Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essen- tial role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving mean- ing to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available

Priorities for synthesis research in ecology and environmental science

Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science

SCADA and Artificial neural networks for maintenance management

Nowadays, the reliability of the wind turbines is essential to ensure the efficiency and the benefits of the wind energy. The SCADA system installed in a wind turbine generates lot of data that need to be processed. The information obtained from these data can be used for improving the operation and management, obtaining more reliable systems. The SCADA systems operate through different control rules that are predefined. However, a static control of the wind turbine can generate a miscorrelation between the control and the real conditions of the wind turbine. For example, two wind turbines can be separated several kilometers in the same wind farm, therefore, the operation conditions must be different and the control strategy should not be unique. This research work presents a method based on neural networks for a dynamic generation of the control strategy. The method suggests that the thresholds used for generating alarms can vary and, therefore, the control of the wind turbine will be adapted to each specific wind turbine