Modeling and Economic Analysis of Greenhouse Top Solar Power Plant with Pvsyst Software

SANCAR, Muhammet Raşit; Bayram, A.

doi:10.47933/ijeir.1209362

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The main reason for the DM noise is that the circuit components in the device interact with each other and create unwanted voltage differences. DM current is half of the difference between phase and neutral line currents as given in Eq (1).…”

Section: Differential Mode Noisementioning

confidence: 99%

“…Importance of power electronics, grid technology and solar power systems is increasing with carbon problems and global warming [1,2]. Due to the increasing importance of electric vehicles and the importance of smart grid technologies, high-power systems have to work together with sensitive communication technologies and complex control mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Analyses of EMI Noise Separator for CISPR25

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Kesler

et al. 2023

International Journal of Engineering and Innovative Research

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In energy transmission systems, devices must be able to work safely with each other. Therefore, electromagnetic emission and susceptibility of the converting systems are expected to be in a certain range. electromagnetic propagation, especially by radiation and conduction, cannot be neglected in power electronics such as electric vehicle, microgrid, aerospace technologies. In order to examine and reduce the noise emitted by the devices, the noise should be separated into common mode and difference mode components. In this study, Shou Wang modeling, which is used to separate the noise components into related components, has been examined in order to analyze the noise components within the framework of the CISPR25 standard used for electric vehicles and redesigned. Circuit simulation and design were done and the results were compared. Thanks to these results, it has been seen that the circuit works efficiently up to 90MHz and gives 8dB S11 reflection parameter at the 108MHz threshold level. In addition, the separator circuit was used in a bidirectional DC-DC converter with a known EMI level of 1KW and the noise components obtained were compared with the components of the converter. As a result, it has been seen that the results obtained using the noise separator circuit are almost the same as the real results. In the 18 - 42 MHz range, only the results obtained with the noise separator are up to 8dBuV higher than the real results, other than that the results are exactly the same.

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Section: Differential Mode Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Analyses of EMI Noise Separator for CISPR25

Yalçın

Göksu

Kesler

et al. 2023

International Journal of Engineering and Innovative Research

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“…Renewable energy such as solar energy (Sancar & Yakut, 2023) (Sancar & Bayram, 2023) and electric vehicle technologies such as battery management system (Krismer & Kolar, 2009) have been widely studied. the intersection points of these topics are usually converters that can transmit bidirectional energy.…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Systems and Power Electronics: Advances, Applications, and Integration

2023

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Dear Readers, Renewable Energy Systems and Power Electronics: Advances, Applications, and Integration, aims to provide a comprehensive resource on renewable energy systems and power electronics. Meeting the energy demand in a sustainable and environmentally friendly manner has become increasingly important in today's world. Therefore, renewable energy-based systems and power electronics technologies have garnered significant attention. This book consists of articles written by expert authors in the field. The authors address various topics, including solar energy, thin film deposition techniques, ultrasonic spray pyrolysis technique, dual active bridge DC/DC converters, and other important subjects. The book aims to serve as a valuable reference for students, researchers, and industry professionals who wish to grasp the fundamentals of these topics. The first section of the book provides a general overview of renewable energy sources. Solar energy, thin film deposition techniques, and other current topics are discussed in detail. Subsequently, the ultrasonic spray pyrolysis technique and its parameters, CDTE thin film solar cell structures and production methods, and other related subjects are examined. Additionally, the working principles and applications of emerging technologies such as Dye Sensitized Solar Cells (DSSCs) and Dual Active Bridge DC/DC Converters are also addressed. I believe that this book will provide readers with a broad perspective on renewable energy systems and power electronics. Each chapter contains detailed information to provide an in-depth understanding. Moreover, current research and applications are taken into account when discussing each topic. Lastly, I would like to express my gratitude to all the authors who contributed to the creation of this book. This work has been made possible through their willingness to share their knowledge and experience in the field of renewable energy systems and power electronics. In conclusion, I hope that this book will serve as a valuable resource for anyone interested in renewable energy systems and power electronics. Enjoy your reading. Best regards,

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RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

ÇELEBİ,

FİDAN

2024

International Journal of Engineering and Innovative Research

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One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

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Modeling and Economic Analysis of Greenhouse Top Solar Power Plant with Pvsyst Software

Cited by 3 publications

References 18 publications

Experimental Analyses of EMI Noise Separator for CISPR25

Experimental Analyses of EMI Noise Separator for CISPR25

Renewable Energy Systems and Power Electronics: Advances, Applications, and Integration

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

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