Talal Guia

This study investigates a material-based solution to this challenge by implementing Functional Graded Materials (FGM) in the insulator housing to achieve a uniform electric field distribution. Three distinct spatial permittivity profiles are designed and analysed, Using Finite Element Method (FEM) simulations in COMSOL Multiphysics, the electric field and potential distributions are computed under dry conditions. The results demonstrate that the U-FGM configuration achieves the most significant improvement, reducing the axial electric field stress at the high-voltage triple point.

The integration of artificial intelligence (AI) and blockchain technology opens new avenues for decentralized, transparent, and secure data-driven systems. However, ensuring privacy and verifiability in collaborative AI environments remains a key challenge, especially when model updates or decisions must be recorded immutably on-chain. In this paper, we propose a novel privacy-preserving framework that leverages an ElGamal-based aggregate signature scheme with aggregate public keys to enable secure, verifiable, and unlinkable multi-party contributions in blockchain-based AI ecosystems. This approach allows multiple AI agents or data providers to jointly sign model updates or decisions, producing a single compact signature that can be publicly verified without revealing the identities or individual public keys of contributors. The design is particularly well-suited to resource-constrained or privacy-sensitive applications such as federated learning in healthcare or finance. We analyze the security of the scheme under standard assumptions and evaluate its efficiency in different terms. The study and experimental results demonstrate the potential of our framework to enhance trust and privacy in AI collaborations over decentralized networks.

Integrating large numbers of electric vehicles will lead to operational and planning difficulties for distribution networks. The installation of electric vehicle charging stations (EVCS) increases power losses, affects stability, and degrades the reliability of the distribution network, resulting in dissatisfaction among customers. This work describes the impacts of the integration of electric vehicle charging stations on power losses, voltage stability index, and the main reliability indices as a function of the position and size of electric vehicle charging stations. This investigation was carried out on the IEEE 33 bus test system. According to the results, a high concentration of EVs increases peak demand, leading to greater energy losses, adversely affecting stability, and degrading reliability indices of the distribution network. The choice of installation points for EVCS is therefore very important, as some points induce greater energy losses than others and affect stability and reliability more than others. Measures to mitigate the effects of EVCS installation on distribution networks are crucial.

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

This paper explores the electric field distribution near a string of six NJ120 high voltage insulators through experimental measurements under various conditions. Using a distributed capacitance method with custom probes connected to an Arduino-based system, tests were conducted on clean and polluted insulators. Pollution was simulated using a tuf-based solution with conductivities ranging from 0.5 to 1.97 mS/cm. Measurements were taken at 13 heights and four distances (1 to1.75 m) under applied AC voltages of 15 to 80 kV. The study also investigates the effects of damaged insulators, placed at different positions (near the high voltage terminal, in the middle, and near the ground), as well as the role of guard rings in improving field uniformity. Results indicate that pollution and damaged insulators significantly increased field intensity, particularly near the high voltage terminal. Guard rings effectively reduced field peaks, with larger radii and optimal placement providing the best performance. These findings contribute to optimizing high voltage insulator design for enhanced reliability.

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

The climatic factors have a strong influence on the polluted high voltage (HV) insulators performance and affect the reliability of networks. Such factors can lead to insulation incidents and outages in the electrical network. Based on monthly 12 weather factors observations (the average temperature, the precipitation, the humidity, the solar radiation, the vapour pressure, the snow depth, the dew point, the air pressure, the visibility, the total cloud cover, the wind direction and the wind speed) obtained during the period 2010–2015 (72 months) at Algerian central region, the authors propose an optimal relationship between the number of HV insulators incidents and the climatic factors. The autoregressive distributed lag (ARDL) method was employed to explore the actual and/or delayed (lagged) effect of each climatic factor on HV insulators performance. According to the results, the ARDL approach revealed the existence of strong relationship between these climatic variables and HV insulators incident. Also, the used statistical tests in this study allow us to say whether the proposed model is real and not simply due to chance. The proposed model has been validated by comparing the number of HV insulators incidents predicted for 10 months to real data recorded in Algerian AC transmission networks.