"The project starts with the intention of continuing and completing the National WindSeaEnergy project (DPI2012-31579), observing that there is a great lack of mathematical models that allow the analysis of the signals that are being monitored by means of Non Destructive Tests (NDT) to determine the state of the structures, as well as in the optimal management of the structures. wind turbines and wind farms. OptiWindSeaPower has continued and completed this study initiated in the field of the rotating elements of the wind turbine and their maintenance management, making a more exhaustive study."
OPTIWINDSEAPOWER
GRANT AGREEMENT ID: DPI2015-67264-P
GRANTED: € 99.900
FUNDED:
Project Schedule
1 January 2016 30 June 2020
Gestión Integral Optima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (OptiWindSeaPower)
The European Union’s energy and environmental policies are aimed at promoting and developing offshore wind platforms. This means that the Spanish electricity system will depend more and more on this type of electricity generation systems. The wind turbines for this case are larger, more complex, and require high demands for safety, reliability, availability and maintainability. In this project, this problem is addressed, with the final aim being the comprehensive and optimal management of this type of wind turbine parks.
The project starts with the intention of continuing and completing the National WindSeaEnergy project (DPI2012-31579), where more than 300 scientific references were analyzed in high impact journals, observing that there are great deficiencies in mathematical models that allow analyzing the signals that are they are monitoring to determine the state of the structures, as well as the optimal management of wind turbines and wind farms.
OptiWindSeaPower intends to continue and complete this study initiated in the field of the rotating elements of the wind turbine, and of the maintenance management thereof, making a more exhaustive study in the monitoring systems and methods of signal processing for the structural elements of said teams. The necessary data from WindSeaEnergy will be taken as a reference, as well as the European OPTIMUS, NIMO, WINPRO and the National IcingBlades projects. For this purpose, the design and development of a test bench of an ultrasound-based monitoring system will be required to determine the structural state of the wind turbines to complete this set of signals. A life cycle cost model for the predictive maintenance system will be developed. It is proposed to use mathematical models based on the analysis in time, frequency and time / frequency, as well as Transformed Wavellets, Neural Networks / Artificial Intelligence, methods based on the extraction of signal characteristics and derivatives of the System Transfer Function . The multivariate analysis will be done through Logical Decision Trees, which will be analyzed using Binary Decision Diagrams, and the important measures created by heuristic methods. This will allow controlling and optimizing the status of a wind turbine integrally. For the optimal management of the offshore wind farm, it will be considered as a Markovian decision problem, and the Restless Bandit indexes will be analyzed in order to determine the structure of the «Whittle» indices in different contexts. Finally, new indexes of significance based on costs will be created and the problem of optimization and its resolution will be formulated through metaheuristic methods to determine the optimal investment policy in the management of this type of parks.
The work team consists of members of the UCLM (6), the University College of Financial Studies of Madrid (3), and 5 of the universities of Drexel (USA), Birmingham (England), Monterrey (USA), King Fasial University (Saudi Arabia), Kyungpook National University (South Korea), with a clear multidisciplinary character, consisting of members specialized in Industrial Engineering, Economics, Statistics and Operations Research, and Energy.
Tareas de Trabajo (TT) y Actividades Requeridas (i,ii,…) | TT previas | Inicio-Final (meses) |
TT1 Estado del Arte y Objetivos |
| 1-48 |
1.i. Estado del arte | 1-2 | |
1.ii. Revisión de los objetivos principales | TT1.i | 2-2.5 |
1.iii. Actualización del estado del arte y los objetivos | TT1.(i,ii) | 2.5-48 |
TT2 Desarrollo de nuevos métodos para el procesamiento de señales |
| 2.5-20 |
2.i. Análisis de datos (WINPRO, NIMO, OPTIMUS,WindSeaEnergy) | TT1.i | 2.5-4 |
2.ii. Identificación de Nuevos Modelos Matemáticos (NMM) | TT1.i | 4-8 |
2.iii. Desarrollo de NMM | TT2.ii | 8-18 |
2.iv. Control y Actualización de NMM | TT2(ii,iii) | 14-20 |
TT3 Diseño y Construcción de un Sistema de Monitorización |
| 4-23 |
3.i. Diseño de un Sistema de Monitorización (SM) | TT(1,2.i) | 4-8 |
3.ii. Desarrollo del SM | TT3.i | 8-18 |
3.iii. Implementación de Sensores | TT3.ii | 18-21 |
3.iv. Ajuste del Sistema | TT3.iii | 21-23 |
TT4. Análisis Coste del Ciclo de Vida del SM | 23-35 | |
6.i. Estudio de Inversiones y Costes de Operación del (SM) | TT3 | 23-26 |
6.ii. Desarrollo del Modelo del Coste del Ciclo de Vida del SM | TT3.i | 26-32 |
6.iii. Análisis del Coste del Ciclo de Vida | TT3.ii | 32-35 |
