Osmani, Khaled

This paper serves as an overview of the current progress concerning the DNeD (Digitalized, legally safe and low-emission airborNe inspection and grid data acquisition using automated Drones) project. DNeD aims to provide a full solution of an intelligent current sensor, integrable inside a dedicated drone-embedded sensor box. The research began with a brief survey of non-invasive current sensing technologies as a guide for the selection of the optimum current sensing physics. Afterwards, an all-optical sensor (MICATU RG235) was tested under laboratory conditions. The results from these tests were systematically tabulated. Concurrently, a study on magnetic field-based current sensing was performed using Finite Element Analysis (FEA) in COMSOL. This dual approach allows for a comprehensive evaluation of different sensing technologies. Ultimately, the study concludes that magnetic-based sensing, utilizing Hall effect sensors (e.g. DRV5055), is by far the most suitable approach. Additionally, the design of the sensor-box housing considered crucial mechanical factors such as robustness and weight. The design process utilized CAD models developed in SolidWorks, which form the backbone for real sensor-box manufacturing. Overall, the circuitry suggestions consisted of the development of analogue modulation electronic circuits, incorporating low pass filters, voltage dividers, and additional protective circuitry to ensure the reliability and accuracy of the sensor data. A prototype was developed, featuring Bluetooth as the communication protocol between the sensor-box and a remote system running MATLAB. Preliminary results indicate that the prototype effectively transfers dummy analogue voltage signals over distances up to 30 metres, demonstrating the feasibility of the design for practical applications. This comprehensive study not only addresses the mechanical and physical design aspects of embedding an intelligent sensor in a drone but also explores various current sensing technologies and their practical implementation. The findings provide a robust framework for future development and optimization of intelligent sensor systems in aerial applications.

Photovoltaische (PV) Systeme leiden unter suboptimaler Leistungsausbeute bei Teilabschattungsbedingungen, bedingt durch wechselnde Umweltfaktoren wie Einstrahlung, Temperatur und partielle Verschattung. Diese Arbeit stellt einen innovativen Algorithmus zum Maximum Power Point Tracking (MPPT) vor, der auf der Partikelschwarmoptimierung (PSO) basiert. Das Ziel ist es, unter herausfordernden Bedingungen den globalen Maximum Power Point (MPP) präzise zu lokalisieren und zu verfolgen. Im Gegensatz zu konventionellen PSO-basierten MPPT-Verfahren, die oft unter langen Konvergenzzeiten oder Oszillationen während der Suche leiden, integriert der vorgestellte Ansatz eine adaptive Anpassungsstrategie. Diese Modifikation ermöglicht eine signifikante Verbesserung der Leistungsfähigkeit, charakterisiert durch minimale stationäre Schwingungen und eine schnelle Anpassung an sich ändernde Umgebungsbedingungen. Die Effektivität des vorgeschlagenen Algorithmus wird durch umfangreiche Simulationen in MATLAB/SIMULINK demonstriert. Die Ergebnisse werden detailliert analysiert und mit etablierten PSO-Varianten verglichen, wobei die überlegene Performance des neuen Ansatzes in Bezug auf Konvergenzgeschwindigkeit, Stabilität und Effizienz der MPP-Verfolgung hervorgehoben wird. Diese Arbeit leistet einen weiteren Beitrag zur Optimierung von PV-Systemen unter realen, variablen Betriebsbedingungen.

This paper serves as a quick guide for the selection process of contactless electrical sensors involved in unmanned aerial systemsbased gridmonitoring applications. Since the selection criteria for such sensors can be enormous and hence challenging, the major key points were elaborated after a thorough review of relevant work. The review presented in this paper, composedof seven current sensors and three voltage sensors topologies, is critically investigated. In terms of flexibility, complexity, sensitivity, and other performance indices, it is concluded that an “all-optical” dual voltage and current contactless sensor would represent an optimum choice to be employed within remote measuring applications. The mainrecommended feature of the suggested sensor is its ability to freely measure electrical quantities while being “relatively” far from the electrical transmission line, implying no need to be “clamped” all around it. By presenting an informative background for intelligent field sensing applications, this paper also suggests the future work relevant to the studied topic.

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

This paper aims to design the fundamental basis for an Unmanned Aerial System (UAS)-driven, remote, and non-invasive current sensing application. Using the COMSOL software, the methodology presented here consists of the Computer Aided Design (CAD) for stranded Transmission Line (TL) geometries composed of 7 to 91 sub-filaments and discretized via tetrahedral-element-based meshes. The radiated Magnetic Field (MF) around each TL is then solved by means of Finite Element Analysis (FEA) after selecting the proper materials for TLs under the coil geometry analysis study. For each TL, all resultant MFs’ norms are presented as tabulated data, with respect to the inducing currents. Eventually, the complex mathematical model needed to evaluate these MFs, radiated around stranded TLs, is surpassed by the scalable models designed through this study. The min/max MFs radiated around each TL resulting from the min/max injected current values are hence obtained. This would serve in the accurate choosing/positioning of magnetic-based sensors in UAS applications, reliably. Additionally, related future works are concretely presented.

Smart grids yield in an efficient energy distribution, driven by advanced grid management and diverse technologies’ integration. For instance, the remote monitoring of transmission lines, facilitates the development process of conventional electrical networks into smart grids. One form of such remote monitoring applications can be actuated by means of a drone, which carries an embedded intelligent sensor. These drone-based applications referred to as Unmanned Aerial Systems (UAS), thus allow an instantaneous surveillance of the actual status of transmission lines, remotely. Accordingly, this paper presents an experimental setup of an “all-optical” electrical sensor, confronted with different forced voltage/current (V/I) levels, in order to test its feasibility for future applications in drone-controlled, electric grid monitoring processes. After analyzing different measurements’ outputs, and comparing them with reference values outputted by reference V/I sensors, the resulting deviated errors are tabulated and justified. Furthermore, the exploration of the studied sensor’s pros and cons, and by taking into consideration the space/weight restrictions that a drone-based application obtrudes, related future work is suggested.