Cost effective methods to improve the power output of a solar panel: An experimental investigation

Gopinath, M.; Balaji, R.; Kirubakaran, V.

doi:10.1109/pestse.2014.6805282

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…One apparent trouble is the inherited deviation of weather, making accurate weather forecasting challenging (Sassi & Oulamara, 2017). Many technologies are available for forecasting, including mathematical calculations based on weather data, artificial intelligence-based techniques, and a combination of both methods (Gopinath et al, 2014). This paper has attempted to employ a controller to predict power output.…”

Section: Introductionmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

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Section: Introductionmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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“…It was first introduced in 1958 for the purpose of increasing the collection area of the incident solar radiation. Consequently, there is a potential for enhancing the efficiency of the PV [ [37] , [38] , [39] , [40] , [41] ]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Development of assessment methods for photovoltaic module enhancing techniques using the lifespan parameter

M. Sultan,

Tso,

Sopian

et al. 2023

Heliyon

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“…Another reflective model proposed by other researchers with the use of two mirrors at the bottom and top without changing the tilt angle, was able to achieve a 15% increase in power output [14], we notice that the size of the two mirrors and their inclination compared to the photovoltaic panel makes the model voluminous, which will complicate the installation of several models in series. The quarter mirror model was thus proposed by researchers who were able to increase the efficiency of the photovoltaic panel by around 30% [15]. According to these studies, the researchers introduced to the photovoltaic panel a system of reflections without follow-up, that is to say they kept the whole model in fixed mode in order to increase the production of energy without consumption, this increase remains limited.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Enhancement of a Fixed Photovoltaic Panel Using Simple Motorized Reflection Model

Bouzakri¹,

Abbou²,

Chennoufi³

2021

IJIES

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The optimal installation of a photovoltaic panel in fixed mode, allows us to receive the maximum of solar radiation at noon, when the sun is in front of the panel, although the output drops in the morning and afternoon. In order to maintain this efficiency as much as possible, the photovoltaic panel must be oriented by a solar tracker. However, the introduction of such a mechanism makes the installation expensive and complicated during maintenance, especially since a part of the energy produced by the panel will itself be consumed for its operation. This paper proposes a simple and less expensive system allowing the photovoltaic panel in fixed mode to maintain its efficiency almost at the maximum all day long with the introduction of a simple reflection system based on two reversible mirrors at noon. After a theoretical study and on the basis of a simulation, the proposed system will have an efficiency of more than about 35% compared to that in fixed mode and more than 90% compared to that in tracking mode, these results will be validated via an experiment at the end of the document with a prototype produced.

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Cost effective methods to improve the power output of a solar panel: An experimental investigation

Cited by 5 publications

References 2 publications

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Development of assessment methods for photovoltaic module enhancing techniques using the lifespan parameter

Efficiency Enhancement of a Fixed Photovoltaic Panel Using Simple Motorized Reflection Model

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