A new ımage-processıng based approach for solar radıatıon forecastıng

Güneş kaynağının kesikli ve değişken yapısı, enerjinin verimli bir şekilde kullanımını oldukça zorlaştırmaktadır. Bu sıkıntıların üstesinden gelebilmek ve güneş enerjisinden etkin bir şekilde faydalanabilmek amacıyla güneş ışınımı tahmini gibi günümüze kadar farklı birçok yöntem kullanılmıştır. Bu çalışmada, gün içinde oluşan bulut hareketlerini takip ederek gelecekte gerçekleşecek bulut hareketlerini tahmin eden, ardından elde edilen bulut hareketi tahmini ve atmosfer dışı güneş ışınımı verilerin kullanılmasıyla güneş ışınımı tahmini gerçekleştiren derin öğrenme yaklaşımı geliştirilmiştir. Bu kapsamda, Afyon Kocatepe Üniversitesi Güneş ve Rüzgâr Uygulama ve Araştırma Merkezine kurulan deney düzeneği aracılığıyla belirli aralıklarda toplanan gökyüzü görüntüleri ve ışınım verileri kullanılmıştır. Sıralı gökyüzü görüntülerinde bulut hareketleri Shi-Tomasi ve Lucas-Kanade yöntemleri kullanılarak takip edilmiştir. Görüntüler üzerinde bulut, gökyüzü, güneş tespitleri ise kırmızı/mavi oranı ve K-means kümeleme yönteminden oluşan hibrit bir tespit yaklaşımıyla gerçekleştirilmiştir. Son olarak, 5 dakikalık zaman ufku için 10 saniye çözünürlüklü güneş ışınımı tahminleri gerçekleştirilmiş ve yaklaşımın performansı test edilmiştir.The intermittent and variable nature of the solar source makes it very difficult to use energy efficiently. In order to overcome these problems and benefit from solar energy effectively, many different methods such as solar radiation estimation have been used until today. In this study, a deep learning approach has been developed that predicts future cloud movements by tracking the cloud movements that occur during the day and then performs solar radiation forecasting using the obtained cloud movement forecast and extraterrestrial solar radiation data. In this context, sky images and radiation data collected at specific intervals through the experimental setup established at Afyon Kocatepe University Sun and Wind Application and Research Center are used. Cloud motions in sequential sky images are followed using Shi-Tomasi and Lucas-Kanade methods. Cloud, sky, and sun detections on the images are performed with a hybrid detection approach consisting of red/blue ratio and K-means clustering method. Finally, solar radiation estimates with a resolution of 10 seconds for the time horizon of 5 minutes are performed, and the performance of the approach is tested

CLOUD TYPE CLASSIFICATION IN GROUND-BASED SKY IMAGES WITH DEEP LEARNING

Clouds cover more than half of the Earth\u27s surface and are the subject of intense research in climate modeling, weather forecasting, meteorology, solar energy research, and satellite communications. Determination of cloud types and characteristics is of great importance in developing and applying solar radiation forecasting models. Therefore, classifying clouds into different categories according to their optical properties is essential for developing solar radiation forecasting algorithms. In this study, we have tried to develop a more efficient, reliable, and cost-effective solution for cloud classification. In this context, a deep-learning CNN model that can classify six different cloud types is developed, and its performance and applicability are examined. The SWIMCAT-EXT dataset, available for research activities, is used for training and testing the model. The experimental results show that the proposed CNN model can successfully classify cloud types and can be integrated into the solar radiation forecasting process

AN EXPERIMENTAL SETUP FOR INSOLATION FAULT DETECTION OF XLPE CABLES

Insulation coordination is one of the most critical issues in high-voltage electrical installations. The smallest defect in the insulation will cause major problems in the near future. Timely insulation failure diagnosis can prevent life-threatening occupational accidents. Insulation fault diagnostics create maintenance costs for the business but prevent much larger costs that are highly likely to occur in the long run. This paper presents an experimental setup to detect insulation problems of XLPE cables. The experimental setup is composed of a coupling capacitor, voltage divider circuit, High-Voltage transformer, coupling device, and coaxial cable. The specimen of XLPE cable is connected from the high-voltage side of the transformer and the data is collected from the measuring device connected at the end of the coupling device. The paper presents different test results for different XLPE cable specimens tested on the presented system. It is possible to test different medium or high-voltage cables using the presented experimental setup