Enterkonnekte güç sistemlerinde rüzgar enerji sistemlerinin güç akış analizine etkilerinin modern optimizasyon teknikleri ile incelenmesi

Tez (Doktora) -- Kırıkkale Üniversitesi108705

Investigation of the effects of wind energy systems on power flow analysis in interconnected power systems by modern optimization techniques

YÖK Tez ID: 430031Rüzgar Enerji Sistemlerinin (RES), diğer güç sistemlerine bağlantılarında, güç sisteminin kapasitesinin artması dolayısıyla güç akış analizinde kullanılan geleneksel optimizasyon teknikleri, verim ve etkinliklerini kaybetmektedir. Bu amaçla bu tezde, RES bağlantısı olan ve olmayan örnek güç sistemleri üzerinde, yakıt maliyeti, emisyon oranları ve algoritma zamanı gibi güç akış analizini belirleyen parametreler, yeniden tasarlanan Genetik-Öğretme Öğrenme Tabanlı Optimizasyon (G-ÖÖTO) adlı modern bir hibrit algoritma ile incelenerek, optimize edilmiştir. Tezde, 40 adet farklı termal enerji kaynağından oluşan Tayvan Termal Güç Sistemi (TTGS), 8 baralı termal güç sistemi ve yeni oluşturulan 19 baralı termal-rüzgâr güç sistemleri güç akışı yönünden analiz edilmiştir. Analizlerde, literatürde geçen, geleneksel Genetik Algoritma (GA), geleneksel Öğrenme-Öğretme Tabanlı Optimizasyon (ÖÖTO) Algoritması ve yeni önerilen Genetik ve ÖÖTO algoritmalarının üstün yönleri bir araya getirilerek oluşturulan G-ÖÖTO algoritma karşılaştırılmalı olarak incelenmiştir. Bunların yanında, güç sistemlerinin yüklenme katsayıları değiştirilerek çeşitli senaryolar da denenmiştir. Bu çalışma, çok sayıda rüzgâr barasının bağlı olduğu ana baraların enterkonnekte sisteme eklenmesiyle, güç sisteminin verimini arttırmakta kullanılabileceğini ispatlamıştır. Ayrıca, geliştirilen hibrit G-ÖÖTO algoritmasıyla bu sistemler için etkin bir optimizasyon algoritması olduğu, yapılan simülasyon çalışmalarıyla doğrulanmıştır. Anahtar kelimeler: Geleneksel Güç Sistemi, Rüzgâr-Termal Güç Sistemi, Optimum Güç Akış Analizi, Genetik-Öğretme Öğrenme Tabanlı Optimizasyon Algoritması, Yakıt Maliyeti, Algoritma Çalışma Zamanı, CO2 salınımıConventional optimization techniques used in a power flow analysis have been losing their efficiency because of the increasing capacity of power systems in connections of the Wind Energy Systems (WES) into other power systems. For this purpose, in this thesis, the parameters which determines the power flow analysis such as fuel cost, emission rates and algorithm run time were analyzed and optimised by a redesigned hybrid Genetic-Teaching Learning Based Optimization (G-TLBO) Algorithm on the sample power systems with and without wind energy. In the thesis, Taiwan thermal power system (TTPS) which was composed of 40 different thermal energy sources, 8 bus thermal power system and 19 bus thermal-wind power systems proposed here newly were analyzed in terms of the power flow analysis. In the analyses, conventional Genetic and Teaching-Learning Optimization (TLBO) algorithms studied in literature were researched comparatively with the G-TLBO algorithm which was proposed first here and was composed by taking the outstanding properties of conventional Genetic and TLBO algorithms. According to literature researches, conventional Genetic Algorithms (GAs), conventional Teaching Learning Based Optimization (TLBOs) and recently new recommended GAs and TLBOs were inspected in detail. Lastly, a new G-TLBO algorithm was suggested. The superior aspects of last algorithms were combined in the new suggested G-TLBO algorithm. The results of it were comparatively presented in the thesis. The study shows that the addition of wind buses which are composed of many wind buses into interconnected system could be used to increase the power system efficiency and developed hybrid G-TLBO algorithm is an effective optimization algorithm that could be used in conventional and wind-thermal power systems. Key Words: Conventional Power System, Wind-Thermal Power System, Optimum Power Flow Analysis, Genetic-Teaching Learning Based Optimization Algorithm, Fuel Cost, Algorithm Run Time, CO2 Emissio

