Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

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

ISG15 Is Upregulated in Respiratory Syncytial Virus Infection and Reduces Virus Growth through Protein ISGylation

UNLABELLED: Human respiratory syncytial virus (RSV), for which neither a vaccine nor an effective therapeutic treatment is currently available, is the leading cause of severe lower respiratory tract infections in children. Interferon-stimulated gene 15 (ISG15) is a ubiquitin-like protein that is highly increased during viral infections and has been reported to have an antiviral or a proviral activity, depending on the virus. Previous studies from our laboratory demonstrated strong ISG15 upregulation during RSV infection in vitro. In this study, an in-depth analysis of the role of ISG15 in RSV infection is presented. ISG15 overexpression and small interfering RNA (siRNA)-silencing experiments, along with ISG15 knockout (ISG15(-/-)) cells, revealed an anti-RSV effect of the molecule. Conjugation inhibition assays demonstrated that ISG15 exerts its antiviral activity via protein ISGylation. This antiviral activity requires high levels of ISG15 to be present in the cells before RSV infection. Finally, ISG15 is also upregulated in human respiratory pseudostratified epithelia and in nasopharyngeal washes from infants infected with RSV, pointing to a possible antiviral role of the molecule in vivo. These results advance our understanding of the innate immune response elicited by RSV and open new possibilities to control infections by the virus. IMPORTANCE: At present, no vaccine or effective treatment for human respiratory syncytial virus (RSV) is available. This study shows that interferon-stimulated gene 15 (ISG15) lowers RSV growth through protein ISGylation. In addition, ISG15 accumulation highly correlates with the RSV load in nasopharyngeal washes from children, indicating that ISG15 may also have an antiviral role in vivo. These results improve our understanding of the innate immune response to RSV and identify ISG15 as a potential target for virus control.This work was supported by grant PI11/00590 from Fondo de Investigación Sanitaria to I.M.S

Prognostic value of discharge heart rate in acute heart failure patients: more relevant in atrial fibrillation?

[Abstract] Aims. The prognostic impact of heart rate (HR) in acute heart failure (AHF) patients is not well known especially in atrial fibrillation (AF) patients. The aim of the study was to evaluate the impact of admission HR, discharge HR, HR difference (admission-discharge) in AHF patients with sinus rhythm (SR) or AF on long- term outcomes. Methods. We included 1398 patients consecutively admitted with AHF between October 2013 and December 2014 from a national multicentre, prospective registry. Logistic regression models were used to estimate the association between admission HR, discharge HR and HR difference and one- year all-cause mortality and HF readmission. Results. The mean age of the study population was 72 ± 12 years. Of these, 594 (42.4%) were female, 655 (77.8%) were hypertensive and 655 (46.8%) had diabetes. Among all included patients, 745 (53.2%) had sinus rhythm and 653 (46.7%) had atrial fibrillation. Only discharge HR was associated with one year all-cause mortality (Relative risk (RR) = 1.182, confidence interval (CI) 95% 1.024–1.366, p = 0.022) in SR. In AF patients discharge HR was associated with one year all cause mortality (RR = 1.276, CI 95% 1.115–1.459, p ≤ 0.001). We did not observe a prognostic effect of admission HR or HRD on long-term outcomes in both groups. This relationship is not dependent on left ventricular ejection fraction. Conclusions. In AHF patients lower discharge HR, neither the admission nor the difference, is associated with better long-term outcomes especially in AF patients

Risk factors associated with adverse fetal outcomes in pregnancies affected by Coronavirus disease 2019 (COVID-19): a secondary analysis of the WAPM study on COVID-19.

Objectives To evaluate the strength of association between maternal and pregnancy characteristics and the risk of adverse perinatal outcomes in pregnancies with laboratory confirmed COVID-19. Methods Secondary analysis of a multinational, cohort study on all consecutive pregnant women with laboratory-confirmed COVID-19 from February 1, 2020 to April 30, 2020 from 73 centers from 22 different countries. A confirmed case of COVID-19 was defined as a positive result on real-time reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens. The primary outcome was a composite adverse fetal outcome, defined as the presence of either abortion (pregnancy loss before 22 weeks of gestations), stillbirth (intrauterine fetal death after 22 weeks of gestation), neonatal death (death of a live-born infant within the first 28 days of life), and perinatal death (either stillbirth or neonatal death). Logistic regression analysis was performed to evaluate parameters independently associated with the primary outcome. Logistic regression was reported as odds ratio (OR) with 95% confidence interval (CI). Results Mean gestational age at diagnosis was 30.6+/-9.5 weeks, with 8.0% of women being diagnosed in the first, 22.2% in the second and 69.8% in the third trimester of pregnancy. There were six miscarriage (2.3%), six intrauterine device (IUD) (2.3) and 5 (2.0%) neonatal deaths, with an overall rate of perinatal death of 4.2% (11/265), thus resulting into 17 cases experiencing and 226 not experiencing composite adverse fetal outcome. Neither stillbirths nor neonatal deaths had congenital anomalies found at antenatal or postnatal evaluation. Furthermore, none of the cases experiencing IUD had signs of impending demise at arterial or venous Doppler. Neonatal deaths were all considered as prematurity-related adverse events. Of the 250 live-born neonates, one (0.4%) was found positive at RT-PCR pharyngeal swabs performed after delivery. The mother was tested positive during the third trimester of pregnancy. The newborn was asymptomatic and had negative RT-PCR test after 14 days of life. At logistic regression analysis, gestational age at diagnosis (OR: 0.85, 95% CI 0.8-0.9 per week increase; pPeer reviewe

