria Laura. Universidad Nacional de la Patagonia Austral; Argentin

METHODOLOGY FOR ESTIMATING BIOMASS ENERGY POTENTIAL AND ITS APPLICATION TO COLOMBIA

This paper presents a methodology to estimate the biomass energy potential and its associated uncertainty at a country level when quality and availability of data are limited. The current biomass energy potential in Colombia is assessed following the proposed methodology and results are compared to existing assessment studies. The proposed methodology is a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential. The paper also includes a proposed approach to quantify uncertainty combining a probabilistic propagation of uncertainty, a sensitivity analysis and a set of disaggregated sub-models to estimate reliability of predictions and reduce the associated uncertainty. Results predict a theoretical energy potential of 0.744 EJ and a technical potential of 0.059 EJ in 2010, which might account for 1.2% of the annual primary energy production (4.93 EJ)

Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life