Microgrids play a crucial role in optimizing renewable energy by integrating various sources to generate and store electricity. Effective resource management within microgrids is essential for improving efficiency and reducing operational costs. This study employs bibliometric analysis to explore key trends and emerging technologies in microgrid energy management. The analysis begins with a co-occurrence network to visualize relationships between key terms and their interconnected research areas, helping to identify thematic clusters. A trend topic analysis follows, highlighting the evolution of significant themes from 2010 to 2024. A dendrogram analysis further organizes subtopics hierarchically, while a word map analysis provides an overview of dominant themes and their practical applications. Thematic map analysis categorizes keywords into distinct clusters, offering a comprehensive understanding of technology trends and connections in the field. Key findings highlight four clusters: energy management systems, smart power grid optimization, battery management systems, and electric frequency control. These clusters underscore critical research gaps and innovative approaches in optimizing microgrid performance. By identifying future research directions, this study aims to contribute to the development of sustainable and efficient energy systems.

This study explores the relationship between the volumetric fraction of jute fiber and the mechanical properties of epoxy matrix composites for motorcycle fairings, highlighting the feasibility of jute fibers due to their strength and sustainability. Through a methodology that includes meticulous fiber cleaning, precise mold fabrication, and rigorous evaluation of tensile and impact properties, the study provides an innovative perspective in composite materials engineering. The results demonstrate that composites with jute fibers are viable for manufacturing fairings with excellent thermal and mechanical performance, opening new avenues for motorcycle manufacturing that prioritize both performance and environmental responsibility.

Active disturbance rejection control (ADRC) is a robust methodology that does not require precise knowledge of the plant. Developed in China by Professor Jingqing Han, it is based on PID control, state observers, and nonlinear functions. Brushed DC motors are known for their low cost and the noise they introduce into control circuits. This paper demonstrates that ADRC can effectively control low-power brushed DC motors using a general nonlinear model and Simulink for tuning. The model is simulated using parameters provided by the manufacturer. An ADRC developed and programmed by the authors in MATLAB is then integrated into the simulation. The controller is tuned, and its performance is verified. Subsequently, the ADRC is implemented on a Raspberry Pi 3 using MATLAB’s support packages and methods developed by the authors. The controller is tested on a Faulhaber 2342L012CR DC motor (12 V/17 W). The results show that it is possible to control the position of the low-power brushed DC motor through simulation-based tuning. The interaction between Simulink and Raspberry Pi 3 enables an optimal control characterized by a fast response, a minimal steady-state error, and no perceptible overshoot. This implementation demonstrates that ADRC is a practical and efficient control method for brushed DC motors.

While hardware selection is widely recognized as a key factor in machine learning performance, systematic and reproducible evaluation across heterogeneous and accessible platforms remains limited, particularly when jointly considering execution time, energy consumption, stability, and cost-efficiency. This work presents a unified and fully reproducible benchmarking framework for supervised learning, designed to enable controlled and comparable evaluation across diverse hardware environments. The proposed methodology enforces consistent training pipelines, fixed hyperparameter configurations, and repeated executions to ensure statistical reliability, while incorporating performance metrics such as execution time, power consumption, and energy usage, as well as performance-per-dollar. The framework is validated on a representative set of platforms, including CUDA-enabled GPUs, Apple Silicon (CPU/GPU), x86 processors, ARM-based embedded systems, and cloud-based environments, using convolutional, recurrent (RNN, LSTM, BiLSTM), and tree-based (XGBoost) models. The results reveal that hardware efficiency is strongly model-dependent. GPUs provide the highest computational performance and stability for parallel workloads, whereas Apple Silicon achieves superior energy efficiency with competitive execution times, particularly for recurrent architectures. The batch size analysis shows that performance can vary significantly depending on workload configuration, especially on CPU-based platforms, while epoch-based evaluation confirms that the measured performance reflects steady-state behavior rather than initialization overhead. In contrast, conventional CPUs and embedded systems exhibit significant scalability limitations for deep learning training, although they remain competitive for tree-based methods such as XGBoost, which demonstrates near hardware-independent predictive performance. These findings highlight the limitations of generalized hardware selection criteria and emphasize the need for model-aware and hardware-aware benchmarking. The proposed framework offers a practical and extensible foundation for reproducible, hardware-aware evaluation of machine learning systems, supporting informed decision-making in research, deployment, and cost-constrained scenarios.

Introduction: SEPIC converters are widely used in power electronics due to their ability to either step up or step down voltage levels. However, their nonlinear behavior and the presence of a right-half-plane zero make it difficult to design fast and stable controllers. This study analyzes the performance of three control strategies applied to a SEPIC converter: Active Disturbance Rejection Control (ADRC), Proportional–Integral (PI), and Proportional–Integral–Derivative (PID).Objectives: to compare the reference tracking capability, disturbance rejection, and implementation complexity of ADRC, PI, and PID controllers applied to a SEPIC DC-DC converter.Materials and Methods: simulations were conducted in MATLAB/Simulink by introducing perturbations in both the power source and the output voltage. The performance indices IAE, ITAE, ISE, and ITSE were computed to quantify accuracy, speed, and robustness. The influence of ADRC tuning parameters, such as observer bandwidth and tracking differentiator speed, was also analyzed.Results: the ADRC controller exhibited the fastest response and best disturbance rejection, although it showed inherent oscillations and higher design complexity. The PI controller achieved a good balance between simplicity and performance, while the PID controller displayed the slowest response but smoother behavior under disturbances.Conclusions: ADRC is suitable for applications requiring fast and robust control performance. However, PI and PID controllers remain valid alternatives when simplicity or smoother control signals are prioritized.

Wind turbines are critical components in renewable energy systems, where early fault detection is essential to ensure reliable operation and reduce maintenance costs. Vibration-based monitoring using multichannel signals provides rich information about the dynamic behavior of the system, although it also introduces challenges related to high dimensionality and feature redundancy. This paper proposes a machine learning-based methodology for fault classification that combines Discrete Wavelet Transform (DWT) for time–frequency feature extraction with Linear Discriminant Analysis (LDA) for dimensionality reduction within a structured processing pipeline. The approach incorporates a Group K-Fold cross-validation strategy to prevent data leakage and ensure a reliable evaluation when working with segmented signals. Experimental results show that the proposed framework achieves high classification performance, reaching a mean accuracy of $98.84\pm 1.16\%$ and a weighted F1-score of $0.9905\pm 0.0089$ using a Support Vector Machine (SVM) classifier over five Group K-Fold splits. The results also indicate that dimensionality reduction plays a critical role in improving class separability, having a greater impact than the specific choice of wavelet transform. Findings demonstrate that the proposed DWT–LDA-based approach provides an effective solution for rotor imbalance detection in the laboratory-scale wind turbine evaluated in this study.