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In this study, a buck converter and a driver has been designed for Linear Switched Reluctance Motor (LSRM) with 6/4 poles, 3 phases, 250W. LSRM controlled by PIC 18F452 microcontroller was used to drive automatic door. Motor was controlled more accurate with linear incremental encoder by sensing acceleration and movement direction of door.

microprocessors and microcontroller programming, serial and parallel active power filters, and photovoltaic systems, photovoltaic irrigating systems, RF control and communications, and distance education material design.

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Özet-Özel firmalara enerji sağlayan firmalar veya işletmeler için enerji tüketiminin tahmini ve ihtiyaç planlaması çok kritiktir. Özellikle endüstri bölgelerindeki enerji ihtiyacı ev kullanıcılarının ihtiyacından daha yüksektir, bundan dolayı enerji ihtiyacının doğru tahminini gerektirir. Bu çalışmada, zaman serileri ve yapay sinir ağları olmak üzere iki farklı yaklaşım kullanılarak Türkiye'deki bir endüstri bölgesi için enerji ihtiyaç tahmini üzerinde çalışılmış ve sonuçlar test edilmiştir. Daha önceki çalışmalardan farklı olarak, kısıtlı veri ile kısa dönem tahmini için basit bir model geliştirilmiştir. Model, giriş parametresi olarak geçmiş günlere ait tüketim verileri ve sıcaklığı içermektedir. Sıcaklık verisi, endüstri bölgelerinde ısıtma amaçlı enerji tüketiminde kullanıldığı için anahtar rol oynamaktadır. Zaman serileri yaklaşımında sadece geçmişe ait enerji tüketim verileri kullanılmıştır. Her iki yaklaşım enerji ihtiyaç tahmininde kullanılmış, sonuçlar tartışılmış ve karşılaştırılmıştır. Elde edilen sonuçlara göre; zaman serileri yöntemi R değeri 0.93901, yapay sinir ağları yöntemi R değeri 0.9859 elde edilmiştir. Zaman serileri yöntemi veri kısıtlığı sebebiyle yapay sinir ağlarına göre daha kötü bir tahmin gerçekleştirmiştir.

Solar-powered irrigation systems are becoming increasingly widespread. However, the initial setup costs of these systems are very high. To reduce these costs, both the energy usage and the prevention of losses from irrigation systems are very important. In this study, a drip irrigation control system of 1000 dwarf cherry trees was controlled using soil moisture sensors in order to prevent excessive water consumption and energy losses in a solar-powered irrigation system. The control system comprises units that are energized with solar panels, and the flow of information is implemented via radio frequency. Thus, by removing the cables that create difficulties in the cultivation of gardens, both the easy cultivation of the soil is enabled and cable costs are eliminated. The portability of the units is enabled by using solar energy; thus, units might be mounted wherever the user wishes. It has been indicated that the moisture of the application area varies depending on the data obtained regarding volumetric water content and the defined area in need of irrigation. In this way, there are reductions in the dependence on instantaneous water demand, the amount of water to be pumped, and the energy requirements needed for irrigation. In addition, pump power and the power of the motor that drives the pump are also reduced. According to the obtained measurement values, it was calculated that hourly water demand decreased by 36%. Thus, the numbers of solar panels, electrical machines, batteries, and units of power control that supply all the energy of the system and constitute a large part of the initial setup costs are reduced at the same rate by means of the applied technique. Thanks to the developed site-specific irrigation system, unnecessary energy consumption was avoided. Along with the elimination of excessive and unnecessary irrigation, problems with trees were also eliminated.

On this study, kinetics of eggplant drying was modeled in the laboratory-scaled Food Drying Dven (FDD) with resistance heater was designed and manufactured. The temperature, energy consumption and drying time of FDD were recorded by keeping the temperature of at different temperatures as 40, 50 and 60 °C. These saved values were chosen as the input parameters of the model. The weight value of the eggplant was taken as the output parameter. Linear and quadratic equations were developed for modeling and constant coefficients of these equations were estimated with Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), symbiotic organisms search (SDS) algorithms. On addition, the performances of these models were compared with the model developed with ANN in terms of performance and time. The results show that the lowest error of the developed linear and quadratic equations was obtained with SDS algorithm. The MSE metric results of ANN were fifty times higher than the performance of SDS algorithm, and the SDS algorithm reached best value three times faster than the ANN.