Design of an automatic transmission system to improve energy capture of vertical axis wind turbine

Nowadays, renewable energies are highly demanded as they are sustainable and environmentally friendly. One of the renewable energy is wind, and it can be harvested by using a wind turbine. There are two types of wind turbines: Horizontal Axis Wind Turbine (HAWT) and Vertical Axis Wind Turbine (VAWT). Large scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is needless to say its efficiency; it is highly efficient. However, it comes with high complexity and cost too. Meanwhile, most small-scale wind turbines in the current market offer a one-speed gearing system only, which means no gear ratios are varied, resulting in low energy efficiency and leading to gears failure. They have recognized a need for the continuous monitoring of major wind turbine components, gearbox parts. These components are seen to require substantial maintenance and repair efforts. For a fixed-speed wind turbine, the generator is directly connected to the electrical grid and they have several drawbacks in which the reactive power or the grid voltage level cannot be controlled. This research concerns the design of an automatic transmission system in VAWT to increase its efficiency in harvesting energy. The gear and clutch system was designed and fabricated for VAWT and the system was analyzed. The gear and clutch system was calculated using the gear and clutch formula. Then, the system was designed using Solidworks and fabricated using a 3D printer for VAWT. The gear ratios have been varied and the number of gears has been increased to two. A Centrifugal clutch is applied to the gear to perform its automatic gear shifting. During the test, incoming wind speed is firstly increased until the vertical axis wind turbine started to spin, then the wind speed is decreased. The incoming wind speed is restricted from 0 m/s to 20 m/s. The energy harvesting efficiency is measured by comparing a vertical axis wind turbine's output voltage and output power with automatic and without automatic transmission systems. The result shows that applying automatic transmission systems with a centrifugal clutch for VAWT is reliable and improves its efficiency. Generally, with the application of an automatic transmission system, the start-up wind speed for VAWT to spin was reduced from 20 m/s to 13 m/s. The VAWT with an automatic transmission system started to spin at 13 m/s of wind speed and it could maintain its spinning even the incoming wind speed was reduced. The voltage and power produced also show that the VAWT can optimize energy harvesting efficiency with less energy loss

A supervised machine-learning method for optimizing the automatic transmission system of wind turbines

Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only where no gear ratios are varied, resulting in low efficiency of harvesting energy and leading to gears failure. In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. The aim is to find the best adjustment for VAWT while the automatic transmission system is taken into account. For this purpose, the system is simulated and tested under various gear ratios conditions while a centrifugal clutch is applied to automatic gear shifting. The outcomes indicated that the automatic transmission system could successfully adjust the spinning in line with the wind speed. As a result, the obtained level of harvested voltage and power by VAWT with the automatic transmission system are improved significantly. Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently