Farklı yükseklikte ölçülen rüzgar hızı verilerine trendsizleştirilmiş salınım analizinin uygulanması
The detrended fluctuation analysis to wind speed data measured at different heights
Yazar: ECE UMUT KAYAALP
Danışman: PROF. DR. KASIM KOÇAK
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Meteoroloji Mühendisliği Ana Bilim Dalı / Atmosfer Bilimleri Bilim Dalı
Derece: Yüksek Lisans, 2022
Konu: Meteoroloji = Meteorology
Dizin: Rüzgar hızı = Wind speed ; Salınım = Oscillation ; İstasyonlar = Stations

Özet:
Rüzgardan enerji üretmek 21. yüzyıl itibariyle insanlık için önemli bir konu olmuştur. Rüzgar enerjisinden enerji üretimi son 35 yılda çok fazla gelişti ve bunun sonucunda daha fazla araştırılmaya başlandı. Her yıl verimi arttırmak için daha fazla çalışma yapılmaktadır.
Trendsizleştirilmiş salınım analizi Peng ve diğ. tarafından bulunmuştur. TSA, verilerin zaman serileri içinde uzun vadede korelasyonları saptamak için trendin seriden çıkarılmasıyla uygulanan bir yöntemdir. TSA, meteorolojide hava tahminlerini daha iyi değerlendirebilmek, veri persistansını ve uzun dönem korelasyonları hesaplamak için kullanılmıştır. Ayrıca DNA ve protein dizileri, kalp atış hızı dinamikleri, nöron artışları, ekonomik zaman serileri gibi çeşitli alanlarda da kullanılmıştır.
Bu çalışmada Vietnam'ın güneyi kıyıları ve orta iç kesimlerinde yer alan 20 farklı istasyondan alınan 10 dakikalık rüzgar hızı verilerine 100m, 80m, 60m ve 40m ölçüm verilerine TSA uygulanmıştır. Yedi istasyon için kullanılan veriler, veri bütünlüğünün, hata değerlerinin, anormalliklerin ve eksiksizliğinin kontrol edilmesi ve doğrulanması, cihazların sinyal göndermediği sürelerin reddi ve rüzgar kanatlarının sürekli aynı sinyali (arıza sensörleri) gönderdiği sürelerin reddedilmesi gibi kalite kontrol prosedürleriyle veriler Alman Uluslararası İş birliği Kurumu (GIZ) tarafından uygun hale getirilerek yayınlanmıştır. Diğer veriler ise ham ölçüm verileri olarak TSA analizi uygulayabilmek için düzenlenmiştir. Yer seviyesindeki rüzgar hızı verilerinin anlaşılması için Avrupa Orta Vadeli Hava Tahmin Merkezi (ECMWF) tarafından üretilen ERA5 veri seti içindeki 10m yükseklikteki saatlik rüzgar hızı verileri kullanılmıştır. Bu veri setlerinde eksik verilerin sayısı veri setinin %5'nden az olan istasyonlar kullanılmıştır.
Çalışmada 100m, 80m, 60m, 40m ve 10m verilerinin log𝐹(𝑛)(𝑠)−log(𝑠) grafikleri çizdirilmiştir. TSA1, TSA2, TSA3, TSA4, TSA5 ve TSA6 için ölçekleme üstellerinin α = 0.5 değerinden büyük olduğu görülmüştür. Grafiklerde iki ayrı crossover olduğu görülmüştür. Bu da rüzgar hızı verilerinin multifraktal olduğunu göstermiştir. Bunun sonucunda da tüm TSA değerleri için 2 adet ölçekleme üsteli (α1) ve (α2) hesaplanmıştır. TSA3 ve TSA4 arasındaki ölçekleme üstelleri en yakın değerler olduğundan dolayı TSA4'ün verdiği ölçekleme üstelleri seçilmiştir. Bunun sonucunda da 100m, 80m, 60m, 40m ve 10m değerlerinin uzun dönem korelasyona sahip olduğu görülmüştür. Çalışmada 100 metre rüzgar hızı ölçüm verileri ile ERA5 reanaliz verileri karşılaştırılmış ve crossover olgusu incelenmiştir. ERA5 verilerinde 6 saatlik periyotlarda crossover gerçekleşirken ölçüm verilerinde ise 2 saatlik periyotlarda crossover gerçekleşmiştir. İstasyonlardaki topoğrafik etkiler göz önünde bulundurularak deniz uzaklığına göre dağlık ve kıyı bölgeleri olarak iki şekilde (α1) ve (α2) ölçekleme üstelleri incelenmiştir. Dağlık bölgelerin kıyı bölgelere göre ölçekleme üstel değerleri daha küçük değerler olduğu görülmüştür.

Abstract:
Generating energy from wind has become an important issue for humanity in the 21st century. Wind was widely used for both mechanical power and transportation. Previously, the cost of energy from wind turbines was much higher than from fossil fuels, and this energy was difficult to transport. But with advancing technology, it has become available with government support to allow wind turbines to connect to power grids and to offer incentives to help speed up the deployment of new technology. Energy generation from wind energy has evolved a lot over the last 35 years, and as a result, more research has been published. Every year, more studies are carried out to increase efficiency.
