Musa Yilmaz

In photovoltaic systems, PSC occur when PV panels are exposed to nonuniform solar irradiance levels. Extracting the maximum power from PV systems operating under PSC represents a complex and challenging task for MPPT algorithms. Optimization-based MPPT techniques have therefore gained significant attention due to their ability to achieve fast convergence and high efficiency under such conditions. In this study, a novel M-DHO algorithm is proposed by integrating the DHO algorithm, which is inspired by the cooperative hunting behavior of the Asiatic wild dog, with a Levy flight strategy to enhance global search capability. Especially with Levy flight support, the M-DHO algorithm eliminates the problems of fast convergence and getting stuck in a local minimum. Furthermore, while the fast convergence problem is eliminated, the Levy Flight algorithm allows reaching the global maximum value faster with high accuracy in complex optimization problems. Nine distinct PSC scenarios are created across six different voltage regions, and the performance of the proposed M-DHO algorithm is comparatively assessed against GWO, WOA, FPA, and the conventional DHO algorithm. Simulation results demonstrate that the proposed M-DHO algorithm achieves faster convergence to the global maximum power point and higher tracking efficiency compared to the benchmark algorithms. When averaged over all scenarios, M-DHO achieved an average extracted power of 838.58W, tracking speed of 0.15s and an average tracking efficiency of 99.52%, outperforming other algorithms.

Maximum Power Point Tracking (MPPT) algorithms, which are employed to extract the maximum power from photovoltaic (PV) systems, exhibit different performance characteristics under uniform irradiance and partial shading conditions (PSC) arising from nonuniform solar irradiance distribution on PV panels. Under PSC, the performance of conventional MPPT algorithms becomes inadequate, leading to increased interest in optimization-based approaches. In this study, the Grey Wolf Optimization (GWO) algorithm, commonly used in MPPT applications, was modified, and an Improved Grey Wolf Optimization (IGWO) algorithm was proposed. A PV system model consisting of four series-connected PV panels and a boost converter was developed in the MATLAB/Simulink environment to evaluate the performance of the proposed algorithm. The effectiveness of the algorithm was tested under nine distinct and complex PSC scenarios. The results obtained under these nine PSC cases were analyzed through comparisons of the proposed IGWO algorithm with GWO, the Cuckoo Search Algorithm (CSA), and the Flower Pollination Algorithm (FPA). The results demonstrate that the IGWO algorithm achieves the highest mean maximum power and exhibits superior MPPT performance compared to the other algorithms, with a mean tracking efficiency of 98.34%.

This study examines the use of Kirchhoff’s law algorithm (KLA) to calibrate a two-degree-of-freedom PID (2DOF-PID) controller for temperature regulation in a non-linear continuous stirred tank reactor (CSTR). The reactor model shows strong thermal nonlinearity. It also shows a noticeable time delay. This is based on the coupled mass and energy balance equations. This makes accurate temperature control much more difficult. These effects were addressed by KLA, which is formulated as a physics-based optimization method rather than a learning or data-driven method. The algorithm produced a set of controller gains that yielded faster, more stable responses by treating the current–voltage equilibrium of electrical circuits as an energy-balanced search method. The optimally tuned 2DOF-PID controller has lower rise and settling times, and minimal steady-state error, compared with the other standard optimizers tested in an identical manner. The results show that combining KLA with controller design can improve both robustness and control accuracy for non-linear thermal systems.

Automatic Electrical energy plays a critical role in the economic and technological development of modern societies. High voltage lines are utilized for the efficient distribution of this energy. These lines enhance energy efficiency and sustainability by minimizing energy transmission losses. Ensuring the safe and uninterrupted transmission of electrical energy is of paramount importance for the robustness and maintenance of the infrastructure. Consequently, the role of high-voltage poles in the effective and efficient transmission of energy is of paramount importance. The objective of this study is to utilize remote sensing and various shallow learning techniques to identify high voltage line poles in the vicinity of Batman. The Sentinel-1 Synthetic Aperture Radar (SAR) satellite was utilized in this study. Sentinel-1 synthetic aperture radar (SAR) imagery was processed on the Google Earth Engine (GEE) platform to derive backscatter information for infrastructure and terrain analysis. Dual-polarization data (VV and VH) from both ascending and descending orbits were acquired between January and June 2025. Standard preprocessing steps, including thermal noise removal, radiometric calibration, and terrain correction using the SRTM 30 m digital elevation model, were automatically applied through the GEE Sentinel-1 GRD pipeline. Median composites were generated to minimize speckle noise and temporal variability. The derived VV and VH backscatter coefficients were subsequently used to extract spatial texture and backscatter difference features that characterize electrical pylons and transformer areas. The dataset was composed of VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization modes. The analysis of these datasets was conducted using algorithms belonging to the supervised learning model, which included the support vector machine, KNN, decision tree, quadratic discriminant, and naive Bayes algorithms. The findings indicate that the support vector machine model demonstrated an 85.0% success rate, the quadratic discriminant model exhibited an 82.5% success rate, the KNN model achieved an 82.2% success rate, the decision tree model attained a 76.8% success rate, and the naive Bayes model registered a 74.0% success rate. The study demonstrates the efficacy of artificial intelligence in facilitating more precise, expeditious, and systematic control of high-voltage poles. In light of the fact that conventional methods tend to be both time-consuming and costly when it comes to the maintenance of high voltage lines, the objective of this study is to enhance the efficiency of the energy infrastructure by means of contributing to the improvement of the monitoring and maintenance processes of power transmission lines.

