Identification of critical components of wind turbines using FTA over the time" Renewable Energy: Optimization Methods in Renewable Energy Systems Design.
Abstract:Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.
The size and the complexity of photovoltaic solar power plants are increasing, and it requires an advanced and robust condition monitoring systems for ensuring their reliability. This paper proposes a novel method for faults detection in photovoltaic panels employing a thermographic camera embedded in an unmanned aerial vehicle. The large amount of data generated by these systems must be processed and analyzed. This paper presents a novel approach to identify panels to detect hot spots, and to set their locations. Two novels region-based convolutional neural networks are unified to generate a robust detection structure. The main contribution is the combination of thermography and telemetry data to provide a response of the panel condition monitoring. The data are acquired and then automatically processed, allowing fault detection during the inspection. A detailed description of the methodology is presented, including the different stages to build the neural networks, i.e. the training process, the acquisition and processing of data and the outcomes generation. A thermographic inspection of a real photovoltaic solar plant is done to validate the proposed methodology. The accuracy, the efficiency and the performance of the approach under different real scenarios are evaluated statistically obtaining satisfactory results.
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