Search Results (165)

BLDC motors are utilized in electric cars, robotics, drones, home appliances and medical equipment due to their effectiveness, dependability, and accurate control. PI controllers have been put forward to enhance the dynamic performance of brushless direct current (BLDC) motors, and they have been tested in many papers with various algorithms (such as PSO, GA, GWO, ACO and ABC) and strategies (such as PI/PID control, FOC, FLC, SMC and MPC). Meanwhile, in this research, and for the first time, the PI controller was tuned by the proposed Sailfish Optimization algorithm (SFO) with a direct torque control (DTC) strategy to enhance the dynamic performance of BLDC motors. Although DTC provides a very fast torque response, it still suffers from high torque ripple and noticeable instability at low speeds. These issues persist even when using conventional PI tuning or common optimization algorithms. Hence, in this research, we proposed an improved control strategy that combines DTC with PI tuning optimized by the Sailfish Optimization algorithm (SFO), which delivers smoother torque, more stable low-speed operation, and stronger robustness during sudden changes in load. In this regard, the PI controller was tested under different levels of torque and compared with the traditional Gray Wolf Optimization (GWO-PI) algorithm controller, as well as PI and PID controllers, and the performance of each of them was evaluated for different torque levels at speeds of 600 rpm and 2000 rpm during physical experiments. The simulation results showed that the Sailfish-PI controller, compared to the others, recorded the fastest response with a rise time of 2.1 ms and settling time of 2.9 ms under 2.39 Nm nominal torque at 2000 rpm speed; in addition, it continuously showed the lowest values of overshoot and undershoot as torque increased. It also maintained the most accurate and consistent performance, keeping the peak rpm almost flat and extremely near to the target of 2001 rpm. Therefore, in systems that require variable speed and torque while operating, such as electric automobiles, the proposed method is suitable for application. Full article

Regional energy self-sufficiency based on microgeneration from clean, local energy sources is essential and strategic for meeting growing electricity demand. In this context, initiatives driven by local governments are decisive in achieving such progress. This study proposes a methodology for sizing photovoltaic (PV) capacity at the parish level, which is the basic political–administrative unit in Ecuador. Rooftop-based microgeneration and self-supply are considered to entail minimal environmental impact while offering significant potential to meet the basic energy demands of buildings in the Andean equatorial climate. The results demonstrate that, using accessible tools such as drones, computer-aided design software, and Agisoft Metashape, and through low-labour processes, it is feasible to estimate the PV potential of buildings at the parish scale. A total of 1698 rooftops were surveyed, and after discarding those with precarious construction materials, the estimated solar potential was found to be between ten and twenty-three times higher than the electrical demand of the analysed parishes. The estimated annual generation potential reaches 28,101 MWh, compared to an annual demand of 1827 MWh for both parishes combined. The proposed process enables the incorporation of rooftop-based technological capacity, relying on a low-technology, affordable methodological approach and instruments for low-income parish governance offices, with low-density populated areas as the main novelty, providing clear information to both authorities and the local population. Full article

In this paper, we propose a signal intelligence (SIGINT) drone swarm system with a three-dimensional (3-D) volumetric self-complementary array configuration. In the proposed system, multiple drones form two array layers separated along the boresight direction of the system, providing sufficient spacing between drones mounting an antenna element. The antenna elements in one array layer are arranged in a complementary manner to fill empty spaces in the other layer, allowing the system to maximize the number of drones deployed within the aperture area. As a result, the effective electrical spacing at 300 MHz is reduced from 1.7λ and 0.9λ to 0.85λ and 0.45λ along the x- and y-axes, respectively. The array gains of the proposed system are 3.96 dBi, 6.40 dBi, and 15.3 dBi at 100 MHz, 200 MHz, and 300 MHz, and the side-lobe levels (SLLs) are −13.0 dB, −12.7 dB, and −13.0 dB. In addition, the proposed drone swarm SIGINT system is evaluated in a practical SIGINT environment that considers terrain features, and then the detection performance is compared with those of conventional ground-based and airborne SIGINT systems. In this SIGINT scenario, the proposed system can detect signals over an extended detection range of 150 km than those of ground-based and airborne systems. Full article

Automated inspection of power infrastructure with drones requires processing video streams in real time and performing object recognition from image data with constrained resources. Server-based object recognition algorithms depend on transmitting data over a network and require considerable computational resources. In this study, we present an automated system designed to inspect power infrastructure using drones in real time. The proposed system is implemented on the Rockchip RK3588 platform and uses a lightweight YOLOv8 architecture incorporating a Slim-Neck model with a VanillaBlock module integrated into the backbone. To support real-time operation, we developed a digital video stream processing system (DVSPS) to coordinate multimedia processor (MPP)-based hardware video decoding, with inference performed on a multicore neural processing unit (NPU) using thread pooling. The system can navigate autonomously using a closed-loop machine vision system that computes the latitude and longitude of electrical towers to perform multilevel inspections. The proposed model attained an 84.2% mAP50 and 52.5% mAP50:95 with 3.7 GFLOPs and an average throughput of 111.3 FPS with 34% fewer parameters. These results demonstrate that the proposed method is an efficient and scalable solution for autonomous inspection across diverse operational conditions. Full article

Photovoltaic systems represent one of the most reliable and widely used technologies for electricity generation from renewable energy sources, although their performance is affected by the occurrence of faults and defects that lead to energy losses and efficiency reduction. Therefore, detecting and localizing defects in photovoltaic panels is essential. A wide variety of image analysis techniques based on aerial thermal imagery acquired by drones have been widely implemented for proper maintenance operations, requiring a comprehensive comparison among these approaches to assess their relative performance and suitability for different scenarios. This study presents a comparative evaluation of several vision-based approaches using artificial intelligence for photovoltaic defect detection. YOLO- and Transformer-based models are analyzed and benchmarked in terms of accuracy, inference time, per-class performance, and sensitivity to object size. Experimental results demonstrate that both YOLO- and Transformer-based models are computationally lightweight and suitable for real-time implementation. However, Transformer-based architectures exhibit higher detection accuracy and stronger generalization capabilities, while YOLOv5 achieves superior inference speed. The RF-DETR-Small model provides the best balance between accuracy, computational efficiency, and robustness across different defect types and object scales. These findings highlight the potential of Transformer-based vision models as a highly effective alternative for real-time, on-site photovoltaic fault detection and predictive maintenance applications. Full article

Three-dimensional printing is the most commonly used method for producing customized or mock-up products for industrial applications. In particular, aviation devices for drones usually require a high spatial resolution to satisfy the small size requirement. In practical applications of drones, the two main tasks are inspection and detection. However, the working environment is often filled with flammable gases, such as natural gas or petroleum gas. Thus, the parts of drones that can easily produce an electrical spark, such as electronic connectors, should be specially protected. In this study, atmosphere control was applied to enhance the printing performance and manufacture of anti-explosion devices. The results demonstrate that atmosphere control can efficiently improve the print quality and that the print resolution of a commercial 3D printer can be enhanced to reach the mm scale. In the anti-pressure testing via a high-pressure smoke experiment, the manufactured anti-explosion devices for drones showed an appropriate intrinsic safety level, suggesting that they can be used in drones used for daily inspections of pipelines in petrochemical plants. The two main contributions of this study are the development of a practical method for improving FDM 3D printers and an anti-explosion device for drones. Full article

This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through filtering, and digital data transmission via Arduino and Bluetooth, the instantaneous line currents are visualized in MATLAB (R2023a) as time-based curves. The sensor box can also be remotely released from the transmission line once measurements are complete, allowing a fully autonomous mode of operation. Laboratory tests demonstrated promising results for real-world applications, with measurement efficiencies ranging from 92% to 98% under various test conditions, including stress tests involving harmonics and total harmonic distortion up to 40%. Future work will focus on implementing effective shielding against high electric fields to further enhance reliability and advance the sensor’s industrialization as a novel solution for power grid digitalization. Full article

Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV performance, covering its optical, thermal, and electrical impacts. Findings from global literature indicate that dust-induced efficiency losses typically range from 10% to 70%, depending on particle characteristics, environmental conditions, and surface orientation. Experimental and modeled I–V and P–V characteristics further reveal significant declines in current and power output as soiling levels increase. Through an extensive literature assessment, this paper identifies Machine Learning (ML)-based approaches as emerging and highly effective techniques for dust detection and mitigation. Recent studies demonstrate the integration of image processing, drone-assisted monitoring, and convolutional neural networks (CNNs) to enable automated, real-time soiling assessment. These intelligent methods outperform conventional manual and time-based cleaning strategies in accuracy, scalability, and cost efficiency. By synthesizing current research trends, this review highlights the growing role of ML and data-driven technologies in enhancing PV system reliability, informing predictive maintenance, and supporting sustainable solar energy generation. Full article

Dynamic social and legal transformations drive technological innovation and the transition of energy and heating sectors toward renewable sources and higher efficiency. Ensuring the reliable operation of these systems requires regular inspections, fault detection, and infrastructure maintenance. Unmanned Aerial Vehicles (UAVs) are increasingly being used for monitoring and diagnostics of photovoltaic and wind farms, power transmission lines, and urban heating networks. Based on literature from 2015 to 2025 (Scopus database), this review compares UAV platforms, sensors, and inspection methods, including thermal, RGB/multispectral, LiDAR, and acoustic, highlighting current challenges. The analysis of legal regulations and resulting operational limitations for UAVs, based on the frameworks of the EU, the US, and China, is also presented. UAVs offer high-resolution data, rapid coverage, and cost reduction compared to conventional approaches. However, they face limitations related to flight endurance, weather sensitivity, regulatory restrictions, and data processing. Key trends include multi-sensor integration, coordinated multi-UAV missions, on-board edge-AI analytics, digital twin integration, and predictive maintenance. The study highlights the need to develop standardised data models, interoperable sensor systems, and legal frameworks that enable autonomous operations to advance UAV implementation in energy and heating infrastructure management. Full article

