TurkeyWind energy has an important place in renewable energy sources. Biggest challenges in wind energy production are the variability of the wind and difficulty of estimation of true wind speed. In this study, 25,777 records have been taken from wind measurements carried out at Mehmet Akif Ersoy University campus. Records include meteorological data such as wind speed at different heights/altitudes, wind direction, temperature, pressure and humidity. In order to estimate the wind speed that may occur at 61 m altitude, multilayer perceptron and radial basis function methods have been used. During the application phase, 100 artificial neural networks were trained and performance evaluations of these networks were done. The obtained results show that the wind speed at 61 m can be estimated with 99% accuracy using artificial neural network when other meteorological data are taken as input.
Bu çalışmada güneş paneli (PV) yüzeyi soğutma işleminin elektriksel verime olan etkisi araştırılmak istenilmiştir. Bu maksatla özdeş her biri 50cmx100cm olan ve 80W güçte iki adet PV Osmaniye Korkut Ata Üniversitesi Mühendislik Fakültesi'ne montajlanmıştır. Yan yana ve eş açılarla montajlanan panellerden birinin üst kısmına suyu homojen bir şekilde gönderebilecek bir boru sistemi yerleştirilmiştir. Boru içerisinden belirli periyotlarda soğuk su gönderilerek PV yüzey alanının soğutulması sağlanmıştır. Her iki durumda da ölçülen akım, gerilim ve güç değerleri kayıt altına alınarak performans kıyaslaması yapılmıştır. Çalışmada PV yüzey sıcaklığı 32oC'den 19oC'ye düşürüldüğünde üretilen elektriksel güç değeri 5 günün sonunda ortalama 79,621W'tan 91,149W'a çıkarılarak %14,47 verim artışı gözlenmiştir.
This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.
Electric vehicle (EV) technology is a new technology aiming to reduce fossil fuel usages. This new technology has some problems including battery charging time and range. One of the problems, battery charging times (level 1 level 2, level 3, and level 4), was investigated in this study. While level 1 and level 2 refer to methods of slow charging, level 3 and level 4 refer to fast charging. Fast charging methods consist of three different topologies: topology with AC/ DC/ DC converters (vehicle-to-grid (V2G) charging stations and one-way charging stations as their subbranches), common AC-connected multi-point topology and transformerless topologies. Figure A. EV Charging Methods Among these topologies, it was concluded that the V2G method is an effective and reliable method in terms of adaptation to renewable energy sources, energy efficient use, easy installation and adaptation to both AC and DC systems. Purpose: It is aimed to determine fastest and the most efficient charging method for EV's. Hence the layout, functionality and importance of EV charging stations have been investigated. Theory and Methods: Topologies of EV charging stations were analyzed and compared in literature from various perspectives. In addition, tables were created to detect variations between charging levels and charging rates. Results: Fast charging stations have been observed to be vital for EV's to adapt to future technologies. The V2G fast charging topology has been observed to have a high energy efficiency and can operate in accordance with other renewable energy sources. Conclusion: Some fast charging stations are viewed as a threat to the electricity grids as having a charging capacity at MW levels. Among the steps that can be taken against this challenge are to reduce the problems caused by the demand for electricity, to preserve the charging station's common link point voltage and to send reactive power to the grid to avoid voltage instability. Another precaution is to use the V2G fast charging method. These stations must also be built in compliance with IEC requirements in terms of user/ network protection, device reliability and energy efficiency.
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