Wind turbine site planning is a multidisciplinary task comprising of several stakeholder groups from different domains and with different priorities. An information system capable of integrating the knowledge on the multiple aspects of a wind turbine plays a crucial role on providing a common picture to the involved groups. In this study, we have developed an interactive and intuitive 3D system (Falcon) for planning wind turbine locations. This system supports iterative design loops (wind turbine configurations), based on the emerging field of geodesign. The integration of GIS, game engine and the analytical models has resulted in an interactive platform with real-time feedback on the multiple wind turbine aspects which performs efficiently for different use cases and different environmental settings. The implementation of tiling techniques and open standard web services support flexible and on-the-fly loading and querying of different (massive) geospatial elements from different resources. This boosts data accessibility and interoperability that are of high importance in a multidisciplinary process. The incorporation of the analytical models in Falcon makes this system independent from external tools for different environmental impacts estimations and results in a unified platform for performing different environmental analysis in every stage of the scenario design. Game engine techniques, such as collision detection, are applied in Falcon for the real-time implementation of different environmental models (e.g. noise and visibility). The interactivity and real-time performance of Falcon in any location in the whole country assist the stakeholders in the seamless exploration of various scenarios and their resulting environmental effects and provides a scope for an interwoven discussion process. The flexible architecture of the system enables the effortless application of Falcon in other countries, conditional to input data availability. The embedded open web standards in Falcon results in a smooth integration of different input data which are increasingly available online and through standardized access mechanisms.
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http://dx.doi.org/10.1016/j.jenvman.2017.09.042 | DOI Listing |
Heliyon
July 2024
Centre for Ultrasonic Engineering, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XQ, UK.
This study explores the inspection of bolted connections in wind turbines, specifically focusing on the application of Phased Array Ultrasonic Testing (PAUT). The research comprises four sections: Acoustoelastic Constant calibration, high tension investigation on bolts, blind tests on larger bolts, and Finite Element Analysis (FEA) verification. The methodology shows accurate results for stress while the bolt is under operative loads, and produces a clear indication of when it is above these loads and beginning to deform.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Health Canada, Consumer and Clinical Radiation Protection Bureau, Non-Ionizing Radiation Health Sciences Division, Ottawa, Ontario K1A 1C1, Canada.
The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data.
View Article and Find Full Text PDFSci Rep
January 2025
Electrical Power and Machines Department, Egyptian Chinese University, Cairo, Egypt.
This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine.
View Article and Find Full Text PDFHeliyon
January 2025
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059, Krakow, Poland.
In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost.
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