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In this study, the effects of operating conditions of photovoltaic (PV) panels in a power plant installed in Burdur province in Turkey with 8 MW capacity, the output power of the inverter and the power quality in the grid were examined experimentally in January and July. In addition, an artificial neural network (ANN) was utilized to estimate the power at the output of the inverter under different operating conditions and optimal operating conditions were attempted to be determined. In the ANN configuration, the output power generated in PV panels (PDC), radiation intensity, relative humidity and temperature measurements were used as input data, output power (PAC) at the junction of the inverter, power factor (PF) and the frequency values were used as output data. In order to ensure the integration of the energy obtained from the solar power plant by using the obtained results, it was tried to estimate the levels of the factors affecting the inverter output power and efficiency in terms of the proper operating conditions and efficiency. The success level of the ANN results was observed to be above 99% with the experimental results. Keywords: PV panel, Inverter, ANN, Power quality, Distributed generation

In this study, a microchannel heat exchanger with operating with ethylene was proposed instead of air-cooled microprocessor heat sinks and flow and heat transfer simulations were made using Flo-EFD software. First, the temperature distribution of the air domain and the microprocessor in the computer case was obtained for air-cooled heat sink, and then the flow paths of the air coming from the outside with the help of the fan were observed. Afterwards, the system with the proposed micro channel heat exchanger was subjected to the same processes and the results were compared. As a result of the study, it was revealed that the micro channel heat exchanger reduced the processor temperature between 54-55°C and 18-19°C. It has also been observed that removing the heat sink allows the air drawn from the outside environment to circulate more efficiently around other heat generating elements in the computer case. Hence more homogeneous and lower temperature distribution within the computer case was obtained., Bu çalışmada hava soğutmalı mikroişlemcili ısı emiciler yerine etilen ile çalışan mikrokanallı bir ısı değiştirici önerilmiş ve Flo-EFD yazılımı kullanılarak akış ve ısı transferi simülasyonları yapılmıştır. Öncelikle hava soğutmalı soğutucu için bilgisayar kasasındaki hava havmi ve mikroişlemcinin sıcaklık dağılımı elde edilmiş, ardından fan yardımı ile dışarıdan gelen havanın akış yolları gözlemlenmiştir. Daha sonra önerilen mikrokanallı ısı değiştiricisi kullanılan sistem aynı işlemlere tabi tutulmuş ve sonuçlar karşılaştırılmıştır. Çalışma sonucunda mikrokanallı ısı değiştiricinin işlemci sıcaklığını 54-55°C'den 18-19°C'ye düşürdüğü ortaya çıkmıştır. Soğutucunun çıkarılmasının, dış ortamdan çekilen havanın bilgisayar kasasındaki diğer ısı üreten elemanlar etrafında daha verimli bir şekilde dolaşmasına izin verdiği de gözlemlenmiştir. Böylelikle bilgisayar kasası içerisinde daha homojen ve daha düşük sıcaklık dağılımı elde edilmiştir.

Relations between effectiveness (e) and expense coefficients (ζ) were derived, and an economic simulation model was developed to simulate heat exchangers (HE) and HE networks (HEN) in all flow types for the first time. e values of parallel flow, counter flow, cross flow and all HEs under the condition of Cr = 0 were derived in terms of ζ, NTU (Number of Transfer Unit) and minimum heat capacities (Cmin). e values obtained from economic calculations were used for developing economic simulation model of HEs. Vectors including outlet temperatures and inlet temperatures of flows were obtained from static simulation to utilize in economic simulation model. Then, case studies were performed with counter flow HE and e values randomly determined in a sample HEN. Use (N), expense (P) and savings (E) of all HEs in a HEN were calculated easily by the way of linear equation systems without any complex processes, iterations, software and special hardware, in terms of both cold and hot flows properties by using economic simulation model.

To enhance energy absorption of photovoltaics, several etching experiments with various parameters were performed. In addition, an Artificial Neural Network (ANN) simulation was utilized to predict chemical nano etching parameters such as masking and etching durations for Silicon (Si) solar cell applications to reach minimum surface reflectance in an optimum etching duration. Experiments were performed with different masking and etching durations to determine the characteristics of surface reflectance of micro textured n-type single crystalline Si wafers in 25mmx25mm width and 300 µm thickness to provide training data for ANN. For this purpose, solutions with identic properties including Ag nanoparticles were applied with different application durations on the surfaces of n-type single crystalline Si wafers to cover partially the Si surfaces with Ag nano-particles at masking step. After, partially masked Si surfaces were exposed to chemical nano etching to develop nano-sized porous structures under different etching durations in an identic acidic etching solution. For the etching of Si wafers, 32 masking and etching processes were performed. Reflectance measurements and SEM images were evaluated to determine the optimum etching duration resulting the best surface quality with minimum reflectance. In addition, reflectance values were utilized as input data for training, testing and validation steps of developed ANN. In the ANN simulation, 70% of reflectance values were used as training, 15% of reflectance values were used as validation and 15% of reflectance values were used to test data in the ANN. Consequently, surface reflectance values under different masking and etching durations were predicted with the new parameter set by using the trained ANN with a success level above 99%. © 2018 Elsevier B.V.

