Wirelessly controlled harvester/sensor of air speed

Original Paper


A wirelessly controlled self-powered multi-functional system that uses a relay to change from harvesting air flow energy to sensing its speed and vice versa is developed. Both functions are achieved through the use of the same micro-wind turbine. When the relay is in the on position, the turbine harvests the air’s kinetic energy to charge a battery. When a measurement is needed, the relay is turned off wirelessly and energy harvesting is shut down. The charged battery is then used to turn on a wirelessly controlled single board computer that controls a data acquisition system to sense the rotational speed of the turbine, which is proportional to the air speed. The system is tested and results from a broad range of wind speeds are presented and analyzed. The system presented here can be used for autonomous sensing of air speed without a need for wired connections to an external power source or batteries that need to be regularly replaced, which makes it ideal for integration within the Internet of things as a platform for a smart building system.


Energy harvesting Harvester/sensor configuration Self-powered sensors Micro-turbine Air speed sensor 

1 Introduction

Monitoring air speed is required for operation and decision-making in different applications. One application is in smart buildings where the need is to ensure proper operation of the heating, ventilation, and air conditioning system for the purpose of reducing energy and operating expenses by controlling temperature, humidity, and air flow in various zones. Another application is the need to acquire wind speed over different sections of a bridge or a high-rise building and relate those measurements to wind-induced vibrations of these structures. In these and other applications, the number of required sensors, the unavailability of power, or the location of the sensing device may preclude the options of using wired power or batteries that need to be replaced regularly. One solution is to revert to harvesting ambient wind energy and use it to power air speed sensors, wirelessly transmit data, and initiate decision-making.

Harvesting the kinetic energy of wind to operate a sensor can be achieved through a turbine or aeroelastic vibrations including flutter Erturk et al. (2010), vortex-induced vibrations Mehmood et al. (2013), and Grouthier et al. (2014) or galloping Bibo and Daqaq (2014) of bodies placed in the air stream. The level of harvested power depends on the size of the harvester, the transduction mechanism, and the air speed. Different studies have shown that air flow harvesters of few \(\mathrm{cm}^2\) in area or \(\mathrm{cm}^3\) in volume can yield power levels of 0.1–100’s \(\mathrm{mW}\) when placed in wind speeds up to 15 m/s. Rancourt et al. (2007) obtained a power level of 9.39 \(\mathrm{mW/cm}^2\) from a 4 \(\mathrm{cm}\) micro-wind turbine that operated at a speed of 11.8 \(\mathrm{m/s}\). Using a turbine that is 2.6 \(\mathrm{cm}\) in diameter, Zakaria et al. (2015) generated power levels that increased from 0.1 and 2 \(\mathrm{mW/cm}^2\) as the incident flow speed was increased from 4 to 10 \(\mathrm{m/s}\). Myers et al. (2007) proposed and optimized the performance of a small-scale piezoelectric windmill that can continuously harvest 5 \(\mathrm{mW}\) at speeds of about 5 \(\mathrm{m/s}\). Fu and Yeatman (2015) proposed and tested a piezoelectric turbine that extracts flow energy at low speeds and uses a self-regulating mechanism to harvest power at high speeds. Comparable levels of energy harvesting can be achieved also from aeroelastic vibrations. Kwon (2010) showed that a 10\(\times \)6\(\times \)3 \(\mathrm{cm}^3\) T-shaped piezoelectric cantilever can harvest 4 \(\mathrm{mW}\) from air flow at a speed of 4 \(\mathrm{m/s}\). Zakaria et al. (2015) harvested 0.17 \(\mathrm{mW}\) from sustained oscillations of a 26\(\times \)2\(\times \)0.05 \(\mathrm{cm}^3\) flexible beam when placed in an air flow with a speed of 9 \(\mathrm{m/s}\) at specific preset angles of attack. Yet, when it comes to a specific application, one must consider the output voltage and current, because these factors determine the ability of the harvester to charge a battery or to wirelessly transmit a signal across a specific platform.

Different approaches have been proposed to combine energy harvesting and sensing devices for monitoring air speed. One approach is to power the sensor with ambient energy harvested from a different source. Another approach would be to use a wind energy harvester to charge a capacitor or a battery and then use that power to operate a sensor that is different from the harvester. Liu et al. (2012) assessed the performance of a piezoelectric PZT microcantilever in terms of flow sensing and energy harvesting capability. They proposed employing one PZT microcantilever for flow sensing and integrating an array of other PZT microcantilevers to harvest enough energy from wind-induced vibrations to power the sensing microcantilever. To the authors’ knowledge, there has not been any investigation for wirelessly operating one device that can harvest the wind energy over a specific period and then use the harvested power to sense and transmit the wind speed over a different period.

