scholarly journals Piezoelectric Wind Energy Harvesting from Self-Excited Vibration of Square Cylinder

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Junlei Wang ◽  
Sheng Wen ◽  
Xingqiang Zhao ◽  
Min Zhang ◽  
Jingyu Ran
Keyword(s):  
Wind Energy ◽  
Aerodynamic Force ◽  
Load Resistance ◽  
Open Circuit ◽  
Transverse Displacement ◽  
Square Cylinder ◽  
Force Output ◽  
Vortex Induced Vibration ◽  
Wind Speeds ◽  
Excited Vibration

Self-excited vibration of a square cylinder has been considered as an effective way in harvesting piezoelectric wind energy. In present work, both of the vortex-induced vibration and unstable galloping phenomenon process are investigated in a reduced velocity (Ur=U/ωn·D) range of4≤Ur≤20with load resistance ranging in100 Ω≤R≤1 MΩ. The vortex-induced vibration covers presynchronization, synchronization, and postsynchronization branches. An aeroelectromechanical model is given to describe the coupling of the dynamic equation of the fluid-structure interaction and the equation of Gauss law. The effects of load resistance are investigated in both the open-circuit and close-circuit system by a linear analysis, which covers the parameters of the transverse displacement, aerodynamic force, output voltage, and harvested power utilized to measure the efficiency of the system. The highest level of the transverse displacement and the maximum value of harvested power of synchronization branch during the vortex-induced vibration and galloping are obtained. The results show that the large-amplitude galloping at high wind speeds can generate energy. Additionally, energy can be harvested by utilization of the lock-in phenomenon of vortex-induced vibration under low wind speed.

Piezoelectric Clamped Beam Energy Harvester Using Vibration Caused by Centrifugal Force at High Wind Speeds

2019 ◽  
Vol 4 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Da Xiao ◽  
Ying Yang ◽  
Qinlong Shen ◽  
Jiamei Jin ◽  
Yiping Wang
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Centrifugal Force ◽  
High Speed ◽  
Energy Harvester ◽  
Structural Parameters ◽  
Electrical Energy ◽  
Open Circuit ◽  
Wind Speeds ◽  
Peak Output Power

Abstract In this study, a piezoelectric wind energy harvester was vibrated that aims to convert high-speed wind energy into electrical energy using vibrations caused by centrifugal force. Vibrations induced by centrifugal force enabled effective distortion of the piezoelectric clamped beam and thus produced electric charge through the piezoelectric effect. A clamped beam was used rather than a conventional thin cantilever to harvest the wind energy in the proposed harvester. The centrifugal force was introduced by a pair of rotating eccentric turbines that are installed on two ball bearings on both sides of the piezoelectric unimorph. Benefiting from the rotating eccentric masses of these turbines, the harvester is capable of capturing wind energy in high speed wind environments. A prototype was set up to examine the effects of the wind speed and the structural parameters on the electrical output of the harvester. It is found that the harvester worked efficiently with wind applied from the axial directions in a 20–55 m/s speed range and produced a maximum open-circuit voltage of 47.2 V. When connected to an external load of 50 kΩ, the harvester showed a peak output power of 3.69 mW at a wind speed of 55 m/s.

SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

2018 ◽  
pp. 28-50
Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Demand ◽  
Diesel Generator ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

scholarly journals An Optimized Flutter-Driven Triboelectric Nanogenerator with a Low Cut-In Wind Speed

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 366
Author(s):  
Yang Xia ◽  
Yun Tian ◽  
Lanbin Zhang ◽  
Zhihao Ma ◽  
Huliang Dai ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Energy Harvesting ◽  
Wind Energy ◽  
Output Power ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
Triboelectric Nanogenerator ◽  
Vibration Behavior ◽  
Air Speed

We present an optimized flutter-driven triboelectric nanogenerator (TENG) for wind energy harvesting. The vibration and power generation characteristics of this TENG are investigated in detail, and a low cut-in wind speed of 3.4 m/s is achieved. It is found that the air speed, the thickness and length of the membrane, and the distance between the electrode plates mainly determine the PTFE membrane’s vibration behavior and the performance of TENG. With the optimized value of the thickness and length of the membrane and the distance of the electrode plates, the peak open-circuit voltage and output power of TENG reach 297 V and 0.46 mW at a wind speed of 10 m/s. The energy generated by TENG can directly light up dozens of LEDs and keep a digital watch running continuously by charging a capacitor of 100 μF at a wind speed of 8 m/s.

