Browsing by Author "Hong, Jiarong"

Now showing 1 - 4 of 4
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    Experimental analysis of the effect of static yaw error on wind turbine nacelle anemometer measurements
    (IEEE, 2023-08-03) Astolfi, Davide; Gao, Linyue; Pandit, Ravi; Hong, Jiarong
    The operation of wind turbines in real-world environments can be affected by the presence of systematic errors, which might diminish the Annual Energy Production up to 3-4%. Therefore, it is fundamental to leverage the availability of SCADA-collected measurements in order to formulate reliable diagnosis methods. The static yaw error of a wind turbine occurs when, due to wind vane or installation defects, the rotor plane is systematically not perpendicular to the wind flow. The present work is devoted to the experimental analysis of how the presence of a static yaw error affects the wind turbine nacelle anemometer measurements. Measurements collected at the Eolos Wind Research Station at the University of Minnesota are analyzed. The qualifying aspect is that a utility-scale wind turbine has been fully controlled and imposed to set to a non-vanishing yaw error. Furthermore, approximately two rotor diameters south of the turbine there is a meteorological tower which provides unbiased measurements of the environmental conditions. The main result of this work is that, for given wind speed measured by the meteorological mast anemometers, the measurements of the nacelle wind speed changes systematically in presence of the static yaw error. This aspect has up to now been overlooked in the literature. Therefore, the results of this work might stimulate a critical revision of the existing methods for static yaw error diagnosis and the formulation of new ones.
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    Individuation of wind turbine systematic yaw error through SCADA data
    (MDPI, 2022-11-01) Astolfi, Davide; Pandit, Ravi; Gao, Linyue; Hong, Jiarong
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    Investigation of wind turbine static yaw error based on utility-scale controlled experiments
    (IEEE, 2024-05-08) Astolfi, Davide; De Caro, Fabrizio; Pasetti, Marco; Gao, Linyue; Pandit, Ravi; Vaccaro, Alfredo; Hong, Jiarong
    Wind energy represents a promising alternative to replace traditional fossil-based energy sources. For this reason, increasing the efficiency in the conversion process from wind to electrical energy is crucial. Unfortunately, the presence of systematic errors (mostly related to the yaw and pitch angles) is one of the key factors causing underperformance, and for this reason, it requires adequate identification. The present work deals with diagnosing wind turbine static yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. A thorough investigation methodology is proposed by considering a unique experimental test-up shared by the Eolos Wind Research Station. A utility-scale wind turbine has been imposed to operate subjected to several static yaw errors and reference meteorological data collected nearby the wind turbine were available. By analyzing the relation between the meteorological data and the SCADA data collected by the wind turbine, a systematic alteration in the measurements of nacelle wind speed in the presence of the yaw error is explicitly shown. This phenomenon has been overlooked in the literature and leads to revisiting the methods mostly employed for the diagnosis of the error. Furthermore, a correlation between the presence of static error, increased blade pitch, and heightened levels of tower vibration is observed. In summary, this work provides a comprehensive characterization of the experimental evidence associated with the presence of a wind turbine static yaw error. This paves the way for more effective diagnostic techniques for wind turbine yaw errors, potentially revolutionizing data-driven maintenance strategies.
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    SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends
    (Sage, 2022-09-19) Pandit, Ravi; Astolfi, Davide; Hong, Jiarong; Infield, David; Santos, Matilde
    This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.