Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated

Finite-size corrections to the rotating string and the winding state

We compute higher order finite size corrections to the energies of the circular rotating string on AdS_5 x S^5, of its orbifolded generalization on AdS_5 x S^5/Z_M and of the winding state which is obtained as the limit of the orbifolded circular string solution when J -> infinity and J/M^2 is kept fixed. We solve, at the first order in lambda'=lambda/J^2, where lambda is the 't Hooft coupling, the Bethe equations that describe the anomalous dimensions of the corresponding gauge dual operators in an expansion in m/K, where m is the winding number and K is the "magnon number", and to all orders in the angular momentum J. The solution for the circular rotating string and for the winding state can be matched to the energy computed from an effective quantum Landau-Lifshitz model beyond the first order correction in 1/J. For the leading 1/J corrections to the circular rotating string in m^2 and m^4 and for the subleading 1/J^2 corrections to the m^2 term, we find agreement. For the winding state we match the energy completely up to, and including, the order 1/J^2 finite-size corrections. The solution of the Bethe equations corresponding to the spinning closed string is also provided in an expansion in m/K and to all orders in J.Comment: v2: 33 pages, misprints corrected, references added, version accepted for publication in JHE

mechanical behaviour of wind turbines operating above design conditions

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Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement

The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates

Wind turbine gearbox condition monitoring through the sequential analysis of industrial SCADA and vibration data

The operation & maintenance expenditure for a wind farm project can reach the impressive share of 30% of the total costs. This matter of fact motivates the need for optimal operation & maintenance, which is estimated to provide up to a 10% of energy production improvement. Such potential benefit can only be achieved through an efficient condition monitoring and predictive maintenance strategy. Based on these motivations, this paper presents a real-world case study in which standard diagnostic techniques failed to detect severe faults in the planetary stage of a wind turbine gearbox in time to prevent prolonged downtime. To address this issue, a measurement data processing and fusion algorithm is developed. The approach is capable of leveraging all the information from different data sources (with low to high time resolution) using different machine and deep learning algorithms, connected between them in cascade. This enables the detection of the fault some weeks in advance, compared to the commonly used methods and with lower-level processing of industrial operational data. A qualifying feature of the proposed workflow is that it enables the identification of the faulty component, which is a well known critical point in real-world applications.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Acoustically efficient concrete: acoustic absorption coefficient of porous concrete with different aggregate size

Porous absorbers are the most widely used type of acoustically absorptive materials. The interest on their outdoor applications has put further attention on the use of porous concrete in the building industry. This work investigates the acoustic properties of porous concrete. The assessment of the sound absorbing performances has been conducted in the small-scale reverberation room of Politecnico di Torino (Italy), in agreement with the indication in the ISO 354:2003 Standard. For each concrete type, three panel thicknesses, i.e. 20 mm, 40 mm, 60 mm were tested. Moreover, different mounting methods were tested, considering the presence of an airgap between the panel and the backing, and considering the introduction of rockwool in the airgap itself. The result show weighted absorption coefficients (aw) in the range 0.30-0.75 depending on the thickness and mounting conditions. These encouraging values make these materials useful for practical applications in architecture and civil engineering

Wind Energy Forecast in Complex Sites with a Hybrid Neural Network and CFD based Method

Abstract The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc.). In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site. The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability

condition monitoring techniques for machine bearings in non stationary operation

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wind turbine generator slip ring damage detection through temperature data analysis

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Long Term Wind Turbine Performance Analysis Through SCADA Data: A Case Study

Performance monitoring of horizontal-axis wind turbines is a complex task because they operate under nonstationary conditions. Furthermore, in real-world applications, there can be data quality issues because the free stream wind speed is reconstructed through a nacelle transfer function from cup anemometers measurements collected behind the rotor span. Given these matters of fact, one of the objectives of the present work is applying an innovative method for correcting the nacelle wind speed measurements, which is based on the manufacturer power curve and statistical considerations. Three operating wind turbines, having 2 MW of rated power and owned by the ENGIE Italia company, are contemplated as test cases. Operation data spanning ten years (2011–2020) are studied: actually, this work aims as well at contributing to the methods for estimating the performance decline with age of wind turbines, basing on long term SCADA data analysis. The raw and corrected wind speed measurements are fed as input to a Support Vector Regression for the power curve: by selecting appropriately the training and validation data sets, it is possible to estimate the average yearly rate of performance decline. Using the corrected wind speed, the estimate obtained in this study is compatible with the most recent findings in the literature, which indicate a -0.17% decline per year