EE- Enerji Bilim ve Teknoloji Lisansüstü Programı - Doktora
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Yazar "Barutçu, Burak" ile EE- Enerji Bilim ve Teknoloji Lisansüstü Programı - Doktora'a göz atma
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ÖgeIncipient Fault Detection in Wind Turbines(Energy Institute, 2019-06-21) Taşkıner, Ayşe Gökçen Kavaz ; Barutçu, Burak ; 10306883 ; Energy Sciences and Technologies ; Enerji Bilim ve TeknolojiThe global goal of increasing the share of renewable energy supplies in the overall energy consumption has resulted in a rising focus on technological developments in this field. Wind energy is one of the promising options amongst renewable energy sources with a growing number of investments and rising installation number and capacities. Due to the increasing demands from wind energy industry, the requirement of more effective wind farm operations has emerged. Wind turbine maintenance systems are essential parts towards achieving this requirement. Today, maintenance of wind turbines is mostly based on preventive and corrective actions. However, these approaches are inadequate to meet current demands from wind energy industry. With the developments in computational capabilities and data collection systems, a high potential of using advanced data-driven techniques has appeared for the maintenance of wind turbines. This thesis proposes a predictive maintenance approach using data which were collected from a wind turbine Supervisory Control and Data Acquisition System (SCADA). SCADA is the primary interface between the wind farm operators and wind turbines which allows remote and local control and monitoring. Various kinds of data are collected by SCADA systems such as wind parameters, temperature values, operational and status data. It is a built-in part in most medium and large-scale modern wind turbines. Therefore, a major advantage of using SCADA data for fault detection purposes is that additional hardware costs are not required. However, there are imperfections in the data such as low sampling frequency and high ratio of missing values. To handle these disadvantages, a suitable approach is required which was provided by Artificial Neural Networks (ANN) in this thesis. Moreover, wind turbines are highly non-linear systems with complex control parts and ANN models are also powerful on handling such complex systems. By this way, this thesis aims to design a cost-effective maintenance system for the overall wind turbine. Firstly, a sensor validation technique to detect faults of temperature sensors was designed. The method solely uses sensor measurements to detect calibration drifts by analyzing a set of sensors located on components with similar temperature characteristics. Auto-Associative and Multi-Input-Single-Output ANN structures were employed. The concurrent use of them provided the best outputs on the detection of the simulated calibration drift. The results prove that, validation of sensors can be realized by continuously monitoring sensor readings. It is advantageous as there is no need of dismantling sensors to test their calibration. Also, this method is a cost-effective solution in terms of not requiring redundant sensor use. After the sensor validation part, a 3-level fault classification system to detect, isolate and predict wind turbine faults was realized. The types of faults attempted in this part are frequent and non-fatal wind turbine faults. Distinguishing these kind faults is a challenging task because they do not show as strong indications as fatal faults do. However, as they are observed frequently in all wind turbines and decrease turbine performance, detection of them is a significant research topic. The core part of algorithms employed in this part is ANN models, in addition to them assistive methods were also designed to increase the fault classification performance. For the initial step of this part, feature construction and selection techniques were employed to find out an effective subset of inputs to be used as inputs of ANN models. These pre-processing tasks are important to design fast and accurate models as performance of algorithms strongly depend on the feature representation of input data in artificial intelligence applications. Raw data collected by the SCADA system were used to generate new features that possibly give more information about the hidden relations indicating fault occurences comparing to the raw features. In the feature selection step, both raw and constructed features were analyzed to identify a subset of relevant features to reduce computational burden and increase accuracy of models. Two different feature selection methods were used in a hybrid way, which are filter and wrapper-based methods. The results show that, the feature construction and selection algorithms designed are useful especially in terms of reducing false fault alarms which is an important issue in fault detection systems built using SCADA data. Finally, a 3-level classification scheme for wind turbine faults was designed using ANN models. By this way, a complete system was formed that provides required information by wind farm operators to take actions or measures in case of a current or an upcoming fault. In the detection level, the status of the turbine was analyzed to find out if the turbine is in a normal or a faulty mode. In the fault isolation level, the specific subsystem subjected to fault was attempted to be found. Therefore, this level includes distinguishing detected faults from each other. Finally, in the fault prediction level it was aimed to predict faults in advance to inform operators for possible prevention or repairing actions. We have obtained comprehensive results proving that the proposed methods are effective in all levels of fault classification. Our findings support the idea that despite the shortcomings of SCADA data, ANN models used with assistive methods are powerful on the classification of wind turbine faults. As a result, this thesis contributes to efforts of designing a cost-effective predictive maintenance approach for wind turbines.