Abstract Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading-edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5-m blade sectio ..read more

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Abstract The importance of resolving stability concerns in weak AC grid-connected doubly fed induction generator (DFIG) wind energy systems during low-voltage ride-through (LVRT) events cannot be ignored, given the increasing popularity of wind power-based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and ..read more

Abstract This paper presents a lifting-line implementation in the framework of a Lagrangian vortex particle method (LL-VP). The novelty of the present implementation lies in the fluid particles properties definition and in the particles shedding process. In spite of mimicking a panel method, the LL-VP needs some peculiar treatments described in the paper. The present implementation converges rapidly and efficiently during the shedding sub-iteration process. This LL-VP method shows good accuracy, even with moderate numbers of sections. Compared to its panel or vortex filaments counterparts, mor ..read more

Abstract Based on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a ..read more

Abstract To investigate the effect of force distributions of each turbine component on the power performance of the Darrieus–Savonius combined vertical axis wind turbine (hybrid VAWT), the hybrid VAWT is modeled as idealized turbine under various force distributions. The goal of idealization is to simplify the intricate interactions between the Savonius and Darrieus components. The simulation actuator surfaces with uniform force distributions lead to a cost-effective way to identify the optimal force distribution of each turbine component. The numerical model was validated against momentum the ..read more

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Abstract Actuator line modeling of wind turbines requires the definition of a free-stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so-called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre-existing approaches. Results show superior method robustness with respect to the regularization kernel width ( ϵ$$ \epsilon $$) choice while preserving acceptable accuracy. In particular ..read more

Abstract Across Europe, negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net-zero energy systems. Understanding public sentiment and its spatio-temporal variations is as such important for decision-making and for developing socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 6 ..read more

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