Experimental investigation and ANN modeling on improved performance of an innovative method of using heave response of a non-floating object for ocean wave energy conversion

Chandrasekaran, S.; Amarkarthik, Arunachalam; Sivakumar, K.; Selvamuthukumaran, Dhanasekaran; Sidney, Shaji

doi:10.1007/s11708-013-0268-4

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Obtaining the optimal electrical load for the incoming wave is a crucial task and there are different methodologies are available in machine learning and artificial intelligence such as Artificial Neural Network (ANN), Random Forest (RF) etc., to predict the load condition for the incoming incident wave based on the previous conditions. In a work the ANN is implemented to find out the response of the buoy for various parameters is predicted and the values are experimentally validated [8]. In another work the power generation analysis of the WEC is performed in the Computational Neural Network (CNN) instead of ANN and the conditioning monitoring is done for fault diagnosis in the system [9].…”

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confidence: 99%

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

Nagulan

2023

tjjpt

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The wave energy converters involved in ocean wave energy harvesting are subjected to various natural calamities and thus obtaining the optimal energy extraction is a challenging task. It is normally noted that the energy extraction is maximum when the frequency of the ocean waves matches the natural frequency of the device. The natural frequency of the converter can be modified by tuning its dynamics, but it is intricate to perform in the ocean environment, so an alternative method should be adopted. In this work, the optimal electrical damping is performed to modify the frequency of the system to match the natural frequency of the incident wave. The grey relation analysis is carried out to find out the optimal damping value. Further, the obtained results are subjected to experimental verification and it is observed that the energy extraction is significantly improved. This approach is less complex and effective when compared with the other methods.

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Section: Figure 1 Types Of Wec [3]mentioning

confidence: 99%

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

Nagulan

2023

tjjpt

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“…The most prevalent techniques are the perturbation and observation (P&O) algorithm [3,4], incremental conductance (IC) [5,6], fuzzy logic [7,8] and artificial neural networks (ANN) [9][10][11]. P&O and IC can track the MPP all the time, regardless of the atmospheric conditions, type of PV panel, by processing real values of PV voltage and current.…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, multilayer perceptron (MLP) neural networks or radial basis function network (RBFN) are employed for modeling PV module and MPPT controller in PV systems [10][11][12][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

et al. 2015

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In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.Keywords photovoltaic system, maximum power point (MPP), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA)

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Comparison of modeling methods for wind power prediction: a critical study

Shetty

Sathyabhama

Pai³

2018

Front. Energy

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Experimental investigation and ANN modeling on improved performance of an innovative method of using heave response of a non-floating object for ocean wave energy conversion

Cited by 8 publications

References 13 publications

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

Comparison of modeling methods for wind power prediction: a critical study

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