Shifting based VLSI scaler

Dhanabal, R.; Sahoo, Sarat Kumar; Bharathi,; Varma, Bh.S.R. Phanindra; Divya, V.; Kalyan, D.

doi:10.1109/isco.2015.7282381

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…In [19], [20], the authors compared the performance of single and dual axis tracker. They concluded that both have better performance than a fixed mount panel.…”

Section: Related Workmentioning

confidence: 99%

“…The next appearance of the sun is detected by the third sensor, wh ich sends an interrupt signal to the microcontroller. The interrupt signal toggles the microcontroller automat ically back to tracking mode 1 http://www.atmel.com/ without any human intervention, as also in [19]. The keypad unit provides an option for manual control o f the solar panel, wh ich could either be an anticlockwise or clockwise rotation.…”

Section: A Hardware Implementationmentioning

confidence: 99%

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A Prototype Automatic Solar Panel Controller (ASPC) with Night-time Hibernation

Aliyu¹,

Okwori²,

Onwuka³

2016

IJISA

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Solar cells, as an alternate means of electricity supply, is rap idly advancing. Generally, output of solar cells depends largely on intensity of sun and angle of incidence on the cells. This means that to get maximu m efficiency fro m these cells, they must remain directly pointed at the sun from sun rise to sun set. However, the position of sun"s highest intensity with respect to a given spot changes with time of the day. It is therefore necessary to automatically control position of solar cells to always align with the h ighest intensity of sun. In this paper, we present a prototype automatic solar panel controller, with n ight time hibernation. The proposed system consists of both software and hardware parts, and it automatically provides best alignment of solar panel with sun to get maximu m intensity. The solar panel controller system detects the presence of sun rays using light dependent resistors (LDR). At the heart of the control mechanism is an AT89C52 microcontroller. It is programmed to constantly monitor the output of an LDR, actuate a stepper motor to reposition the solar panel to a direction with the highest intensity. The proposed system also has an option of manual control of the panel via a computer interface or a keypad unit for easy of user interactivity during maintenance. Testing the proposed system, results shows that it can successfully track the sun and enter idle mode in the absence of sun rays, hence, conserving over 50% of energy required to operate the system.

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“…In [19], [20], the authors compared the performance of single and dual axis tracker. They concluded that both have better performance than a fixed mount panel.…”

Section: Related Workmentioning

confidence: 99%

Section: A Hardware Implementationmentioning

confidence: 99%

A Prototype Automatic Solar Panel Controller (ASPC) with Night-time Hibernation

Aliyu¹,

Okwori²,

Onwuka³

2016

IJISA

View full text Add to dashboard Cite

show abstract

“…A solar tracker device is usually applied to direct the solar panel following the movement of the Sun [4]. Studies on the use of solar tracker in a so-lar panel system show an increase in power output in the range of 31% to 82% compared to the fixed solar panel [5][6][7][8]. However, the use of solar tracking entails an additional cost for the system, and therefore the economic factor should be considered [9].…”

Section: Introductionmentioning

confidence: 99%

Machine learning based modeling for estimating solar power generation

Uddin,

Purwanto,

Nugraha

2024

E3S Web Conf.

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The solar power plant is a rapidly growing renewable energy source that has a potential role in reducing climate change and replacing fossil fuels. Estimation of the power generated by a solar power plant is required to determine the energy supply. Unfortunately, the solar power generated is highly uncertain due to highly dependence to nature, such as solar radiation and weather. This makes the estimation of solar power generation to be very difficult. This study presents a development of machine learning to model a solar power plant for estimating the generated power. The machine learning is developed by implementing the k-NN algorithm. A data set of power generated in a solar power plant is applied to build the machine learning. The development resulted in a machine learning that models the solar power plant. Simulation test result show the machine learning was able to estimate the solar power generated with an accuracy of 69.6%. The developed model is very useful to estimate potential of solar power resource in a region. The developed model is very useful in feasibility studies to estimate the potential of solar power resources in an area.

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Shifting based VLSI scaler

Cited by 2 publications

References 10 publications

A Prototype Automatic Solar Panel Controller (ASPC) with Night-time Hibernation

A Prototype Automatic Solar Panel Controller (ASPC) with Night-time Hibernation

Machine learning based modeling for estimating solar power generation

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