TT5 Implementación de modelos avanzados en la toma de decisiones (TD) Integrando los Distintos NMM |
| 20-35 |
4.i. Diseño y Desarrollo de TD | TT2 | 20-23 |
4.ii. Análisis Cualitativo del TD | TT4.i | 23-27 |
4.iii. Conversión del TD al BDD | TT4.ii | 27-30 |
4.iv. Análisis Cuantitativo del TD | TT4.i | 30-33 |
4.v. Medidas de Importancia de los Eventos del TD | TT4.iv | 33-35 |
TT6 Gestión Óptima de los Recursos Empleados en el Mantenimiento de los Parques Eólicos Offshore |
| 35-46 |
5.i. Formulación del Problema | TT5 | 35-37 |
5.ii. Desarrollos de la Estructura de Costes, Probabilidades de Transición, Conjunto de Acciones, etc. | TT4.i | 35-38 |
5.iii. Optimización de la Planificación de las Políticas de Mantenimiento y los Recursos Empleados en Mantenimiento | TT6.ii | 38-40 |
5.iv. Análisis de los Indicadores de Transcendencia | TT6.iii | 40-42 |
TT7. Optimización de inversiones basado en TT4, TT5 y TT6 | 35-46 | |
7.i. Determinación de las Inversiones de TD de TT5 | TT5 | 35-38 |
7.ii. Creación de nuevos índices de significación basados en costes | TT5 | 35-39 |
7.iii. Diseño y Elaboración de Modelo de Optimización de Inversiones | TT7.ii | 39-43 |
7.iv. Análisis de los índices de significancia basados en costes | TT7.iii | 43-46 |
TT8. Dirección, Difusión de Resultados y Trabajos Futuros |
| 1-48 |
8.i. Dirección de Proyecto | Todas TT | 1-48 |
8.ii. Estudiante(s) de Doctorado | Todas TT | 1-48 |
8.iii. Difusión de Resultados | Todas TT | 20-48 |
8.iv. Determinación y Preparación de Futuros Trabajos | Todas TT | 44-48 |
2021
(2021) Alarms management by supervisory control and data acquisition system for wind turbines, EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 23(1), p. 110-116, pdf, doi:http://dx.doi.org/10.17531/ ein.2021.1.12.
2020
(2020) Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm, Energies 13(13), url, doi:10.3390/en13133500
(2020) Detection of structural defects in wind turbine blades employing guided waves and machine learning methods, Developments in Renewable Energies Offshore, p. 509-514, CRC Press, url
(2020) Smart Farming: Intelligent Management Approach for Crop Inspection and Evaluation Employing Unmanned Aerial Vehicles, International Conference on Management Science and Engineering Management, p. 119-130, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-030-49889-4_11
(2020) Non-destructive methods for detection and localisation of partial discharges, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, p. 177-193, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00012-5
(2020) Predictive Analysis of Robotic Manipulators Through Inertial Sensors and Pattern Recognition, Handbook of Research on Big Data Clustering and Machine Learning, p. 334-344, IGI-Global, url, doi:DOI: 10.4018 / 978-1-7998-0106-1.ch015
(2020) Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach, Energies 13(11), p. 2840, url, doi:https://doi.org/10.3390/en13112840
(2020) Reliability Dynamic Analysis by Fault Trees and Binary Decision Diagrams, MDPI 11(6), p. 324, url, doi:https://doi.org/10.3390/info11060324
(2020) Chapter 6 – Non-destructive testing of wind turbines using ultrasonic waves, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 91-101, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00006-X
(2020) Chapter 4 – Non-destructive testing for the evaluation of icing blades in wind turbines, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 49-68, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00004-6
(2020) Chapter 1 – Wind turbines: A general reliability analysis, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 1-18, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00001-0
(2020) Chapter 8 – Remotely operated vehicle applications, Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets, Mayorkinos Papaelias, Fausto Pedro García Márquez, Alexander Karyotakis (ed.), p. 119-132, Boston: Butterworth-Heinemann, url, doi:https://doi.org/10.1016/B978-0-08-101094-5.00008-3
(2020) Maintenance management based on Machine Learning and nonlinear features in wind turbines, Renewable Energy 146, p. 316-328, Pergamon, url, doi:10.1016/J.RENENE.2019.06.135
2019
(2019) OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (2ª parte), Asociación Española de Ensayos No Destructivos 89, p. 34-48, url
(2019) Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing, Energies 12(9), pdf, doi:10.3390/en12091753
(2019) Maintenance Management in Wind Turbines by Monitoring the Bearing Temperature, International Conference on Management Science and Engineering Management, Cham Springer (ed.), p. 678-687, Toronto: Springer, url, doi:DOI https://doi.org/10.1007/978-3-030-21248-3
(2019) Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines, Reliability Engineering & System Safety 191, p. 106574, url, doi:https://doi.org/10.1016/j.ress.2019.106574
(2019) Advanced analytics for detection and diagnosis of false alarms and faults: A real case study, Wind Energy 0(0), John Wiley & Sons, Ltd, url, doi:10.1002/we.2393
(2019) Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers, Reliability Engineering & System Safety 184, p. 2-12, url, doi:https://doi.org/10.1016/j.ress.2018.02.013
