As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

Failures in the main bearings of wind turbines are critical in terms of downtime and replacement cost. Early diagnosis of their faults would lower the levelized cost of wind energy. Thus, this work discusses a gated recurrent unit (GRU) neural network, which detects faults in the main bearing some months ahead (when the event that initiates/develops the failure releases heat) the actual fatal fault materializes. GRUs feature internal gates that govern information flow and are utilized in this study for their capacity to understand whether data in a time series is crucial enough to preserve or forget. It is noteworthy that the proposed methodology only requires healthy Supervisory Control and Data Acquisition (SCADA) data. Thus, it can be deployed to old wind parks (nearing the end of their lifespan) where specific high frequency condition monitoring sensors are not installed and to new wind parks where faulty historical data do not exist yet. The strategy is trained, validated, and finally tested using SCADA data from an in-production wind park composed of nine wind turbines.

The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.

A polymer electrolyte fuel cell (PEFC) is an electrochemical device that converts chemical energy into electrical energy and heat. The energy conversion is simple; however, the multiphysics phenomena involved in the energy conversion process must be analyzed in detail. The gas diffusion layer (GDL) provides a diffusion media for reactant gases and gives mechanical support to the fuel cell. It is a complex medium whose properties impact the fuel cell’s efficiency. Therefore, an in-depth analysis is required to improve its mechanical and physical properties. In the current study, several transport phenomena through three-dimensional digitally created GDLs have been analyzed. Once the porous microstructure is generated and the transport phenomena are mimicked, transport parameters related to the fluid flow and mass diffusion are computed. The GDLs are approximated to the carbon paper represented as a grouped package of carbon fibers. Several correlations, based on the fiber diameter, to predict their transport properties are proposed. The digitally created GDLs and the transport phenomena have been modeled using the open-source library named Open Pore Network Modeling (OpenPNM). The proposed correlations show a good fit with the obtained data with an R-square of approximately 0.98.

Structural health monitoring (SHM) systems are designed to continually monitor the health of structures (e.g., civil, aeronautic) by using the information collected through a distributed sensor network. However, performing tests on real structures, such as wind turbines, implies high logistic and operational costs. Therefore, there is a need for a vibration test system to evaluate designs at smaller scales in a laboratory setting in order to collect data and devise predictive maintenance strategies. In this work, the proposed vibration test system is based on a lab-scale wind turbine jacket foundation related primarily to an offshore environment. The test system comprises a scaled wave generator channel, a desktop application (WTtest) to control the channel simulations, and a data acquisition system (DAQ) to collect the information from the sensors connected to the structure. Various equipment such as accelerometers, electrodynamic shaker, and DAQ device are selected as per the design methodology. Regarding the mechanical part, each component of the channel is designed to be like the wave absorber, the mechanical multiplier, the piston-type wavemaker, and the wave generator channel. For this purpose, the finite element method is used in static and fatigue analysis to evaluate the stresses and deformations; this helps determine whether the system will work safely. Moreover, the vibration test system applies to other jacket structures as well, giving it greater utility and applicability in different research fields. In sum, the proposed system is compact and has three well-defined components that work synchronously to develop the experimental simulations.