Stability impact on wake development in moderately complex terrain

This paper uses a year of SCADA data from Whitelee Wind Farm near Glasgow to investigate wind turbine wake development in moderately complex terrain. Atmospheric stability measurements in terms of Richardson number from a met mast at an adjoining site have been obtained and used to assess the impact of stability on wake development. Considerable filtering of these data has been undertaken to ensure that all turbines are working normally and are well aligned with the wind direction. A group of six wind turbines, more or less in a line, have been selected for analysis, and winds within a 2 degree direction sector about this line are used to ensure, as far as possible, that all the turbines investigated are fully immersed in the wake/s of the upstream turbine/s. Results show how the terrain effects combine with the wake effects, with both being of comparable importance for the site in question. Comparison has been made with results from two commercial CFD codes for neutral stability, and reasonable agreement is demonstrated. Richardson number has been plotted against wind shear and turbulence intensity at a met mast on the wind farm that for the selected wind direction is not in the wake of any turbines. Good correlations are found indicating that the Richardson numbers obtained are reliable. The filtered data used for wake analysis were split according to Richardson number into two groups representing slightly stable to neutral, and unstable conditions. Very little difference in wake development is apparent. A greater difference can be observed when the data are separated simply by turbulence intensity, suggesting that, although turbulence intensity is correlated with stability, of the two it is the parameter that most directly impacts on wake development through mixing of ambient and wake flows

CFD modelling of double-skin facades with venetian blinds

This paper describes CFD modelling of Double Skin Façades (DSF) with venetian blinds inside the façade cavity. The 2-D modelling work investigates the coupled convective, conductive and radiative heat transfer through the DSF system. The angles of the venetian blind can be adjusted and a series of angles (0, 30, 45, 60 and 80 degrees) has been modelled. The modelling results are compared with the measurements from a section of façade tested within a solar simulator and with predictions from a component based nodal model. Agreement between the three methods is generally good. Discrepancies in the results are generally caused by the simplification of the CFD model resulting less turbulence mixing within the façade cavity. The CFD simulation output suggests that the presence of the venetian blinds has led up to 35 percent enhancement in natural ventilation flow for the façade cavity and 75 percent reduction in heat loads for the internal environment. It was also found that little changes of the convective heat transfer coefficients on the glazing surfaces have been caused by the venetian blinds with different angles

Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts

This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering additional spatial measurements. To achieve this, two different data sets have been used: (i) wind speed and direction measurements taken over 23 Met Office weather stations distributed across the UK, and (ii) outputs from the Consortium for Small-scale Modelling (COSMO) numerical weather model on a grid of points covering the UK and the surrounding sea. A multivariate complex valued adaptive prediction filter is applied to these data. The study provides an assessment of how well the proposed model can predict the data one hour ahead and what improvements can be accomplished by using additional data from the COSMO model

On the use of high-frequency SCADA data for improved wind turbine performance monitoring

SCADA-based condition monitoring of wind turbines facilitates the move from costly corrective repairs towards more proactive maintenance strategies. In this work, we advocate the use of high-frequency SCADA data and quantile regression to build a cost effective performance monitoring tool. The benefits of the approach are demonstrated through the comparison between state-of-the-art deterministic power curve modelling techniques and the suggested probabilistic model. Detection capabilities are compared for low and high-frequency SCADA data, providing evidence for monitoring at higher resolutions. Operational data from healthy and faulty turbines are used to provide a practical example of usage with the proposed tool, effectively achieving the detection of an incipient gearbox malfunction at a time horizon of more than one month prior to the actual occurrence of the failure

A review of grid-tied converter topologies used in photovoltaic systems

This study provides review of grid-tied architectures used in photovoltaic power systems, classified by the granularity level at which maximum power point tracking (MPPT) is applied. Grid-tied PV power systems can be divided into two main groups, namely centralized MPPT (CMPPT) and distributed MPPT (DMPPT). The DMPPT systems are further classified according to the levels at which MPPT can be applied, i.e. string, module, submodule, and cell level. Typical topologies for each category are also introduced, explained and analyzed. The classification is intended to help readers understand the latest developments of grid-tied PV power systems and inform research directions

New hydrogen-like potentials

Using the modified factorization method introduced by Mielnik, we construct a new class of radial potentials whose spectrum for l=0 coincides exactly with that of the hydrogen atom. A limiting case of our family coincides with the potentials previously derived by Abraham and MosesComment: 6 pages, latex, 2 Postscript figure

Gaussian process power curve models incorporating wind turbine operational variables

This is the final version. Available from Elsevier via the DOI in this record. The IEC standard 61400−12−1 recommends a reliable and repeatable methodology called ‘binning’ for accurate computation of wind turbine power curves that recognise only the mean wind speed at hub height and the air density as relevant input parameters. However, several literature studies have suggested that power production from a wind turbine also depends significantly on several operational variables (such as rotor speed and blade pitch angle) and incorporating these could improve overall accuracy and fault detection capabilities. In this study, a Gaussian Process (GP), a machine learning, data-driven approach, based power curve models that incorporates these operational variables are proposed in order to analyse these variables impact on GP models accuracy as well as uncertainty. This study is significant as it find out key variable that can improve GP based condition monitoring activities (e.g., early failure detection) without additional complexity and computational costs and thus, helps in maintenance decision making process. Historical 10-minute average supervisory control and data acquisition (SCADA) datasets obtained from variable pitch regulated wind turbines, are used to train and validate the proposed research effectiveness The results suggest that incorporating operational variables can improve the GP model accuracy and reduce uncertainty significantly in predicting a power curve. Furthermore, a comparative study shows that the impact of rotor speed on improving GP model accuracy is significant as compared to the blade pitch angle. Performance error metrics and uncertainty calculations are successfully applied to confirm all these conclusions.Marie Sklodowska-Curie grantEuropean Union’s Horizon 2020 researc

Operational Variables for improving industrial wind turbine Yaw Misalignment early fault detection capabilities using data-driven techniques

This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOffshore wind turbines are complex pieces of engineering and are, generally, exposed to harsh environmental conditions that are making them to susceptible unexpected and potentially catastrophic damage. This results in significant down time, and high maintenance costs. Therefore, early detection of major failures is important to improve availability, boost power production and reduce maintenance costs. This paper proposes a SCADA data based Gaussian Process (GP) (a data-driven, machine learning approach) fault detection algorithm where additional model inputs, called operational variables (pitch angle and rotor speed) are used. Firstly, comparative studies of these operational variables are carried out to establish whether the parameter leads to improved early fault detection capability; it is then used to construct an improved GP fault detection algorithm. The developed model is then validated against existing methods in terms of capability to detect in advance (and by how much) signs of failure with a low false positive rate. Failure due to yaw misalignment results in significant down time and a reduction in power production was found to be a useful case study to demonstrate the effectiveness of the proposed algorithms. Historical SCADA 10-minute data obtained from pitch-regulated turbines were used for models training and validation purposes. Results show that (i) the additional model inputs were able to improve the accuracy of GP power curve models with rotor speed responsible for a significant improvement in performance; (ii) the inclusion of rotor speed enhanced early failure detection without any false positives, in contrast to the other methods investigated.U.S. Department of Commerc