(2019) Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis, Renewable Energy 132, p. 1034-1048, Pergamon, url, doi:https://doi.org/10.1016/j.renene.2018.08.050
2018
(2018) Big Data and Wind Turbines Maintenance Management, Renewable Energies, p. 111-125, Springer, url, doi:https://doi.org/10.1007/978-3-319-45364-4_8
(2018) Acoustic Maintenance Management Employing Unmanned Aerial Vehicles in Renewable Energies, International Conference on Management Science and Engineering Management, p. 969-981, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-93351-1_76
(2018) Future Maintenance Management in Renewable Energies, Renewable Energies, p. 149-159, Springer, url, doi:https://doi.org/10.1007/978-3-319-45364-4_10
(2018) Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers, Reliability Engineering & System Safety, Elsevier, url, doi:https://doi.org/10.1016/j.ress.2018.02.013
(2018) Remotely Piloted Aircraft System and Engineering Managenment: A Real Case Study, International Conference on Management Science and Engineering Management, p. 1173-1185, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-93351-1_920
(2018) Emisiones acústicas y procesamiento de señales para la localización de defectos en materiales compuestos, url
(2018) Wind Energy Power Prospective, Renewable Energies, p. 83-95, Springer, url, doi:https://doi.org/10.1007/978-3-319-45364-4_6
(2018) A survey of artificial neural network in wind energy systems, Applied Energy, url, doi:10.1016/j.apenergy.2018.07.084
(2018) Managing Costs and Review for Icing Problems, Renewable Energies, p. 97-109, Springer, doi:https://doi.org/10.1007/978-3-319-45364-4_7
(2018) Machine Learning for Wind Turbine Blades Maintenance Management, Energies 11(1), p. 13, Multidisciplinary Digital Publishing Institute, url, doi:https://doi.org/10.3390/en11010013
(2018) Concentrated Solar Power: Present and Future, Renewable Energies, p. 51-61, Springer, url, doi:https://doi.org/10.1007/978-3-319-45364-4_4
(2018) Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis, Renewable Energy 116(B), p. 42-54, Elsevier, url, doi:http://dx.doi.org/10.1016/j.renene.2017.03.052
2017
(2017) An Approach for Dynamic Analysis of Electrical/Electronic Components in Wind Turbines, Energies 10(8), url
(2017) Fuzzy Logic Applied to SCADA Systems, Springer, url, doi:https://doi.org/10.1007/978-3-319-59280-0_61
(2017) New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves, Acta Acustica united with Acustica, p. 772-781, url, doi:https://doi.org/10.3813/AAA.919106
(2017) Optimal dynamic analysis of electrical/electronic components in wind turbines, Energies 10(8), p. 1111, Multidisciplinary Digital Publishing Institute, url, doi:doi:10.3390/en10081111
(2017) SCADA and Artificial neural networks for maintenance management, url, doi:https://doi.org/10.1007/978-3-319-59280-0_75
(2017) Cracks and Welds Detection Approach in Solar Receiver Tubes Employing EMATs, Structural Health Monitoring, url, doi:https://doi.org/10.1177/1475921717734501
(2017) SCADA and Artificial Neural Networks for Maintenance Management, International Conference on Management Science and Engineering Management, p. 912-919, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-59280-0_75
(2017) New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves, url, doi:https://doi.org/10.3813/AAA.919106
(2017) Fuzzy Logic Applied to SCADA Systems, International Conference on Management Science and Engineering Management, p. 749-757, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-59280-0_61
(2017) Emisiones Acústicas y Procesamiento de Señales para la Localización de Defectos en Materiales Compuestos, url
(2017) Cracks and weld detection approach in solar receiver tubes employing EMATS, url, doi:https://doi.org/10.1177/1475921717734501
(2017) Optimal decision-making via binary decision diagrams for investments under a risky environment, International Journal of Production Research 55(18), p. 5271-5286, Taylor & Francis, url, doi:https://doi.org/10.1080/00207543.2017.1308570
(2017) Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers, Structural Health Monitoring, p. 1475921717734501, SAGE Publications Sage UK: London, England, url, doi:https://doi.org/10.1177/1475921717734501
(2017) Machine Learning and Neural Network for Maintenance Management, International Conference on Management Science and Engineering Management, p. 1377-1388, Springer, Cham, url, doi:https://doi.org/10.1007/978-3-319-59280-0_115
2016
(2016) Calculus of the defect severity with EMATs by analyzing the attenuation curves of the guided waves, url, doi:https://doi.org/10.12989/sss.2017.19.2.195
2013
(2013) Wind turbine reliability analysis, Renewable and Sustainable Energy Reviews 23, p. 463-472, Pergamon, url, doi:http://dx.doi.org/10.1016/j.rser.2013.03.018
(2013) Railroad inspection based on ACFM employing a non-uniform B-spline approach, Mechanical Systems and Signal Processing 40(2), p. 605-617, Academic Press, url, doi:doi: 10.1016/j.ymssp.2013.05.004
2003
(2003) A reliability centered approach to remote condition monitoring. A railway points case study, Reliability Engineering & System Safety 80(1), p. 33-40, Elsevier, doi:https://doi.org/10.1016/S0951-8320(02)00166-7
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