One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

The performance of photovoltaic (PV) arrays are affected by the operating temperature, which is influenced by thermal losses to the ambient environment. The factors affecting thermal losses include wind speed, wind direction, and ambient temperature. The purpose of this work is to analyze how the aforementioned factors affect array efficiency, temperature, and heat transfer coefficient/thermal loss factor. Data on ambient and array temperatures, wind speed and direction, solar irradiance, and electrical output were collected from a PV array mounted on a CanmetENERGY facility in Varennes, Canada, and analyzed. The results were compared with computational fluid dynamics (CFD) simulations and existing results from PVsyst. The findings can be summarized into three points. First, ambient temperature and wind speed are important factors in determining PV performance, while wind direction seems to play a minor role. Second, CFD simulations found that temperature variation on the PV array surface is greater at lower wind speeds, and decreases at higher wind speeds. Lastly, an empirical correlation of heat transfer coefficient/thermal loss factor has been developed.

No abstract

The atmosphere of Venus is 96% carbon dioxide and contains clouds of sulfur dioxide and sulfuric acid, with surface temperatures in excess of 470°C and pressures 92 times that of Earth. These extreme environmental conditions make planetary exploration difficult, as modern electronics cannot survive for prolonged periods of time. Photovoltaics, a conventional power generation method for Mars rovers, are inefficient on the planet’s surface due to the dense cloud cover and harsh environment. The NASA – JPL Hybrid Automaton Rover Venus proposes using a mechanical wind energy harvester to further explore the Venusian surface. At the proposed landing site, the surface wind speeds range from 0.3 to 1.3 m/s with an average wind speed of 0.6 m/s. These wind speeds, combined with the high density of Venusian air, results in promising potential for power generation. The power goal for the proposed wind harvester is 9W at the average wind speed of 0.6 m/s. A horizontal axis wind turbine (HAWT) is used to avoid dynamic stall experienced by vertical axis wind turbines at low wind speeds. In the HAWT, existing airfoil profiles were evaluated and chosen using an iterative design process. The blade designs were analyzed using blade element momentum theory (BEM) to predict and improve turbine performance. Testing was performed in water, as the greater fluid density allowed for testing at a lower speed than in air to better simulate Venus surface conditions. The preliminary water testing was carried out to characterize turbine performance. In this process, a 3D printed PLA 1:4 scale turbine was placed in an open-channel pool with flow supplied through a pump. The turbine was a fixed 2.3 m distance away from the inlet of the flow. The flow speed, turbine rotational speed, and torque produced were recorded. The results yielded turbine efficiencies between 7.7% and 46.1%. These results exceeded design expectations at the designed TSR, where an efficiency of 40% was to be expected. Based on the preliminary results, modifications are being made to the water testbed to improve the testing process as well as more accurately simulate conditions on the surface of Venus. The collected data and the aforementioned design tools are used to improve the current turbine design.