Laboratory experiment on using non-floating body to generate electrical energy from water waves

Amarkarthik, Arunachalam; Chandrasekaran, S.; Sivakumar, K.; Sinhmar, Harender

doi:10.1007/s11708-012-0210-1

Cited by 13 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1). The general working principle of the proposed innovative concept is published in the previous paper [10]. This particular work contributes specifically on the energy harvesting competence.…”

Section: Experimental Methods and Systemsmentioning

confidence: 99%

Performance of artificial neural network in prediction of heave displacement for non‐buoyant type wave energy converter

Santhosh¹,

Jegadheeswaran²,

Amarkarthik³

et al. 2016

IET Renewable Power Generation

View full text Add to dashboard Cite

Non‐buoyant type of wave energy converter is an innovative method to harness ocean waves. In this paper the assessment of heave displacement for non‐buoyant type wave energy converter is investigated by means of artificial neural networks (ANNs). The significant water wave amplitude and time period are chosen as a basis for the heave displacement, and thus these two parameters are considered as effective parameters for the development of ANN model. For this purpose a wide range of dataset of about 4500 data (water wave amplitude and time period (seconds) is considered as input parameter and corresponding heave amplitude of non‐buoyant body is considered as output parameter) is obtained from an extensive laboratory campaign and is used to develop an ANN. The developed model has the capability of predicting the position of non‐buoyant five seconds ahead of actual heave displacement. The AI technique chosen for the model is a nonlinear autoregressive network with exogenous inputs (NARX) trained with the Levenberg‐Marquardt algorithm. A comparative study of 30 different architectures is carried out and finally the best performing ANN architecture is selected and successfully validated based on the measured data.

show abstract

Section: Experimental Methods and Systemsmentioning

confidence: 99%

Performance of artificial neural network in prediction of heave displacement for non‐buoyant type wave energy converter

Santhosh¹,

Jegadheeswaran²,

Amarkarthik³

et al. 2016

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…A number of wave energy devices are available and research is being done for its commercialization (6) . The wave energy can be converted into electrical energy by various methods as shown in Figure 1 below (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) . https://www.indjst.org/ The point absorber is mostly used to harness wave energy.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Buoy Geometries for Wave Energy Generator

Khankal¹,

Edlabadkar²,

Minase³

et al. 2021

IJST

View full text Add to dashboard Cite

Objectives/Methods: The main objective here is to make an optimum design of buoy. Wave power generation is one of the extensive research fields all over the world. The ocean waves are usually converted into electrical energy by a device called a wave energy converter (WEC). The electricity generated depends upon the intensity of the waves. In a traditional way, the motion of a buoy that occurred in a vertical direction due to waves in water, can be converted into current generation. Here, the numerical simulation using the Ansys tool is done to find an optimum design of buoy shape to get maximum energy conversion. Findings: The simulations have shown that the drag force for the Oval shape is minimum 0.063 N and the first mode of frequency for the Conical shape is minimum 165 Hz for Point-Absorber type Wave Energy Converter.

show abstract