GRANT AGREEMENT ID: DPI2015-67264-P

GRANTED:        € 99.900

FUNDED:

1/Jan/2016-30/Jun/2020

 

Background

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.

Objectives

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