In this paper, we develop a wirelessly controlled self-powered multi-functional system that harvests the air flow energy and senses and transmits its speed using a battery charged with its own harvested power. This self-powered autonomous air speed sensor eliminates the need for separate devices or components for sensing and energy harvesting and, thereby, reduces the size and cost of wireless sensing nodes. The system is controlled wirelessly through a single board computer that is also powered by the harvested energy.

2 Experimental setup

For the current experiments, the need was to charge a 5 V battery that is required to power a single board computer and a relay. Towards this end, a 9-cm micro-turbine as shown in Fig. 1 was used as the energy harvester. The parameters of this turbine are presented in Table 1. The turbine was connected to a micro-DC generator that is also shown in the picture of Fig. 1.

All tests of the proposed system were performed in a suction-type open circuit wind tunnel powered by a 15-hp centrifugal fan. This fan forces the flow to pass through a square (1.5 \(\mathrm{m}\) \(\times \) 1.5 \(\mathrm{m}\)) honeycomb inlet made of straws that are 0.001 \(\mathrm{m}\) in diameter and 0.09 \(\mathrm{m}\) long. This inlet is followed by turbulence reduction screens that ensure a uniform flow with a variation across the 52 \(\mathrm{cm}\) \(\times \) 52 \(\mathrm{cm}\) square test section that is less than 2.5\(\%\) and a turbulence intensity that is less than 2\(\%\). The turbine used in these tests was placed in the center of the test section with the Pitot-static tube set 10 \(\mathrm{cm}\) away from the axis of rotation and 20 \(\mathrm{cm}\) ahead of the turbine. The flow velocity is measured with an accuracy of 70.5\(\%\) based on the reading recorded from the Pitot-static tube that uses a differential pressure scani-valve. To determine the power that can be harvested from this turbine, the generator’s output was measured using a DATAQ instruments unit. The data sampling rate was set to 2500 \(\mathrm{Hz}\) and each data segment was recorded over a period of 5 s.
Fig. 1

Picture of the tested micro-turbine and DC generator

Table 1

Parameters of wind turbine

Total diameter

Hub diameter


Midpoint chord

9 cm

2.5 cm

5.2 g

3.2 cm

Fig. 2

Variation of the calculated mean power with the load resistance at different incident air speeds

3 Harvesting/sensing system

3.1 Harvesting power of micro-wind turbine

Figure 2 shows the variations of the harvested power from the micro-turbine with the load resistance for different flow speeds. The plots show that, for each speed, the harvested power increases at a high rate as the resistance is increased from 50 \(\Omega \) to reach a maximum near about 300 \(\Omega \). With further increase in the load resistance, the level of harvested power decreases. These results indicate that the optimal load resistance for energy harvesting is near 300 \(\Omega \). It is also important to note that as the flow speed is decreased from 8 to 6 and 4 \(\mathrm{m/s}\), the corresponding level of harvested power is decreased by one order of magnitude from about 500 \(\mathrm{mW}\) to about 200 and 50 \(\mathrm{mW}\).

Given that the rotational speed of the turbine and, thus, the open circuit voltage is proportional to the incident flow speed, this voltage can be used as a measure of the wind speed. Figure 3 shows the variation of the measured voltages for open circuit and closed circuit with 300 \(\Omega \) configurations with the air speed. The results show that an open circuit mean voltage can be measured at air speeds that are as low as 2 \(\mathrm{m/s}\). In contrast, adding an electric load to the system increases the start-up speed to 3 \(\mathrm{m/s}\). In the open circuit configuration, the measured mean voltage increases gradually from a value of about 5 \(\mathrm{V}\) at 3 \(\mathrm{m/s}\) to a value of about 26 \(\mathrm{V}\) at 11 \(\mathrm{m/s}\). The corresponding values in the closed circuit configuration are 3.8 and 18 \(\mathrm{V}\) respectively. In the following, a wirelessly controlled circuit is set up to allow for using the harvested energy from the air flow to charge a battery over a specific period and then for the charged battery to measure and to transmit the open circuit voltage outside the periods over which the battery was charging.
Fig. 3

Measured open and closed circuit voltages as a function of the flow speed. The closed circuit voltage was obtained using a load resistance of 300 \(\Omega \)

When charging a battery, the output voltage of the turbine would be smaller than that of the open circuit voltage, because the battery acts like a damper by drawing energy from the turbine. Therefore, the voltage used to charge the battery cannot be directly related to the wind speed, because it depends on the battery characteristics and its charge level. On the other hand, the open circuit voltage is directly related to the wind speed.

