This article proposes a method of measuring wind speed using the data logged by the autopilot of a quadrotor drone. Theoretical equations from works on quadrotor control are utilized and supplemented to form the theoretical framework. Static thrust tests provide the necessary parameters for calculating wind estimates. Flight tests were conducted at a test site with laminar wind conditions with the quadrotor hovering next to a static 2D ultrasonic anemometer with wind speeds between 0–5 m/s. Horizontal wind estimates achieve exceptionally good results with root mean square error (RMSE) values between 0.26–0.29 m/s for wind speed, as well as between 4.1–4.9 for wind direction. The flexibility of this new method simplifies the process, decreases the cost, and adds new application areas for wind measurements.

This paper presents an optimal design of a hybrid wind turbine/PV/battery energy system for a household application using a multiobjective optimization approach, namely, particle swarm optimization (PSO). The ultimately optimal component selection of the hybrid renewable energy system (HRES) is suggested by comprehensively investigating the effects of various factors on the cost-reliability relation, such as the mounting orientation, temperature on the PV modules, wind turbine hub height, different types of batteries, and different load profiles. The optimization results show the feasibility of HRES for a single-family household demand in the arctic region of Troms, Norway. As we will discuss in the results, an HRES operating in such a region can achieve great energy-autonomous levels at a reasonable cost partially thanks to the cold climate. The mounting structure and temperature effects on the PV modules and the battery type can significantly change the system performance in terms of cost and reliability, while a higher wind turbine hub offers little improvement. The result suggests an optimal HRES consisting of a wind turbine with a swept area of 21m² and a hub height of 12m, a PV system of 12m² with 2-axis tracking, and a battery bank of 3kWh. This system will achieve 98.2 in self-reliance. Assuming that the system lifetime is 20 years, the annual cost is about 900 USD. Even though this study focuses on an HRES for a single-family application in the arctic, such an approach can be extended for other applications and in other geographical areas.

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