23 research outputs found
Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models
In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time.
In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions.
The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved.
Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures.
Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities
Modelos y algoritmos para el mantenimiento predictivo en plantas solares
El uso de la tecnología solar fotovoltaica se ha incrementado en la última década debido a la caída de los costes de producción, la concienciación por el desarrollo sostenible y los incentivos facilitados por numerosos países, entre ellos España. A pesar de la citada disminución de los costes de la tecnología, la explotación de instalaciones de este tipo sigue requiriendo de algún tipo de incentivación para que el caso de negocio sea viable. De hecho, los cambios regulatorios acaecidos a este respecto han afectado seriamente la rentabilidad de parques solares fotovoltaicos ya en operación. Si el lado de los ingresos de las explotaciones se ha visto reducido por las modificaciones producidas en el ámbito regulatorio, la única variable disponible sobre la que actuar para conseguir rentabilizar las inversiones es el gasto de mantenimiento, por lo que la optimización de la gestión del mantenimiento de este tipo de instalaciones, generalmente desatendidas, es un elemento crucial para su rentabilidad y la viabilidad económica de las inversiones efectuadas. La incorporación de la gestión del mantenimiento en el esquema general de la organización se viene produciendo de forma continua desde hace cuatro décadas. Desarrollar un modelo para la gestión del mantenimiento eficiente, embebido en el sistema de información de la gestión de la organización, se ha convertido en un tópico de investigación y un aspecto fundamental para alcanzar los objetivos organizacionales. Esta tesis doctoral tiene como objetivo principal establecer un marco de trabajo para el desarrollo de algoritmos aplicables al mantenimiento predictivo de diferentes modos de fallo de los componentes que conforman un parque solar fotovoltaico, dentro del alcance de un sistema de gestión del mantenimiento. La metodología parte del análisis de series temporales asociadas a las condiciones ambientales y de operación del proceso, el estudio de los posibles modos de fallo relacionados con dichos factores y la evaluación del impacto en la degradación de la función de utilidad del sistema. Los algoritmos generados se integran en el sistema de información de la planta conforme a un marco de referencia para la gestión del mantenimiento (MGM) basado en estándares industriales y de comunicaciones, especialmente en la especificación de la gestión de activos ISO 55000:2014, en el estándar de gestión de la calidad ISO 9001:2008 y el estándar abierto de intercambio de información OpenO&M
Analysis of dynamic reliability surveillance: a case study
In this paper a reliability model based on artificial neural networks and the generalized renewal process is developed. The model is used for failure prediction, and is able to dynamically adapt to changes in the operating and environmental conditions of assets. The model is implemented for a thermal solar power plant, focusing on critical elements of these plants: heat transfer fluid pumps. We affirm that this type of model can be easily automated within the plant’s remote monitoring system. Using this model we can dynamically assign reference values for warnings and alarms and provide predictions of asset degradation. These in turn can be used to evaluate the associated economic risk to the system under existing operating conditions and to inform preventive maintenance activitie
Obtención de características y futuros campos de empleo de los diferentes aeropropulsores
Premio Juan de la Cierva 1947Tit. en antep.: "Aeropropulsores por reacción"Ante
Corolla micromorphology in 12 plant species with different pollination systems
Background: Floral microstructure, with multiple functions, is very important in pollination biology.
Questions: Are the expected general characteristics of corolla micromorphology fulfilled in the selected species with different pollination systems?
Studied species: Agave americana, Arbutus unedo, Aristolochia paucinervis, Cestrum nocturnum, Cytinus hypocistis subsp. macranthus, C. ruber, Dianthus lusitanus, Grevillea robusta, Musa x paradisiaca, Nicotiana glauca, Stellaria media and Teucrium fruticans.
Study site and dates: Southern Spain, 2017-2019.
Methods: Floral micromorphological characters were studied by light and scanning electron microscopy and analyzed using different statistical tests.
Results: The adaxial side of the corolla lobes in melittophilous, myrmecophilous, psychophilous and sapromyophilous species showed papillose cells, and the same was true of the sphingophilous species Cestrum nocturnum and hummingbird-pollinated Nicotiana glauca. In contrast, tabular cells were observed in the perching-pollinator ornithophilous species Grevillea robusta, the two studied chiropterophilous species, and autogamous Stellaria media. In addition, corolla mesophyll was thicker in chiropterophilous species. Furthermore, differences were detected in cell turgidity (in protogynous Aristolochia paucinervis) and in mesophyll thickness between male and female flowers (Cytinus).
Conclusions: Papillose cells were present in corollas in physical contact with pollinators, as expected, but also appeared in corollas of some other species without such contact. We recommend that studies of dichogamous and unisexual species include comparisons of corolla micromorphology between sexual states, as differences may exist in cell turgidity or mesophyll thickness. We also caution against the widespread view that certain types of pollinators do not mechanically interact with the epidermal surface of the corolla
A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources
The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis
Room temperature synthesis of water-dispersible Ln3+:CeF3 (Ln = Nd, Tb) nanoparticles with different morphology as bimodal probes for fluorescence and CT imaging
The singular properties of lanthanide-based inorganic nanoparticles (NPs) has raised the attention of the
scientific community in biotechnological applications. In particular, those systems with two or more functionalities
are especially interesting. In this work, an effective and commercially attractive procedure has
been developed that renders uniform, water-dispersible Ln3+:CeF3 (Ln = Tb, Nd) NPs with different shapes
and size. The method consists of the homogeneous precipitation, in a mixture of polyol and water, of cations
and anions using precursors that allow the controlled release of the latter. The advantages of the reported
method are related to the absence of surfactants, dispersing agents or corrosive precursors as well as to the
room temperature of the process. The obtained Tb:CeF3 NPs produce an intense emission after excitation
through the Ce-Tb energy transfer band located in the UV spectral region, thus being potentially useful as
phosphors for in-vitro imaging purposes. On the other hand, the synthesized Nd:CeF3 NPs are good candidates
for in-vivo imaging because their excitation and emission wavelengths lie in the biological windows.
Finally, the excellent X-ray attenuation efficacy of the Nd:CeF3NPs is shown, which confers double functionality
to this material as both luminescence bioprobe and contrast agent for X-ray computed-tomography.Peer Reviewe