Maternal and perinatal outcomes of pregnant women with SARS-CoV-2 infection

Objectives To evaluate the maternal and perinatal outcomes of pregnancies affected by SARS-CoV-2 infection. Methods This was a multinational retrospective cohort study including women with a singleton pregnancy and laboratory-confirmed SARS-CoV-2 infection, conducted in 72 centers in 22 different countries in Europe, the USA, South America, Asia and Australia, between 1 February 2020 and 30 April 2020. Confirmed SARS-CoV-2 infection was defined as a positive result on real-time reverse-transcription polymerase chain reaction (RT-PCR) assay of nasopharyngeal swab specimens. The primary outcome was a composite measure of maternal mortality and morbidity, including admission to the intensive care unit (ICU), use of mechanical ventilation and death. Results In total, 388 women with a singleton pregnancy tested positive for SARS-CoV-2 on RT-PCR of a nasopharyngeal swab and were included in the study. Composite adverse maternal outcome was observed in 47/388 (12.1%) women; 43 (11.1%) women were admitted to the ICU, 36 (9.3%) required mechanical ventilation and three (0.8%) died. Of the 388 women included in the study, 122 (31.4%) were still pregnant at the time of data analysis. Among the other 266 women, six (19.4% of the 31 women with first-trimester infection) had miscarriage, three (1.1%) had termination of pregnancy, six (2.3%) had stillbirth and 251 (94.4%) delivered a liveborn infant. The rate of preterm birth before 37 weeks' gestation was 26.3% (70/266). Of the 251 liveborn infants, 69/251(27.5%) were admitted to the neonatal ICU, and there were five (2.0%) neonatal deaths. The overall rate of perinatal death was 4.1% (11/266). Only one (1/251, 0.4%) infant, born to a mother who tested positive during the third trimester, was found to be positive for SARS-CoV-2 on RT-PCR. Conclusions SARS-CoV-2 infection in pregnant women is associated with a 0.8% rate of maternal mortality, but an 11.1% rate of admission to the ICU. The risk of vertical transmission seems to be negligible. (C) 2020 International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

Mejora de la Eficiencia Energética de una Planta Fotovoltaica con la Detección Temprana de Fallos Mediante el Uso de Modelos Predictivos de Redes Neuronales Artificiales

