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Inspection of wind turbine bolted connections using the ultrasonic phased array system.
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 PDFAn analysis of self-reported sleep disturbance from nighttime wind turbine noise suggests minimal effects but highlights the need for standardization in research designa).
J 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 PDFComparative analysis of PI and fuzzy logic controller for grid connected wind turbine under normal and fault conditions.
Sci 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 PDFA two-stage deep learning-based hybrid model for daily wind speed forecasting.
Heliyon
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 PDFImproved Intelligent Condition Monitoring with Diagnostic Indicator Selection.
Sensors (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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