Detecting anomalies in electrical equipment and improving maintenance efficiency are critical for ensuring operational safety, reliability, and sustainability. To address the structural limitations of conventional manual and visual inspection methods, this study developed an object-recognition-based automated damage diagnosis system for lightning rods and insulators (ADS-LI), which enabled non-contact and fully automated diagnosis of lightning rods and insulators. ADS-LI employs a dual-module architecture. The first module precisely detects lightning rods and insulators using the PointRend algorithm applied to drone-acquired aerial imagery. The second module is a formula-based diagnostic model that quantitatively determines structural anomalies using the geometric attributes of the detected objects. Specifically, anomalies in lightning rods are identified by analyzing variations in inclination derived from center-coordinate shifts ($\Delta x$), while insulator anomalies are evaluated based on the mask area conservation ratio ($r$). The performance of ADS-LI was validated using 90 independent test datasets, achieving a 0.89 F1-score and 99% overall accuracy. These results demonstrate that ADS-LI effectively automates labor-intensive diagnostic tasks that previously relied on skilled experts. Furthermore, by quantifying anomaly detection criteria, it ensures consistency and reproducibility for diagnostic outcomes. This study is also expected to contribute, in the long term, to the transition of elevated electrical installations toward a sustainable maintenance regime. Full article

The increasing complexity of the electromagnetic environment poses an increasing risk to unmanned aerial vehicles (UAVs) operating in airspaces subject to adverse electromagnetic effects. This paper investigates the potential electromagnetic interference that UAVs may encounter during flight through the lens of electromagnetic compatibility (EMC), which defines the requirements for the proper operation of UAV electronics. According to existing EMC standards, the immunity threshold for typical commercial drones is 10 V/m. However, European standards for public exposure permit electromagnetic fields and suggest that it is possible for an electromagnetic field of a mobile base station antenna to be as strong as 61 V/m. To assess drone vulnerability to its electromagnetic environment, investigation was conducted in an anechoic chamber, which determined that commercially available drones typically experience uncontrolled descent when subjected to an electric field strength of 30 V/m or higher. The primary coupling path for this interference is through the UAV’s internal cables, as induced parasitic currents perturb the motor control signals. This disruption leads to flight instability as the propellers can no longer be reliably controlled, resulting in flight instabilities. Based on a maximum effective radiated power (ERP) of 40 dBW per sector for a base station antenna, a minimum safe operating distance of 20 m was calculated. Adherence to this safe distance is therefore strongly recommended for any commercial drone operator to avoid EMI-induced flight failure. Full article

Stingless bees, particularly Tetragonula pagdeni, are vital for both ecosystems and the economy due to their pollination services and nest products. However, little is known about their nesting habits. This study investigated the nesting ecology of Tetragonula pagdeni in West Bengal, India. The species was found inhabiting a variety of landscapes, including agricultural, forest, rural, semi-urban, and urban areas, with a greater abundance in rural areas featuring mixed vegetation. Colonies, which were eusocial, perennial, and cavity-nesting, occupied diverse substrates, including tree trunks, building walls, rock crevices, electric poles, and field ridges—tree trunks and walls being the most common. Wild nests were located at heights ranging from 0 to 13.46 m, mostly around 2 m. Nest entrances varied in shape (circular, oval, slit-like, or irregular), with a longest opening axis of 10.50 ± 2.94 mm, and were oriented in multiple directions. Internally, nests measured 198.31 ± 86.36 mm in length and 142.73 ± 17.28 mm in width. Nests featured brood zones surrounded by honey and pollen pots, along with structure-supporting elements like the involucra and pillars. Brood cells were light brown and oval; those for workers and drones were similar, while queen cells were larger. Honey pots were light to dark brown, oval, dome-shaped, or irregular. Each involucrum was a thin, flat sheet, and the pillar was short, narrow, thread-like. These findings offer valuable insights into the distribution, nesting behaviour, and nest architecture of Tetragonula pagdeni, supporting its conservation and sustainable management. Full article

This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article

With the growing demand for high-efficiency and high-performance electric motors in applications such as electric vehicles, drones, and industrial drive systems, Axial Flux Motors (AFMs) have gained significant attention due to their high torque density and compact structure. However, low-pole AFMs are prone to performance degradation and noise issues caused by magnetic saturation in the rotor back yoke and increased torque ripple. In this study, a conventional 6-pole, 9-slot Radial Flux Motor (RFM) was redesigned as an AFM within the same external volume. To minimize losses, the stator inner diameter and slot thickness were co-optimized. In addition, tapering techniques were applied to both the stator and magnets to reduce torque ripple, and a parametric analysis of magnet tapering was conducted to identify optimal design conditions. A rolling core fabrication method was adopted to ensure both electromagnetic performance and manufacturability. The final AFM design demonstrated a 1.4 percentage point improvement in efficiency. Additionally, torque ripple was reduced by 69.44%, thereby validating the effectiveness of the AFM redesign and ripple reduction strategy. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article