3.2 Design of system

A functional system diagram for the harvester/sensor that uses the same device, which, in this case, is the wind turbine, is shown in Fig. 4. The components include a relay switch that is connected to a single board computer. Both of these components are accessed and controlled remotely. The switch controls the operational functions of the integrated turbine and generator system. These functions consist of sensing the wind speed and charging a battery. When the relay switch is in the close position, the system acts as a harvester. The generated power of the turbine through the DC generator is connected to a charging module and a battery. This battery is used to power a single board computer (SBC) and a data acquisition system (DAQ). When the relay switch, which is controlled remotely, is turned to the open position, the turbine acts as an air speed sensor and an analog signal is sent to the DAQ and read remotely for further data analysis. The charged battery is used to power the relay switch, the SBC, and DAQ. This functional diagram guided the design of the multi-functional system that is explained next.
Fig. 4

Functional system diagram of the air speed harvester/sensor

Fig. 5

Picture of the connected components in the experimental setup

The picture of the multi-functional system, presented in Fig. 5, shows the main components of the wirelessly controlled harvester/sensor system. The 9-cm micro-wind turbine is connected to a direct current (DC) generator. When the relay switch is set in the closed position, the generator is connected to a charging module and a battery. The charging module is responsible for maintaining the charging voltage at 5 V as required to charge the battery. The battery is used to power a Raspberry pi 3 model B (SBC) and a BTH-1208LS data acquisition system. Through a wireless connection, the SBC controls the relay switch and turns it to the off position. In this configuration, the generated voltage is read by the DAQ and transmitted by the SBC wirelessly to a remote computer. By turning the relay switch on, the data acquisition is stopped and the energy generated by the turbine is used to charge the battery.
Fig. 6

Time series of the voltage as the system function is changed between charging and sensing modes. Results are presented for five different incident air speeds

3.3 Testing and analysis

System testing and analysis were performed at different air speeds between 3 and 7 \(\mathrm{m/s}\) and samples of the harvesting and sensing records are presented in Fig. 6. At all speeds, the test is started by charging the battery for 25 s, taking data for 10 s, and repeating this process three times. At 3 \(\mathrm{m/s}\), the charging voltage is about 3 \(\mathrm{V}\) and the sensing voltage is about 5 \(\mathrm{V}\). At higher speeds, the charging voltage is limited to 4.2 \(\mathrm{V}\). This limit is set by the charging module, which limits the voltage supplied to the battery. The sensing voltage increases from 8.4 \(\mathrm{V}\) at 4 \(\mathrm{m/s}\), to 11.8 \(\mathrm{V}\) at 5 \(\mathrm{m/s}\), 14.2 \(\mathrm{V}\) at 6 m/s, and 17.9 \(\mathrm{V}\) at 7 \(\mathrm{m/s}\), which reflects the increase in the output voltage as the air speed is increased.
Fig. 7

Variations of the measured mean power and charging current at different air speeds

Fig. 8

Comparison of the variation of the open circuit voltage as obtained from the experiments and measurements

Assessment of the system’s performance must also be based on the levels of harvested power and charging current. The variations of the mean values of these quantities with the incident air speed are plotted in Fig. 7. The plots show that the charging current increases from about 2 \(\mathrm{mA}\) at 3 m/s to 85.6 \(\mathrm{mA}\) at 8 \(\mathrm{m/s}\) and 153.3 \(\mathrm{mA}\) at 11 \(\mathrm{m/s}\), which indicates an increase of about two orders of magnitude in the value of the charging current as the flow speed is increased from 3 to 11 \(\mathrm{m/s}\). The power plot in the figure shows almost a similar trend for the charging power, which increases from about 12 \(\mathrm{mW}\) at flow speed of 3 \(\mathrm{m/s}\) to about 450 \(\mathrm{mW}\) at a speed of 8 \(\mathrm{m/s}\) and to about 800 \(\mathrm{mW}\) at a speed of 11 \(\mathrm{m/s}\).

A comparison of the measured mean open circuit voltage and the measured mean value of the harvester/sensor device when operating in the sensing mode as a function of the air speed is presented in Fig. 8. Based on the observed agreement, it is concluded that the measured voltage by the proposed system reflects accurately the open circuit voltage that is directly related to the air speed as discussed above.

4 Conclusions

A self-powered multi-functional system that uses its own harvested power to sense a voltage was designed and tested. The tested system is based on a micro-wind turbine that harvests kinetic energy of air flow to charge a battery. The battery is then used to power a wirelessly controlled single board computer that is connected to a relay and a data acquisition system. The relay is used to switch the function of the system from harvesting air flow energy to sensing its speed and vice versa. The results show that switching between the two functions is achieved smoothly. The charging voltage, power, and current as a function of the air speed were analyzed. The accuracy of the system was assessed by comparing the sensing voltage of the multi-functional system against the open circuit voltage that is directly related to the air speed.



Muhammad Hajj acknowledges the support of the Center for Energy Harvesting Materials and Systems and the National Science Foundation under Grant 1035042.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Engineering Mechanics ProgramVirginia TechBlacksburgUSA

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