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

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs