Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System

Pazikadin, Abdul Rahim; Rifai, Damhuji; Ali, Kharudin; Mamat, Nor Hana; Khamsah, Noraznafulsima

doi:10.3390/s20236744

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…As presented in Section 2.3.2, the ANN architecture parameters are optimized using the genetic algorithm. Based on the objective function presented in (10), the optimized number of neurons in the hidden layer is 28 neurons, whereas the optimized activation functions are the purlin for the neurons in the hidden layer and the logsig for the neuron in the output layer, while the optimized learning algorithm is the traingdm [40]. To show the effectiveness of the GA-ANN based MPPT, its response of the output DC power compared to these of the conventional methods, is presented in Figure 13.…”

Section: Application Of Ga-ann Mppt Methodsmentioning

confidence: 99%

“…The MPP tracking (MPPT) is considered a control unit to preserve the output DC generated power at the maximum rate at numerous environmental and load circumstances [4][5][6][7]. A major benefit of efficiency improvement of PV is to minimize the overall operating cost of using these distributed generations (DG), which is a challenge towards the effective utilization of renewable DG units [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Ali

Mahmoud

Lehtonen

et al. 2021

Sensors

115

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This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

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Section: Application Of Ga-ann Mppt Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Ali

Mahmoud

Lehtonen

et al. 2021

Sensors

115

View full text Add to dashboard Cite

show abstract

“…Fuzzy logic regression may be especially helpful in circumstances in which the link between the variables that are input and those that are output is not well defined or in which the data is intrinsically uncertain or imprecise [162], [163]. Fuzzy logic regression, for instance, can be a flexible and interpretable method of modeling that can be utilized in applications such as climate modeling, economic forecasting, or medical diagnosis, all of which involve inputs that may be qualitative or uncertain [164], [165]. It is essential to remember that fuzzy logic regression might only sometimes be the most suitable option for every single regression problem.…”

Section: ) Defuzzificationmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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“…Outdoor light illuminance can reach values of up to 130,000 lux, depending on location, climate and elevation. Countries located on the equator receive a daily average of 7.7 kW/m 2 of solar radiation between September and December [48]. In the CIE 62471 risk classification, irradiance is an important radiometric parameter to assess the level of harmful radiation produced by a light source [49].…”

Section: Retinal Impactmentioning

confidence: 99%

Blue Light and Eye Damage: A Review on the Impact of Digital Device Emissions

Hipólito,

Coelho

2023

Photonics

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The pandemic and lockdown caused by COVID-19 accelerated digitalization. Personal digital devices, emitting high-energy light, namely in the blue wavelength, have raised concerns about possible harmful effects on users’ eyes. Scientific research history has shown a relationship between exposure to blue light and changes in ocular structures. The main goal of this review is to examine frequent and prolonged exposure to blue radiation from computers, tablets and smartphones and its consequences on vision and ocular structures. Bibliographic research was carried out on changes induced by blue light in ocular structures, the cornea, the crystalline lens and the retina based on the following scientific databases: BioOne Complete™; Google Scholar™; Paperity™; PubMed™; and ScienceOpen™. The most significant studies on blue light and ocular damage were selected and reviewed. The most relevant bibliographic data were analyzed and summarized and some gaps in the theme of blue light from digital devices were identified. The experimental need to acquire additional new data is suggested. The hypothesis that continued use of digital devices enriched with blue light may interfere with the biological tissues of the cornea, crystalline lens, or retina is not clarified in the available scientific evidence. Therefore, additional studies are needed to answer this problem.

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Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System

Cited by 5 publications

References 29 publications

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Artificial intelligence applications in solar energy

Blue Light and Eye Damage: A Review on the Impact of Digital Device Emissions

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