A test bench was carried out to monitor the rolling elements available in a wind turbine in WinSeaEnergy project (Ref.: DPI2012-31579, 2012-2015). The results obtained in the laboratory could be contrasted by the European Project NIMO (Ref. FP7-ENERGY-2008-TREN1:239462, 2010-2014). OptiWindSeaPower (Ref.: DPI2015-67264-P, 2016-2019) continued the achievements of NIMO and WindSeaEnergy, focused on the monitoring systems of the structural components, doing a detailed study of the dynamic and structural systems. The results were validated through the IcingBlades (IPT-2012-0563-120000) and OPTIMUS (FP7-ENERGY-2012-2-322430) projects. The monitoring systems were analysed their life cycle cost, comparing with other alternatives and based on a set of scenarios.

WindSound proposes, for the first time, the use of acoustic sensors in drones for CMS of WTs. The information will be analysed with the data from the SCADA and CMS of the offshore WTs to analyse the alarms, i.e., it will replace human resources to access to the WTs to verify the alarms in favour of their safety. This information will be finally studied with endogenous (resources, budgets for maintenance tasks) and exogenous variables (electricity price, weather conditions, legal issues). WindSound presents a new holistic multidisciplinary approach to this paradigm in favour of the competitiveness and reliability of this industry that has not been previously studied.

The most relevant scientific and technical outcomes from WindSound are:1.- Optimization of maintenance elements and components of a production system, with important improvements in the availability; 2.- Significant improvements in the safety of Human Resources due to an adequate control of assets at the optimal maintenance policy / policies that have been selected through advanced decision tools; 3.- Improving the service quality provided to customers, due to the influences that increases availability may have on the operating time; 4.- Reduction in maintenance costs generated by better maintenance polices; 5.- Minimize of the costs of corrective maintenance activities caused by errors in the application of certain warning and alarm values, and lower costs of preventive maintenance; 6.- Development of a Smart Prognostic Maintenance System, easy to use by the managers and operators of maintenance. This is intended to employ complex mathematical models into the smart maintenance systems, but the results may be a practical system in its application in any organization of the sector; 7.- Determination of optimal management policies in the maintenance schedules offshore wind farms.

2020

Alfredo Peinado Gonzalo, Alberto Pliego Marugan, Fausto Pedro Garcia Marquez (2020) Survey of maintenance management for photovoltaic power systems, Renewable and Sustainable Energy Reviews 134, p. 110347, url, doi:10.1016/j.rser.2020.110347