This study evaluates the performance of a pulse‐excited printed circuit board (PCB) resolver in comparison with the traditional square‐wave excitation method. Although the pulse‐excited resolver exhibits slightly higher error levels than the square‐wave excitation approach, these differences remain acceptable for applications that prioritize reduced power consumption and improved thermal performance. Experimental results indicate that the average position errors of the resolver are 1.109° at low speeds and 0.338° at high speeds. These error levels are considered acceptable for a wide range of industrial applications. Thermal analysis demonstrates that the proposed pulse excitation method substantially reduces thermal stress on the resolver, yielding a maximum temperature of approximately 32.0°C during testing, compared with nearly 59.7°C under square‐wave excitation. Overall, the findings highlight the advantages of the proposed method, particularly for applications requiring effective heat management. The results suggest that the pulse‐excited resolver represents a viable alternative, combining operational efficiency with enhanced thermal performance.

This paper presents an estimation method for determining the position and orientation of a real mobile robot using raw data from an Inertial Measurement Unit (IMU) sensor, alongside linear and angular velocities obtained from simulation. The dataset was collected using a real TurtleBot3 differential drive wheeled mobile robot in the ROS-Gazebo simulation environment, encompassing 2018 routes-2009 from simulation and 9 from real-world experiments-each consisting of five randomly generated waypoints. To improve the accuracy of the estimation models, noise from the real IMU sensor was incorporated into the input data, and velocities derived from the pure pursuit algorithm were also included. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gradient Boosting (GB), and Random Forest (RF) models were employed to estimate the robot’s position and orientation, and their performance was compared across both simulated and experimental scenarios. The results indicate that the CNN architecture consistently outperforms other models across all routes. Unlike many existing studies, this work directly utilizes raw sensor data without applying any feature extraction techniques, highlighting its novelty and contribution to the field.

The identification of various objects and species found in nature is of great importance today. Active and passive imaging systems are in a beneficial position in this direction, both in terms of cost and convenience. Recently, mucilage events in our country pose a great risk for both marine life and human life. In this study, water areas in one of the regions affected by the mucilage event that occurred in May 2021 were chosen as the object. The region between Armutlu-Zeytinbağı in the Marmara Sea was chosen as the study area. 1300 samples were selected from the mucilage region and recorded with the help of GPS. After these selected samples were chosen as mucilage area for 17 May–22 May and as a clean area for 21 June-22 June (2600 samples in total), image analyses were made using time series with the help of Sentinel-1 satellite images. These image analyses were performed using Sentinel-1 band parameters (VV-VH). A unique data set was created by recording the numerical data showing the backscattering values of the VV-VH polarization band images. It is aimed to automatically detect the mucilage area by applying deep learning and machine learning to the obtained data set. It has been observed that the accuracies of our applied hybrid (Transformer Method + Logistic Regression), deep learning (RNN, CNN) and machine learning models (Decision Tree, Naive Bayes, SVM) are high (96%−100%). With the applied deep learning and machine learning methods, it is thought that regions can be detected more easily and intervened early in these regions.

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks.

Proton Exchange Membrane Fuel Cells (PEMFCs) enable continuous energy production regardless of environmental conditions due to the storability of hydrogen. When examining the current–power (I–P) curve of a PEMFC under steady-state operating conditions, maximum power is observed at a specific current level. To extract this power, Maximum Power Point Tracking (MPPT) algorithms are employed. These algorithms should feature a simple structure and rapidly track the maximum power point. However, intelligent and optimization-based methods in the literature often involve high computational complexity. In this study, a modified Perturb and Observe (P&O)-based MPPT algorithm is developed to achieve a fast steady-state response under varying PEMFC operating conditions. The proposed algorithm also minimizes power oscillations in the steady state. Its performance is evaluated in a MATLAB/Simulink environment under five different scenarios. A comparative analysis is conducted against the conventional P&O and optimization-based MPPT algorithms, including Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Genetic Algorithm (GA). The results, presented graphically, demonstrate the advantages of the proposed approach.

Assessing the trend of fatalities in recent years and forecasting road accidents enables society to make appropriate planning for prevention and control. This study analyses the road traffic accident data between the years 2013 and 2022 obtained for the province of Batman in Turkey, where it has not been considered before. The scope of the data analysed includes the fatalities and injuries of drivers, passengers and pedestrians. The road accident forecast for the next ten years up to 2032 is the focus of this study and numerous analyses using learning methods such as State Space Models (SSM), Artificial Neural Networks (ANN), Autoregressive Integrated Moving Average (ARIMA) and hybrid models (CNN + LSTM and Attention + GRU) have been performed on the available data. The predictions made with the above models give results with acceptable accuracy. However, they give different results depending on the parameters used. The models created with the data studied show that the number of road accidents and the related deaths and injuries will continue to increase over the next 10 years, starting in 2022. If the causes of road accidents are not eliminated and the situation remains stable as it is in 2022, the number of accidents, deaths and injuries is expected to double by 2032.