An artificial neural network (ANN) simulation was utilized to determine the lapping parameters such as rotation speed, lapping duration and lapping pressure under a constant slurry supply for n-type crystalline Silicon (c-Si) wafers. Experiments were done with a Logitech PM5 lapping and polishing machine to obtain input data and target data for training, testing and validation of ANN. Lapping operation had five main parameters affecting surface quality: rotation speed, lapping duration, lapping pressure, flowrate of abrasive slurry and particle size in abrasive slurry. However, in this study slurry flowrate was assumed constant due the researches performed before. 218 lapping operations were performed with different values of the selected parameters and new lapping parameters were derived for different lapping conditions to achieve the best surface quality by using an ANN. In this study, wafers in 400 µm thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (R a ) values were measured three times from different points of the wafers after lapping with a contact type surface roughness measurement tool using a microscopic scale stylus profiler (SP). In ANN simulation 70% of R a values were utilized for training, 15% of R a values were utilized for validation and 15% of R a values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.

Currently, cooling of photovoltaic (PV) panels, a significant issue due the negative effect on panel efficiency, is subjected to intensive researches. For this purpose, numerous researches on cooling methods are performed to keep the panel efficiencies around the design values. In this study, a comprehensive literature review was presented to give the status of the technological improvement in cooling options. In addition, strong and weak aspects of the studies were discussed, and advantageous methods were emphasized for the further application of PV panels. Finally, future perspective of PV panel cooling studies was explored with proper cooling options in lower operating temperatures for higher operation efficiencies.

A temperature increase plays a negative role on photovoltaic (PV) panel conversion efficiency by increasing recombination rates. In this study, air- and water-cooling options were simulated to investigate the efficiency behavior of a specific PV panel made of heterojunction Silicon (Si) whilst PV panel was cooling in operation by using ANSYS-FLUENT. For air cooling, two different options were suggested: air cooling with four different flow speeds and air cooling with a heat sink addition with three different flow speeds. As for water-cooling three flowrates were considered. Temperature distributions of PV panels for the all cooling options were demonstrated as a function of flow velocity of air and flowrate of water for different cooling conditions and compared with each other. The influence of temperature difference on panel conversion efficiency were also discussed. As a result, heat sink with a proper flow arrangement cooling option showed the best performance in terms of minimum material, minimum cost and minimum complexity with the 42 degrees C, 38.4 degrees C, 35.9 degrees C average surface temperatures and 20.9%, 21.3%, 21.5% panel efficiencies.

Crystalline silicon (c–Si) wafer was manufactured by applying micro texture and nano etching process without lapping and polishing for solar cell applications. Before micro texture and nano etching, wafers were exposed to chemical polishing process in high concentrated alkaline solution to remove saw damages and obtain a surface with low surface roughness (Ra). Uniform pyramidal structures were formed in alkaline solution. On pyramidal structures, nano sized porous shapes were formed by using Ag nano particles via wet chemical etching method. Micro textured and nano etched surface formed without any chemical mechanical (CM) lapping and polishing step, examined by Scanning Electron Microscope (SEM) for surface structure and UV–visible spectrometer for reflectance and absorbance measurements. Considerably good uniform porous surface with low reflectance was formed and compared with usual solar wafers produced with CM lapping and polishing. Furthermore, present process for preparing solar cell provides low cost, short time and less material consumption by removing lapping and polishing steps from overall process. Reflectance values were measured between 7 and 10% for best micro textured and nano etched c-Si wafers, respectively. Absorbance values were measured 0.4 and 1.2% for best micro textured and nano etched c–Si wafers, respectively. Curves of $${\text{h}}{\upnu }$$ versus $${\left({\upalpha }{\text{h}}{\upnu }\right)}^{2}$$ indicated the optical band gaps almost 1.12 e.V. for all wafers cut from same n-type single c-Si ingot.

An Artificial Neural Network (ANN) simulation was utilized to predict surface roughness values (R-a) for a Silicon (Si) ingot cutting operation with a Diamond Wire Saw (DWS) cutting machine. Experiments were done on a DWS cutting machine to obtain data for training, testing and validation of the ANN. The DWS cutting operation had three parameters affecting surface quality: spool speed, z axis speed and oil ratio in a coolant slurry. Other parameters such as wire tension, wire thickness, and work piece diameter were assumed as constant. The DWS cutting machine performed 28 cutting operations with different values of the selected three parameters and new cutting parameters were derived for different cutting conditions to achieve the best surface quality by using the ANN. Wafers 400 mu m thick were cut from a n-type single crystalline Si ingot in a STX 1202 DWS cutting machine. R-a values were measured three times from different regions of the wafers. In ANN simulation 70% of R-a values were used as training, 15% of R-a values were used as validation and 15% of R-a values were used to test data in ANN. The ANN simulation results validated training output data with success above 99%. Consequently, the R-a values corresponding to the cutting parameters, and also proper cutting parameters for specific R-a values were determined for DWS cutting using the ANN. (C) 2017 Elsevier Ltd. All rights reserved.