Geometric and Material Optimization of Vortex-Induced Vibration Micro Energy Harvesters for Localized Wind Environments

2012 ◽  
Author(s):  
Jesse J. French ◽  
Colton T. Sheets
Keyword(s):  
Energy Harvesting ◽  
Power Output ◽  
Fluid Velocity ◽  
Frequency Variation ◽  
Vortex Induced Vibration ◽  
Energy Harvesters ◽  
Wind Speeds ◽  
Material Optimization ◽  
Piezoelectric Energy ◽  
Wind Velocities

Wind energy capture in today’s environment is often focused on producing large amounts of power through massive turbines operating at high wind speeds. The device presented by the authors performs on the extreme opposite scale of these large wind turbines. Utilizing vortex induced vibration combined with developed and demonstrated piezoelectric energy harvesting techniques, the device produces power consistent with peer technologies in the rapidly growing field of micro-energy harvesting. Vortex-induced vibrations in the Karman vortex street are the catalyst for energy production of the device. To optimize power output, resonant frequency of the harvester is matched to vortex shedding frequency at a given wind speed, producing a lock-on effect that results in the greatest amplitude of oscillation. The frequency of oscillation is varied by altering the effective spring constant of the device, thereby allowing for “tuning” of the device to specific wind environments. While localized wind conditions are never able to be predicted with absolute certainty, patterns can be established through thorough data collection. Sampling of local wind conditions led to the design and testing of harvesters operating within a range of wind velocities between approximately 4 mph and 25 mph. For the extremities of this range, devices were constructed with resonant frequencies of approximately 17 and 163 Hz. Frequency variation was achieved through altering the material composition and geometry of the energy harvester. Experimentation was performed on harvesters to determine power output at optimized fluid velocity, as well as above and below. Analysis was also conducted on shedding characteristics of the device over the tested range of wind velocities. Computational modeling of the device is performed and compared to experimentally produced data.

Fluttering Amplitude Amplification by Utilizing Flapping Moment in Flutter-Driven Triboelectric Nanogenerator

2021 ◽  
Author(s):  
Yi Zhang ◽  
Ka Chung Chan ◽  
Sau Chung Fu ◽  
Christopher Yu Hang Chao
Keyword(s):  
Flow Velocity ◽  
High Speed ◽  
Aerodynamic Force ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Energy Output ◽  
Small Scale ◽  
Triboelectric Nanogenerator ◽  
Peak Value

Abstract Flutter-driven triboelectric nanogenerator (FTENG) is one of the most promising methods to harvest small-scale wind energy. Wind causes self-fluttering motion of a flag in the FTENG to generate electricity by contact electrification. A lot of studies have been conducted to enhance the energy output by increasing the surface charge density of the flag, but only a few researches tried to increase the converting efficiency by enlarging the flapping motion. In this study, we show that by simply replacing the rigid flagpole in the FTENG with a flexible flagpole, the energy conversion efficiency is augmented and the energy output is enhanced. It is found that when the flag flutters, the flagpole also undergoes aerodynamic force. The lift force generated from the fluttering flag applies a periodic rotational moment on the flagpole, and causes the flagpole to vibrate. The vibration of the flagpole, in turn amplifies the flutter of the flag. Both the fluttering dynamics of the flags with rigid and flexible flagpoles have been recorded by a high-speed camera. When the flag was held by a flexible flagpole, the fluttering amplitude and the contact area between the flag and electrode plates were increased. The energy enhancement increased as the flow velocity increased and the enhancement can be 113 times when the wind velocity is 10 m/s. The thickness of the flagpole was investigated. An optimal output of open-circuit voltage reaching 1128 V (peak-to-peak value) or 312.40 V (RMS value), and short-circuit current reaching 127.67 μA (peak-to-peak value) or 31.99 μA (RMS value) at 12.21 m/s flow velocity was achieved. This research presents a simple design to enhance the output performance of an FTENG by amplifying the fluttering amplitude. Based on the performance obtained in this study, the improved FTENG has the potential to apply in a smart city for driving electronic devices as a power source for IoT applications.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Improving Lidar-Derived Turbulence Estimates for Wind Energy

2016 ◽  
Author(s):  
Jennifer F. Newman ◽  
Andrew Clifton
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Sonic Anemometer ◽  
Turbulence Models ◽  
Volume Averaging ◽  
Turbine Rotor ◽  
Error Reduction ◽  
Power Prediction ◽  
Wind Speeds

Abstract. Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors remained for periods when the mean wind speed was close to the rated wind speed of the turbine and periods when variance contamination had a large effect on the lidar turbulence error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

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