ALBERT

Description: The emergy concept, integrated with a multi-objective linear programming method, was used to model the agricultural structure of Xinjiang Uygur Autonomous Region under the consideration of the need to develop a low-carbon economy. The emergy indices before and after the structural optimization were evaluated. In the reconstructed model, the proportions of agriculture, forestry and artificial grassland should be adjusted from 19:2:1 to 5.2:1:2.5; the Emergy Yield Ratio (1.48) was higher than the average local (0.49) and national levels (0.27); and the Emergy Investment Ratio (11.1) was higher than the current structure (4.93) and that obtained from the 2003 data (0.055) in Xinjiang Uygur Autonomous Region, the Water Emergy Cost (0.055) should be reduced compared to that before the adjustment (0.088). The measurement of all the parameters validated the positive impact of the modeled agricultural structure. The self-sufficiency ratio of the system increased from the original level of 0.106 to 0.432, which indicated a better coupling effect among the subsystems within the whole system. The comparative advantage index between the two systems before and after optimization was approximately 2:1. When the mountain ecosystem service value was considered, excessive animal husbandry led to a 1.41 × 1010 RMB·a−1 indirect economic loss, which was 4.15 times the GDP during the same time period. The functional improvement of the modeled structure supports the plan to “construct a central oasis and protect the surrounding mountains and deserts” to develop a sustainable agricultural system. Conserved natural grassland can make a large contribution to the carbon storage; and therefore, it is wise alternative that promote a low-carbon economic development strategy.

Description: Energies, Vol. 10, Pages 1877: The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT Energies doi: 10.3390/en10111877 Authors: Yancai Xiao Ruolan Dai Guangjian Zhang Weijia Chen In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.

Description: Linear wave energy converters generate intrinsically intermittent power with variable frequency and amplitude. A composite energy storage system consisting of batteries and super capacitors has been developed and controlled by buck-boost converters. The purpose of the composite energy storage system is to handle the fluctuations and intermittent characteristics of the renewable source, and hence provide a steady output power. Linear wave energy converters working in conjunction with a system composed of various energy storage devices, is considered as a microsystem, which can function in a stand-alone or a grid connected mode. Simulation results have shown that by applying a boost H-bridge and a composite energy storage system more power could be extracted from linear wave energy converters. Simulation results have shown that the super capacitors charge and discharge often to handle the frequent power fluctuations, and the batteries charge and discharge slowly for handling the intermittent power of wave energy converters. Hardware systems have been constructed to control the linear wave energy converter and the composite energy storage system. The performance of the composite energy storage system has been verified in experiments by using electronics-based wave energy emulators.