A smart mobile pouch as a biomechanical energy harvester towards self-powered smart wireless power transfer applications

Chandrasekhar, Arunkumar; Alluri, Nagamalleswara Rao; Sudhakaran, M.S.P.; Mok, Young Sun; Kim, Sang‐Jae

doi:10.1039/c7nr00110j

Cited by 54 publications

(32 citation statements)

References 38 publications

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“…The theory of the triboelectric nanogenerator was firstly proposed by Wang [2]. Based on the theory, researchers have developed numbers of triboelectric nanogenerator [3][4][5], which had been widely used in many fields, such as the energy collection [6][7][8][9], industrial sensors [10][11][12][13], medical equipment [14,15], leisure equipment [16][17][18][19], geological monitoring [20,21], and other industrial applications [22,23]. Therefore, the triboelectric nanogenerator brings hope for solving the problems of vibration energy collection and vibration measurement in downhole.…”

Section: Introductionmentioning

confidence: 99%

Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration

Huang²,

et al. 2020

Sensors

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The traditional downhole drilling vibration measurement methods which use cable or battery as power supplies increase the drilling costs and reduce the drilling efficiency. This paper proposes a spherical triboelectric nanogenerator, which shows the potential to collect the downhole vibration energy and measure the vibration frequency in a self-powered model. The power generation tests show that the output signal amplitude of the spherical triboelectric nanogenerator increases as the vibration frequency increases, and it can reach a maximum output voltage of 70 V, a maximum current of 3.3 × 10−5 A, and a maximum power of 10.9 × 10−9 W at 8 Hz when a 10-ohm resistor is connected. Therefore, if the power generation is stored for a certain period of time when numbers of the spherical triboelectric nanogenerators are connected in parallel, it may provide intermittent power for the low-power downhole measurement instruments. In addition, the sensing tests show that the measurement range is 0 to 8 Hz, the test error is less than 2%, the applicable working environment temperature is below 100 degrees Celsius, and the installation distance between the spherical triboelectric nanogenerator and the vibration source should be less than the critical value of 150 cm because the output signal amplitude is inversely proportional to the distance.

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Section: Introductionmentioning

confidence: 99%

Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration

Huang²,

et al. 2020

Sensors

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“…With the development of advanced generation technologies, there has been an enormous increase in the amount of renewable energy generation in recent years [1,2]. As a clean and widely available resource, wind energy has become one of the most popular renewable sources.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Interval Forecasting Based on Confidence Interval Optimization

Zhang

Zang³

et al. 2018

Energies

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Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.

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“…The concept of bandwidth is used to design observer parameters β 01 , β 02 and β 03 , which are configured as (s + ω c ) 3 for two order controlled objects. Therefore, ESO parameters can be designed based on β 01 = 3ω c , 4.5β 02 = 3ω c 2 , 9.46β 03 = ω c 3 , where ω c is the observer bandwidth.…”

Section: Error Analysis Of Esomentioning

confidence: 99%

“…With the growing shortage of resources and environmental pollution problems, the development of new energy becomes inevitable in the electric power industry [3][4][5]. Among the renewable resources, wind power is one of the most potential types of energy for its wide distribution and easy conversion to electricity.…”

Section: Introductionmentioning

confidence: 99%

Active-Reactive Additional Damping Control of a Doubly-Fed Induction Generator Based on Active Disturbance Rejection Control

Liu

Haihang

et al. 2018

Energies

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Large-scale wind power interfacing to the power grid has an impact on the stability of the power system. However, with an additional damping controller of the wind generator, new ways for improving system damping and suppressing the low frequency oscillation (LFO) of power systems can be put forward. In this paper, an active-reactive power additional damping controller based on active disturbance rejection control (ADRC) is proposed. In order to improve the precision of the controller, the theory of data driven control is adopted, using the numerical algorithms for subspace state space system identification (N4SID) to obtain the two order model of the ADRC controlled object. Based on the identification model, the ADRC additional damping controller is designed. Taking a 2-area 4-machine system containing the doubly fed induction generator (DFIG) wind farm as an example, it is verified that the active-reactive additional damping controller designed in this paper performs well in suppressing negative-damping LFO and forced power oscillation. When the operation state of the power system changes, it can still restrain the LFO effectively, showing stronger robustness and better effectiveness compared to the traditional proportional-integral-derivative (PID) additional damping controller.

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A smart mobile pouch as a biomechanical energy harvester towards self-powered smart wireless power transfer applications

Cited by 54 publications

References 38 publications

Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration

Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration

Wind Power Interval Forecasting Based on Confidence Interval Optimization

Active-Reactive Additional Damping Control of a Doubly-Fed Induction Generator Based on Active Disturbance Rejection Control

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