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Abstract The present work reports the physical, size and shape, flowability, drying and devolatilization properties of ground wood and ground bark particles. Mechanical sieving and image processing identify the size and shape of ground particles, respectively. Ground particles are dried at initial moisture contents of 0.30, 0.50, 0.70 and 0.90 (dry mass basis) and drying temperatures of 70, 100, 130 and 160 °C. Devolatilization rate of particles is measured using a thermogravimetric analyzer. Microscopic investigations show that wood particles are longer and thinner than bark particles. More spherical shape facilitates the flowability of the bark particles. Wood particles are cohesive and have poorer flowability properties than bark particles. Bark particles have a lower internal void fraction than wood particles. Denser structure of bark particles diminishes the drying and devolatilization rate and prolongs the heat and mass transfer process compared to the wood particles.

The aim of this work is to develop a prediction method for renewable energy sources in order to achieve an intelligent management of a microgrid system and to promote the utilization of renewable energy in grid connected and isolated power systems. The proposed method is based on the multi-resolution analysis of the time-series by means of Wavelet decomposition and artificial neural networks. The analysis of predictability of each component of the input data using the Hurst coefficient is also proposed. In this context, using the information of predictability, it is possible to eliminate some components, having low predictability potential, without a negative effect on the accuracy of the prediction and reducing the computational complexity of the algorithm. In the evaluated case, it was possible to reduce the resources needed to implement the algorithm of about 29% by eliminating the two (of seven) components with lower Hurst coefficient. This complexity reduction has not impacted the performance of the prediction algorithm.

In this paper, a small-scale standalone wind energy conversion system composed of a squirrel-cage induction generator, a buck converter and a current-source inverter is proposed, as an attractive renewable energy solution for off-grid communities. Geared squirrel-cage induction generators are well-known for their robustness, simplicity, light weight and low cost. Current-source inverters, even though mainly used in medium-voltage, high power applications, and proposed for megawatt-level grid-connected wind energy conversion systems, offer potential benefits in small-scale off-grid wind energy conversion systems that are yet to be investigated and evaluated against those of commonly-used voltage-source inverters. In the proposed system, the generator's shaft speed is controlled by a buck converter to extract maximum available wind power in normal mode of operation, and the wind power is dumped when it is not possible to absorb maximum available power by the storage system and the load. A novel scheme for integration of a battery energy storage system is proposed and an effective power management algorithm is employed to maintain the supply-demand power balance through direct control of dc-link current. A systematic approach for the dc-link inductor design is presented. The feasibility of the proposed system and its performance under variable wind and load conditions are analyzed and demonstrated through simulation.

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Abstract Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.

The effects of dispatch strategy on electrical performance of amorphous silicon-based solar photovoltaic-thermal systems, Renewable Energy 68, pp. 459-465 (2014). http://dx. Abstract: Previous work has shown that high-temperature short-term spike thermal annealing of hydrogenated amorphous silicon (a-Si:H) photovoltaic thermal (PVT) systems results in higher electrical energy output. The relationship between temperature and performance of a-Si:H PVT is not simple as high temperatures during thermal annealing improves the immediate electrical performance following an anneal, but during the anneal it creates a marked drop in electrical performance. In addition, the power generation of a-Si:H PVT depends on both the environmental conditions and the Staebler-Wronski Effect kinetics. In order to improve the performance of a-Si:H PVT systems further, this paper reports on the effect of various dispatch strategies on system electrical performance. Utilizing experimental results from thermal annealing, an annealing model simulation for a-Si:H-based PVT was developed and applied to different cities in the U. S. to investigate potential geographic effects on the dispatch optimization of the overall electrical PVT systems performance and annual electrical yield. The results showed that spike thermal annealing once per day maximized the improved electrical energy generation.

Abstract In this paper, numerical and experimental studies are presented to determine the operating performance and power output from a vertical axis wind turbine (VAWT). A k-ɛ turbulence model is used to perform the transient simulations. The 3-D numerical predictions are based on the time averaged Spalart-Allmaras equations. A case study is performed for varying VAWT stator vane (tab) geometries of a Zephyr vertical axis wind turbine. The mean velocity is used to predict the time averaged variations of the power coefficient and power output. Power coefficients predicted by the numerical models are compared for different turbine geometries. The predictive capabilities of the numerical model are verified by past experimental data, as well as wind tunnel experiments in the current paper, to compare two particular geometric designs. The numerical results examine the turbine's performance at constant and variable rotor velocities. The effects of stator vanes on the turbine's power output are presented and discussed.

The aerodynamic interaction of vertical axis wind turbines in several array configurations was studied by conducting a series of wind tunnel measurements. Four configurations of two- and three-turbine arrays were tested and their results were compared with that of the isolated reference case. Two pairs of counter-rotating and co-rotating vertical axis wind turbines were tested where the free-stream wind was perpendicular to the two side-by-side turbines. The counter-rotating configuration resulted in a slight improvement in the aerodynamic performance of each turbine compared to the isolated case, while the co-rotating installation caused a slight performance reduction of turbines at some free-stream velocities. Several measurements were also performed for three-turbine arrays with different spacing where a vertical axis wind turbine was operating downstream of a counter-rotating pair, perpendicular to the free-stream wind. An enhancement in the aerodynamic performance of the downstream turbine was observed in almost all arrays and at most tested wind speeds. For the array spacing studied, the optimum range of the streamwise distance of the downstream turbine from the counter-rotating pair and the spacing between the pair was determined to be about three and one rotor diameters, respectively.