Un importante número de empresas españolas han decidido invertir en los últimos años en instalaciones de generación de energías renovables, debido fundamentalmente a la rentabilidad de dichas instalaciones. Dicha rentabilidad ha sido motivada por incentivos que el Gobierno español ha promocionado a través de decretos (R.D. 661/2007 de 25 de Mayo) que primaban considerablemente la generación de “energías limpias”. En concreto en Septiembre del 2008 comenzaron sus puestas en marcha numerosas Instalaciones Fotovoltaicas cuya energía eléctrica generada vertían a la red eléctrica española. Estas instalaciones se construían en los denominados Parques Fotovoltaicos o Solares los cuales se agrupaban en instalaciones de 100 Kw, que denominaremos en adelante Huertos Solares. La inversión realizada por cada uno de estos Huertos Solares suponía una media de 660.000 € (dato que utilizaremos para todo el proyecto aunque dicha cantidad lógicamente puede variar dependiendo de la tecnología adoptada), por lo que en general cualquier instalación de este tipo por pequeña que sea, supone una considerable inversión. El modelo de negocio inicial para estas instalaciones tenía previsto un periodo de 12 años para amortizar la inversión con una vida media de 25 años, lo que suponía una rentabilidad aproximada del 15%. A partir de dicha fecha podrían seguir produciendo los Parques Fotovoltaicos vendiendo la energía a precio de mercado sin incentivo alguno. Sin embargo, en este trabajo no nos vamos a centrar en el modelo de negocio, sino en cómo mejorar la producción lo que hará que lógicamente mejore la rentabilidad. El largo periodo de vida de estas instalaciones, junto con posibles modificaciones en los incentivos regulados por Decreto (de hecho ya ha ocurrido) hace necesario tener modelos predictivos de producción adaptables en cada momento que nos permitan rehacer el modelo de negocio para conocer con exactitud (o al menos del modo más aproximado posible) el resultado de la importante inversión. Conocido el modelo ideal de predicción de producción de la Instalación Fotovoltaica, éste nos permitiría por una parte rehacer el modelo de negocio y por otra comparar con la producción real, pero no nos aportaría más que una predicción que al fin y al cabo no modificaría la producción real, la cual es realmente lo que al propietario o inversor le interesaría poder mejorar. Es por ello que en este trabajo pretendemos dar un paso más adelante como aportación al conocimiento, obteniendo no sólo el modelo de predicción ideal, sino utilizándolo para mejorar la producción real de la instalación. Dada la gran inversión de un Parque Solar cualquier mejora en la producción mediante el modelo supondría una importante ganancia en euros. Para la obtención del modelo ideal de producción existen diversas técnicas matemáticas, como el clásico modelo de regresión matemática que nos permitiría obtener resultados con intervalos de confianza y parámetros totalmente definidos para el modelo. Sin embargo, el patrón de comportamiento de producción no sigue relación directa o indirecta con el conjunto de variables principales que influyen directamente en la producción, de ahí que pudiendo ser el mejor método, en el caso particular no se adapta y no aporta una solución fiable. Es por ello que revisada la bibliografía el modelo seleccionado para el estudio por considerar que se adapta muy bien al patrón de comportamiento es el de las Redes Neuronales Artificiales. Obtenido el patrón de comportamiento ideal de producción del Parque Solar, a través del entrenamiento de una red Neuronal con datos históricos de producción horaria a lo largo de un año (filtrados en ausencia de fallo, de modo que el patrón sea el ideal) obtendremos una comparativa con la producción real en cada momento. Una vez realizada esta comparación, en el presente trabajo nos centraremos en los posibles fallos del Parque Solar y en concreto en aquellos fallos que afecten al menos a un Huerto Solar completo. De los fallos seleccionados volveremos a centrarnos en aquellos fallos que nos permitan obtener la caída o disminución en la producción con suficiente antelación a la detección del fallo por el Sistema. Con un posterior análisis de criticidad y un sistema que defina la condición de alarma podremos implementar en el Scada un sistema de alerta temprana que permita avisar al personal de mantenimiento de la planta con objeto de reparar la avería o fallo, y evitar por tanto pérdidas innecesarias o visto de otro modo mejorar la producción o beneficio del Parque Solar. Este Trabajo Fin de Master, desarrollado con el grupo de Sistemas Inteligentes de Mantenimiento de la ETSI de Sevilla, se enmarca dentro del proyecto de investigación del citado grupo de título SMARTSOLAR, del programa OPN – INNPACTO (Ref IPT-2011-1282-920000), del Ministerio de Ciencia e Innovación

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

Prognostic value of discharge heart rate in acute heart failure patients : More relevant in atrial fibrillation?

The prognostic impact of heart rate (HR) in acute heart failure (AHF) patients is not well known especially in atrial fibrillation (AF) patients. The aim of the study was to evaluate the impact of admission HR, discharge HR, HR difference (admission-discharge) in AHF patients with sinus rhythm (SR) or AF on long- term outcomes. We included 1398 patients consecutively admitted with AHF between October 2013 and December 2014 from a national multicentre, prospective registry. Logistic regression models were used to estimate the association between admission HR, discharge HR and HR difference and one- year all-cause mortality and HF readmission. The mean age of the study population was 72 ± 12 years. Of these, 594 (42.4%) were female, 655 (77.8%) were hypertensive and 655 (46.8%) had diabetes. Among all included patients, 745 (53.2%) had sinus rhythm and 653 (46.7%) had atrial fibrillation. Only discharge HR was associated with one year all-cause mortality (Relative risk (RR) = 1.182, confidence interval (CI) 95% 1.024-1.366, p = 0.022) in SR. In AF patients discharge HR was associated with one year all cause mortality (RR = 1.276, CI 95% 1.115-1.459, p ≤ 0.001). We did not observe a prognostic effect of admission HR or HRD on long-term outcomes in both groups. This relationship is not dependent on left ventricular ejection fraction. In AHF patients lower discharge HR, neither the admission nor the difference, is associated with better long-term outcomes especially in AF patients