Recently, the development of advanced power semiconductor devices has made widespread use of power switching circuits and other power electronics applications in commercial and industrial environments. This paper focuses on Single Phase Matrix Converter (SPMC) as a AC-DC boost rectifier and AC-DC buck rectifier. The SPMC was operated in the desired mode using a single driver circuit. Insulated Gate Bipolar Transistors (IGBTs) are used in the power circuit as switching devices to implement SPMC topology. Switching signals are obtained by Sinusoidal Pulse Width Modulation (SPWM) technique. The simulation is carried out using MATLAB / Simulink.

Variable-speed wind energy systems equipped with permanent magnet synchronous generators (PMSGs) have become a common configuration in wind energy industry. Appropriate pitch angle control is designed to limit the output power of the system by adjusting the pitch angle of the wind turbine blades at higher wind speeds. This approach reduces mechanical stress on the turbine, extends its operating speed range, and increases the overall lifespan. Intelligent control methods, such as optimization algorithms, are notable for their fast and reliable control performance; however, their use in pitch angle control applications remains relatively limited. In this study, a pitch angle control based on the Archimedes Optimization Algorithm (AOA) was developed and analyzed using MATLAB/Simulink. The proposed control system demonstrated fast and stable performance at higher wind speeds. Additionally, the control mechanism was set to deactivated at lower wind speeds to optimize energy efficiency for varying conditions. When compared to other conventional control methods, alternative approaches generally achieve an average accuracy of %75 to %80. However, the proposed control method performed a significantly faster convergence speed and achieved an accuracy rate exceeding %90.

Fuel cells are considering as primary power source for portable applications, electrical vehicles and residential applications in the future. However, since the voltage value obtained from a single fuel cell is too low, the output voltage values of the fuel cells must be increased. Therefore, DC/DC and DC/AC converters should be used to obtain a balanced output voltage and to make FCs available for all applications. This paper discusses the principle and electrical characteristics of the fuel cell, DC/DC boost converter and DC/AC inverter.

This study investigates the feasibility of hydrogen production potential using the available renewable energy source potentials in the province of Elazig, Türkiye. The study aims to create a potential assessment of each district in Elazig, based on wind and solar sources and Proton Exchange Membrane (PEM) electrolyzer technology. It aims not only to evaluate the green hydrogen potential but also to outline the feasibility of implementing this alternative energy source at a localized level. In this way, an alternative energy source can be assessed for production, storage, and the scarcity of energy in emergency conditions such as earthquakes or other cases. The comprehensive analysis integrates a multitude of parameters, including geographic variations and technological considerations, supported by detailed illustrations to present a holistic view of the hydrogen potential in Elazig. The study’s outcomes suggest that districts with high energy potential and minimal geographical and topological constraints have a significant potential for hydrogen production.

Rüzgâr enerjisi, elektrik üretiminde kullanımı giderek artan bir enerji kaynağıdır. Rüzgâr enerjisinde verimlilik, uygun elemanların ve kontrol tekniğinin seçimine bağlıdır. Son yıllarda rüzgâr enerjisi sistemlerinde değişken hızlı rüzgâr türbinleriyle uyumlu kalıcı mıknatıslı senkron generatörler ön plana çıkmaktadır. Bu tip generatörler elektriksel kayıpları azaltabileceği gibi mekanik bileşenlere bağımlılığı azaltarak genel sistem performansını yükseltir. Dönüşüm sistemlerinde anlık rüzgâra göre sistem verimliliğini en üst seviyede tutabilmek için maksimum güç noktası izleme kontrolünden yararlanılır. Bu çalışmada; dönüşüm sistemi bileşenlerinin sistem büyüklükleri üzerinden çalışma prensipleri açıklanmış. Kanat uç hız oranı, tepe tırmanma ve bulanık mantık tabanlı maksimum güç noktası izleme kontrol yöntemleri açıklanmıştır. Bu kontrol yöntemleri, Matlab/Simulink ortamında modellenerek çeşitli sistem büyüklükleri üzerinden karşılaştırmalı analizleri gerçekleştirilmiştir.

Power transformers are among the most critical components of electric power transmission and distribution systems, and unexpected failures can lead to substantial economic losses and prolonged power outages. Dissolved Gas Analysis (DGA) is the most widely used diagnostic technique for transformer fault diagnosis and involves interpreting gases dissolved in insulating oil. However, conventional interpretation methods, such as the Rogers ratio, the Doernenburg ratio, the IEC 60599 ratio method, and the Duval Triangle, rely heavily on expert knowledge, may produce inconsistent diagnoses for the same oil sample, and may fail to provide a diagnosis in certain cases. In this study, 12 classification models were evaluated using the publicly available Power Transformers Fault Detection and Diagnosis (FDD) and Remaining Useful Life (RUL) dataset and a unified evaluation protocol. Model performance was assessed using Accuracy, Balanced Accuracy, and Macro-F1 score, while model interpretability was investigated through Shapley Additive Explanations (SHAP) analysis. The results showed that ensemble tree-based methods achieved the best overall performance. LightGBM and Random Forest both attained an Accuracy of 0.969 and a Macro-F1 score of 0.924, while LightGBM further achieved the highest Balanced Accuracy of 0.940. XGBoost exhibited the most stable performance under cross-validation. SHAP analysis revealed that engineered relative concentration features, particularly the CO/H₂ ratio and the combined gas ratio, were among the most influential features for fault classification. These findings demonstrate that, for datasets of this scale, ensemble tree-based models combined with well-designed features provide strong and interpretable performance for imbalanced DGA-based fault diagnosis, highlighting the effectiveness of feature-based ensemble learning for small- to medium-sized datasets.