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This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA's core concept is inspired by the dwarf mongoose's foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy. [ABSTRACT FROM AUTHOR]

This paper discusses enhancing of the measurements’ uncertainty for AC high voltage up to 100 kV. This is achieved by using a high voltage divider calibration method. Voltage measurements have been carried out at the Egyptian national institute for standards (NIS), using a high voltage measuring system (Phenix-KVM100), that consists of a high voltage divider and a voltage display. The voltage divider and display have been calibrated in low and high voltage ranges. Reference standard digital voltmeter and a multifunction calibrator have been used to calibrate the KVM100 for achieving accurate and traceable results. All calibrations have been performed automatically using Laboratory Virtual Instrument Engineering Workbench (LabVIEW) programs specially designed for this task. Uncertainty budget has been evaluated to get the measurements’ expanded uncertainties.

Open phase in three phase induction motors is a common fault that can occur as a result of a fuse blowing or a protective device failing on one phase of the motor. This paper introduces a new method, which is based on the transient measurement and can distinguish the fault of one phase connecting ground. The method has been proved to be in correspondence with the simulation results by Matlab and LabVIEW in practice. The method has merits of simplicity, accuracy and ease of use. [ABSTRACT FROM AUTHOR]