In this study, the horizontal wind speed component at different heights of 20 measurement stations in southern Vietnam was used. The measuring period and measuring heights differ for each station. Vietnam is located east of the Indochina Peninsula in Southeast Asia. The climate of Vietnam, which has more than 3000 km of coastline, is affected by the monsoon peculiar to Southeast Asia. While the South Asian monsoon is effective in the summer months, the East Asian monsoon is effective in the winter months. The dominant effects on synoptic scale winds in tropical Vietnam are the summer and winter monsoons created by temperature differences between the Asian land mass and the surrounding oceans. The south and west winds often resulted in a counterclockwise circulation around south and east Asia caused by the summer monsoon. Winds blowing from the north and east were mainly due to reverse circulation caused by the winter monsoon. There are 7 different climatic zones in Vietnam. These are Northwest, northeast, north delta, north central coast, central highlands, south central coast, and south. The stations where the data used in this study were taken have the south central coast and central highlands climate. Both of these climate regions are under the influence of tropical monsoons. The central mountainous part is a region with dry winters and heavy rainfall in summers due to the higher altitude. The average annual temperature is between 21 and 23 degrees Celsius. In the south central coastal part, the winters are warm and sunny, and the summers are hot and dry due to the westerly winds. Average annual temperatures are between 25 and 27 degrees Celsius.
Detrended fluctuation analysis was found by Peng et al. DFA is a method applied by subtracting the trend from the series to detect long-term correlations within the time series of data. The accuracy of long-term correlations increases as a result of filtering out the trends and long-term oscillations of the data. Trends are caused by external influences and are often assumed to have a monotonous or slow-oscillating behavior. Strong trends in the data can lead to incorrect detection of long-term correlations if not interpreted carefully. The advantage of TSA is that it can eliminate these trends. DFA has been used in meteorology to evaluate weather forecasts better and calculate data persistence and long-term correlations. DFA has also been used in various fields such as DNA protein sequences, heart rate dynamics, neuron amplification, and economic time series.
In this study, DFA was applied to the 10-minute wind speed data of 100m, 80m, 60m and 40m measurement data for 20 different stations located on Vietnam's southern coast and central interior. Seven measurement data from the data made available by the German International Cooperation Agency (GIZ) were used in this study. The datasets were cleaned considering the following; quality control procedures such as data integrity, error values, checked and verified anomalies, denial of periods when devices do not send signals, and periods when wind blades repeatedly send the same signal (failure sensors). Other datasets are arranged to be able to apply DFA analysis as raw measurement data. The European Medium-Term Weather Forecast Center (ECMWF) has produced a re-analysis ERA5 dataset from 1979 to the present. ERA5 data provides hourly forecasts of numerous atmospheric, terrestrial and oceanic climate variables. In order to understand the surface wind speed regarding long-term correlation, hourly wind speed data at 10 meter altitude in the ERA5 dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) were added to the analysis. Also, 100 meter ERA5 reanalysis data were used to compare the masurement data at 100 meter. Stations with less than 5% of the missing data were used in these data sets.
This study includes the log𝐹(𝑛)(𝑠)−log(𝑠)graphs of wind speed data at 100m, 80m, 60m, 40m and 10m. Scaling exponents for DFA1, DFA2, DFA3, DFA4, DFA5 and DFA6 were found to be greater than α = 0.5. As a result, it has been observed that the values of 100m, 80m, 60m, 40m and 10m have long-term correlations.
Telesca et al. (2016) applied MTSA to 10-minute wind speed data for 6 different stations in their study. In their study, they found that the wind speed is monofractal or weakly multifractal in short time scales, and multifractal in long time scales, according to the (α) values found in the time series. In this study, it has been obviously observed that there are two separate crossovers in the graphs. This situation shows that wind speed has multifractal behavior at long timescales. As a result, 2 scaling exponents (α1) and (α2) were calculated for all DFA values. Since the scaling exponents between DFA3 and DFA4 are the closest values, the scaling exponents given by DFA4 were chosen.
In the study, 100 meters wind speed measurement data and ERA5 reanalysis data produced by ECMWF were compared and the crossover phenomenon was examined. It has a lower log(s) value than the crossover measurement data. In other words, when the antilogarithm of log(s) is taken, the crossover phenomenon occurs in approximately 6 hour periods. When the antilogarithm of the log(s) is taken in the graphs of the measurement data at 100 meters, the crossover phenomenon occurs in approximately 2 hour periods. The fact that the measurement data is in 10-minute periods and the ERA5 data is in 1-hour periods is the reason why the crossover phenomenon occurs earlier. As a result of all these, the application of the DFA method to the measurement data provided more detailed results than the application to the ERA5 reanalysis data.
Considering the topographical effects at the stations, the scaling exponents (α1) and (α2) were examined in two ways as mountainous and coastal regions according to sea distance. It has been observed that the scaling exponential values of the mountainous regions are smaller than the coastal regions. These two regions are under the influence of monsoon climate. When the DFA method was applied to the wind speed data used in the study as mountainous and coastal regions, α1 and α2values were always smaller in mountainous regions than in coastal regions at 100, 80, 60, 40 and 10 meters.
Measurement station data in mountainous areas and ERA5 reanalysis data at 10 meters show a correlation with large oscillations followed by large oscillations, small oscillations followed by small oscillations. It showed a similar result to Brownian approximation in the wind data of 100, 80 and 60 meters altitude in coastal areas.
It has been calculated that the severity of the crossover phenomenon increases through from 100 meters to 10 meters. Thus, it has been observed that the wind speed at the lower levels has a more multifractal structure compared to the upper levels. This fractal behavior characterizes wind speed over short and large time scales. In particular, it has been found in previous studies that on short time scales, wind speed is monofractal or weakly multifractal, while on larger time scales it is multifractal. These results need to be calculated separately on short and long time scales in the future to understand the profiles of wind speed data at different altitudes.