Wind energy, with an exponential growth in the last years, is nowadays one of the most important renewable energy sources. Modern wind turbines are bigger and complex to produce more energy. This industry requires to reduce its operating and maintenance costs and to increase its reliability, safety, maintainability and availability. Condition monitoring systems are beginning to be employed for this purpose. They must be reliable and cost-effective to reduce the long periods of downtimes and high maintenance costs, and to avoid catastrophic scenarios caused by undetected failures. This paper presents a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades. In addition, it analyses the future trends and challenges of structural health monitoring systems in wind turbine blades.
Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks.
Wind turbines are complex systems that use advanced condition monitoring systems for analyzing their health status. The gearbox is one of the most critical components due to its elevated downtime and failure rate. Supervisory Control and Data Acquisition systems are employed in wind farms for condition monitoring and control in real time. The volume and variety of the data require novel and robust techniques for data analysis. The main novelty of this work is the development of a new modelling of the temperature curve of the gearbox bearing versus wind speed to detect false alarms. An approach based on data partitioning and data mining centers is employed. The wind speed range is divided into intervals to increase the accuracy of the model, where the centers are considered representative samples in the modelling. A method based on the alarm detection is developed and studied together with the alarms report provided by a real case study. The results obtained allow the identification of critical alarm periods outside the confidence interval. It is validated that the study of alarm identification, pre-filtered data, state variable, and output power contribute to the detection of the false alarms.
Wind turbines are increasing in number, size and market share. It is determined whether they are efficient through operating and maintenance costs. Therefore, one of the main objectives of the wind turbines is to increase the service life of the components by applying different methodologies for fault detection. The gearbox is a critical component since it causes the most downtime and failure rate of the wind turbines. The Supervisory Control and Data Acquisition system offers the measurement of several variables, and by a correct analysis it is possible to detect the faults before they occur. This paper analyses the temperature curve of bearing versus wind speed as significant variables of a gearbox failure for fault detection.
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