Band 8, Heft 2

This study intends to examine the performance of a balloon-assisted micro airborne wind turbine in a low wind speed location. The influence of the balloon separation gap on the airborne wind energy system (AWES) performance is also explored. A micro-AWES with a diameter of 3 m and a power output of 1 kW was fabricated and tested at 50, 100, 150, 200, and 250 m. Further, the optimum separation spacing of 13 m was maintained between the balloon and the ducted turbine to reduce balloon turbulence on the turbine. The airborne wind turbine achieved a maximum power output of 250 W at 250 m height while the average wind speed remained 6 m/s. The maximum power coefficient obtained was 0.25 while annual energy production (AEP) remained 1200 kWh. The low power coefficient is credited to the turbulence and drifting in the airborne system and the drag caused by the airborne structure. While a cost-effective commercial model of micro AWES is still being developed, the present work attempts to harvest wind energy at high elevations in low wind speed areas.

In recent years, the use of wind energy to generate electricity in the world has been accelerating and growing. Wind farms are unstable when dynamic voltage fluctuations occur, especially sudden and sudden changes in load, and show oscillating performance at their output. In this paper, the Unified Power Flow Controller (UPFC) has been simulated and studied by Matlab software to improve the dynamic stability and transient behavior of the wind power plant in the event of sudden load changes. The simulation results show that by controlling the UPFC series inverter, voltage fluctuations in the PCC bus are prevented and the UPFC parallel inverter injects power after changing the load for faster recovery and stability of the PCC bus voltage and thus the stability of the wind farm. The UPFC can control the active and reactive power at the transmission line, and in fact, controls the output of the wind turbine with the generator from both sides to the fluctuations caused by sudden load changes that play a role such as sudden disturbances and oscillating errors. Also, the presence of UPFC in the system reduces power fluctuations.

In this paper, the improvement of the sensitivity of a capacitive MEMS pressure sensor is investigated. The proposed spring for the sensor can increase the sensitivity. Silicon is used as the substrate and gold and aluminium nitrate are used as the diaphragm and the dielectric layer, respectively. The dimensions of the diaphragm are 150 µm × 150 µm, which is suspended by four springs. The air gap between the diaphragm and the top electrode is 1.5 µm. The proposed structure is an efficient sensor for the pressures in the range of 1–20 kPa. By using the proposed design, the sensitivity of the MEMS sensor in 18 kPa has improved to 663 (× 10 −3  pF/kPa).

Workflow scheduling is the recent researching area in the cloud environment, in which user satisfaction based on the cost and bandwidth is the most challenging task. Several research methods are devised to minimize the execution time and cost, which compromises the attributes. Hence, this research introduces an effective task scheduling mechanism in a cloud environment utilizing the Regressive Whale Water Optimization (RWWO) algorithm, which is derived by the integration of Regressive Whale Optimization (RWO) and Water Cycle Algorithm (WCA). The fitness parameters utilized are Quality of Service (QoS), resource utilization, and predicted energy. However, predicted energy is determined using Deep Maxout Network. Moreover, the proposed RWWO + Deep Maxout Network achieved a minimum task scheduling time of 0.0208, minimum task scheduling cost of 0.0017, minimum predicted energy of 0.1971, and maximum resource utilization of 0.9999.