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SAĞLAM, ŞAFAK

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SAĞLAM

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ŞAFAK

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2022 - 20243
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    PublicationOpen Access
    A Hydrophobic antireflective and antidust coating with $\text{SiO}_2$ and $\text{TiO}_2$ nanoparticles using a new 3-D printing method for photovoltaic panels
    (2022-07-01) EKREN, NAZMİ; SAĞLAM, ŞAFAK; Ekren N., Sarkin A. S., Sağlam Ş.
    The main outdoor factors that reduce the efficiency of the photovoltaic (PV) panel are the reflection and refraction of light, dirt, dust, and organic waste accumulating on the panel surface. In this article, an antireflection, self-cleaning coating was applied on the PV panel cover glass with a new method. With the coating, the surface has been given a hydrophobic feature. As a coating method, a 3-D printer has not been seen in the literature and used as a new method. The electrospinning method has also been tried as an alternative method. Solutions in different combinations were developed using polylactic acid or polymethylmethacrylate polymer, chloroform ($\text{CHCl}_3$) as a solvent, and silicon dioxide ($\text{SiO}_2$) and titanium dioxide ($\text{TiO}_2$) nanoparticles as primary materials in a modified 3-D printer for bioprinting. Five PV panels were obtained by applying different 3-D parameters from three solutions, which have the best results. Coating thicknesses are in the range of 3.12-8.47 mu m. Coated and uncoated PV panels were tested in outdoor conditions for ten-day periods. The power outputs of the PV panels were measured, and their ten-day average efficiency was presented. According to the results, the highest efficiency increase is 8.7%. The highest light transmittance is 88.2% at 550 nm. In addition, hydrophobic properties were observed on all surfaces and the water contact angle was measured as 96.18 degrees.
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    PublicationOpen Access
    Investigation of Failures during Commissioning and Operation in Photovoltaic Power Systems
    (2024-03-01) SAĞLAM, ŞAFAK; ORAL, BÜLENT; Gökgöz M., Sağlam Ş., Oral B.
    Considering global warming and environmental problems, the importance of renewable energy sources is increasing day by day. In particular, the effects of wind and solar power, which are variable renewable power sources, on the power system necessitate their evaluation in terms of the reliability of the power system. Photovoltaic panels, which enable the conversion of solar power into electrical power with semiconductors, have started to take an important place in global energy investments today. Photovoltaic power plants increase the demand for this energy source with continuous energy conversion depending on sunshine duration and radiation intensity. Among the renewable energy sources, the most easily utilized energy source, regardless of geographical conditions, is the sun. To prevent the energy production of PV power plants from being interrupted, it is necessary to address and analyze all kinds of faults that will affect power production in order to increase the reliability of the system. Academic studies in this field are generally grouped under two topics: classification of faults or modeling of electrical faults. Based on this, in this study, the problems that occur during the installation and operation of photovoltaic systems are classified, and the relevant faults are modeled and simulated in MATLAB Simulink version 23.2 (R2023b). Thus, a scientific approach to the problems of photovoltaic power plant operating conditions has been gained, which will be the basis for academic studies.
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    PublicationOpen Access
    A review of short-term wind power generation forecasting methods in recent technological trends
    (2024-12-01) SAĞLAM, ŞAFAK; ORAL, BÜLENT; Arslan Tuncar E., Sağlam Ş., Oral B.
    Climate change and the escalating demand for energy are among the most pressing global challenges of our era. Renewable energy sources, such as wind energy, are considered a viable solution to these issues. However, the integration of renewable energy sources into electric power systems also presents operational challenges, particularly in terms of uncertainty. In order to mitigate this uncertainty, it is crucial to improve the accuracy of generation forecasting methods for wind energy. This review explores various wind power forecasting methods, categorizing them by factors such as time frame, and model structure. Special attention is given to short-term forecasting, crucial for the day-ahead electricity market. This study traces the evolution of wind power forecasting, from early statistical approaches to the integration of numerical weather prediction, machine learning, neural networks, and advanced techniques. Its aim is to provide valuable insights into wind power forecasting methods for stakeholders, including grid operators, traders, and wind farm operators. This review serves as a vital resource for researchers and industry professionals navigating the dynamic field of wind power forecasting, contributing to effective renewable energy resource management in a rapidly evolving energy sector.