Enhancing smart grids with a new IOT and cloud-based smart meter to predict the energy consumption with time series

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features

Wind speed measurement with a low-cost polymer optical fiber anemometer based on Fresnel reflection

This study designed and experimentally verified a simple and low-cost anemometer based on Fresnel reflection using Polymer Optical Fiber (POF). The system was developed especially for wind speed measurement in harsh environmental conditions such as high electromagnetic interference. System verification was performed using a controlled wind source and a high-standard anemometer. In measurements made under normal temperature conditions, the dynamic range of the anemometer is between 3 m/s and 15 m/s. Experiments were carried out with two propellers of different diameters to evaluate the propeller diameter effect. According to the results obtained from the wind speed measurement experiments, it was observed that the mean error value for all measurements was 0.16, and the mean percentage relative error value was 1.85%

Evanescent Field Absorption-Based Fiber Optic Sensor for Detecting Power Transformer Oil Degradation

Observing the transformer oil quality is essential for the transformer's health and system. The aging state of the transformer oils and the average remaining life accordingly can be determined using the breakdown voltage (BDV) test, ultraviolet-visible (UV-Vis) spectroscopy, and the refractive index (RI) standard methods. The subject of this study is to design and manufacture a much simpler and low-cost, evanescent field absorption-based fiber optic sensor (EFA-FOS) compatible with these proven standard methods and to perform a comparative performance analysis by determining a critical voltage threshold. Apart from the expensive and cumbersome standard methods mentioned above, the usability of the oil is carried out, thanks to the online sensor system, with the pretest using this critical voltage value. EFA-FOS measurement results of oil samples can determine the samples' aging degree with high accuracy. The samples' RI is determined with EFA-FOS with a sensitivity of -70 V/RIU and a linearity of 0.98 (R2). A strong relationship has also been determined between the sensor output and measured BDV with a regression constant of 0.86 (R2)

The Impact of CoronaVac Vaccination on 28-day Mortality Rate of Critically Ill Patients with COVID-19 in Türkiye

Background:Vaccines against coronavirus disease-19 (COVID-19) have been effective in preventing symptomatic diseases, hospitalizations, and intensive care unit (ICU) admissions. However, data regarding the effectiveness of COVID-19 vaccines in reducing mortality among critically ill patients with COVID-19 remains unclear.Aims:To determine the vaccination status and investigate the impact of the COVID-19 vaccine on the 28-day mortality in critically ill patients with COVID-19.Study Design:Multicenter prospective observational clinical study.Methods:This study was conducted in 60 hospitals with ICUs managing critically ill patients with COVID-19. Patients aged ≥ 18 years with confirmed COVID-19 who were admitted to the ICU were included. The present study had two phases. The first phase was designed as a one-day point prevalence study, and demographic and clinical findings were evaluated. In the second phase, the 28-day mortality was evaluated.Results:As of August 11, 2021, 921 patients were enrolled in the study. The mean age of the patients was 65.42 ± 16.74 years, and 48.6% (n = 448) were female. Among the critically ill patients with COVID-19, 52.6% (n = 484) were unvaccinated, 7.7% (n = 71) were incompletely vaccinated, and 39.8% (n = 366) were fully vaccinated. A subgroup analysis of 817 patients who were unvaccinated (n = 484) or who had received two doses of the CoronaVac vaccine (n = 333) was performed. The 28-day mortality rate was 56.8% (n = 275) and 57.4% (n = 191) in the unvaccinated and two-dose CoronaVac groups, respectively. The 28-day mortality was associated with age, hypertension, the number of comorbidities, type of respiratory support, and APACHE II and sequential organ failure assessment scores (p < 0.05). The odds ratio for the 28-day mortality among those who had received two doses of CoronaVac was 0.591 (95% confidence interval: 0.413-0.848) (p = 0.004).Conclusion:Vaccination with at least two doses of CoronaVac within six months significantly decreased mortality in vaccinated patients than in unvaccinated patients