“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Power Management for Energy Systems

The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

GTTC Future of Ground Testing Meta-Analysis of 20 Documents

National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team

Applied aerodynamics: Challenges and expectations

Aerospace is the leading positive contributor to this country's balance of trade, derived largely from the sale of U.S. commercial aircraft around the world. This powerfully favorable economic situation is being threatened in two ways: (1) the U.S. portion of the commercial transport market is decreasing, even though the worldwide market is projected to increase substantially; and (2) expenditures are decreasing for military aircraft, which often serve as proving grounds for advanced aircraft technology. To retain a major share of the world market for commercial aircraft and continue to provide military aircraft with unsurpassed performance, the U.S. aerospace industry faces many technological challenges. The field of applied aerodynamics is necessarily a major contributor to efforts aimed at meeting these technological challenges. A number of emerging research results that will provide new opportunities for applied aerodynamicists are discussed. Some of these have great potential for maintaining the high value of contributions from applied aerodynamics in the relatively near future. Over time, however, the value of these contributions will diminish greatly unless substantial investments continue to be made in basic and applied research efforts. The focus: to increase understanding of fluid dynamic phenomena, identify new aerodynamic concepts, and provide validated advanced technology for future aircraft

Aeronautical engineering: A special bibliography with indexes, supplement 80

This bibliography lists 277 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1977

Aeronautics research and technology program and specific objectives

Aeronautics research and technology program objectives in fluid and thermal physics, materials and structures, controls and guidance, human factors, multidisciplinary activities, computer science and applications, propulsion, rotorcraft, high speed aircraft, subsonic aircraft, and rotorcraft and high speed aircraft systems technology are addressed

Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version