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Partial shading conditions (PSCs) may be unpredictable and difficult to forecast in large-scale solar photovoltaic (PV) systems. Potentially degrading the PV system's performance results from numerous peaks in the P–V curve caused by PSC. On the other hand, the PV system must be run at its maximum power point (GMPP) to maximize its efficiency. Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. In this research, an innovative grey wolf optimization, abbreviated as NGWO, is presented as a solution to overcome the shortcomings of the standard GWO method, which includes long conversion times, a rate of failure, and large oscillations in a steady-state condition. This paper seeks to address these issues and fill a gap in research by enhancing the GWO's performance in tracking GMPP. The original GWO is modified to incorporate the Cuckoo Search (CS) abandoned process to shorten the time it takes for effective adoption. Based on the simulation finding, the proposed IGWO method beats other algorithms in most circumstances, particularly regarding tracking time and efficiency, where the average tracking time is 0.19s, and the average efficiency is 99.86%. [ABSTRACT FROM AUTHOR]

Eddy‐current technique has been used in several industrial applications, such as non‐destructive testing and characterization, transmission systems, and separation processes. The main advantage of other separation techniques for eddy current separation is the absence of direct contact and do not use other liquid and water. This paper presents a novel configuration for a single‐disk vertical eddy current separator, covered by alternating permanent magnets. Unlike several other works, which employ a horizontal orientation for magnetic disks. The advantage is that it contains one driving motor, few strong permanent magnets, and a small size and simpler construction than the usual eddy‐current separators. In this work, the analysis of an eddy current separator with magnetic single disk using a numerical model based on finite element method, the obtained partial differential equation is done into the numeric solver. The experimental device was realized to separate two kinds of non‐ferrous waste: aluminium and copper. The experiment results show the effect of speed variation, metals size, and kind of metal on ejection distance. [ABSTRACT FROM AUTHOR]

Globally, and especially in developing nations, the increasing demand for energy, coupled with transmission and consumption inefficiencies, poses significant challenges. As the proliferation of household appliances and electric vehicles (EVs) rises, dependency on electricity surges, further straining the existing power infrastructure. While renewable energy resources (RERs) such as solar and wind gain traction, the reliance on conventional utilities persists, especially in homes. This reliance, especially on fossil fuels, intensifies environmental concerns due to greenhouse gas emissions. This paper presents the smart household energy management system (SHEMS), designed to optimize domestic energy consumption. Capitalizing on the Internet of Things (IoT), SHEMS offers real-time energy monitoring and management, providing homeowners an adaptable architectural framework to regulate their energy use. Fundamental component of SHEMS is a controller interfacing with all household loads and the main meter, ensuring timely data on power consumption. Notably, during peak times, the system can autonomously adjust appliance settings, balancing energy savings without compromising user convenience. Paired with a user-friendly application, SHEMS provides individuals comprehensive oversight of their consumption patterns, fostering more sustainable energy use in an increasingly electrified world. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems. The first issue is that the initial values of pollen are determined randomly at first, which can lead to premature convergence. To minimize the convergence time and enhance the possibility of detecting the GMPP, the initial pollen values were modified so that they were near the expected peak positions. Secondly, in the modified FPA, population fitness and switch probability values both influence swapping between two-mode optimization, which may improve the flower pollination algorithm's tracking speed. The performance of the modified flower pollination algorithm (MFPA) is assessed through a comparison with the perturb and observe (P&O) method and the standard FPA method. The simulation results reveal that under different partial shading conditions, the tracking time for MFPA is 0.24, 0.24, 0.22, and 0.23 s, while for FPA, it is 0.4, 0.35, 0.45, and 0.37 s. Additionally, the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions, with values of 99.98%, 99.90%, 99.93%, and 99.26%, compared to FPA with MPPT efficiencies of 99.93%, 99.88%, 99.91%, and 99.18%. Based on the findings from simulations, the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel auto-scaling variable step-size maximum power point tracking (MPPT) method. The novel method solves the problems associated with conventional variable step-size method. Firstly, the conventional variable step-size method use individual scaling factor N adjusted in order to modify the step-size to balance between the precision of tracking and its divergence rate during the design stage. However, this individual scaling factor N causes the dynamic response of the PV system to be slow in the extremely irradiation change condition. To address this issue, the proposed method combines the dual scaling factors N, which are (N1) with a high value and (N2) with a low value. The N1 is used for a faster response at the beginning of the execution. In the meantime, the second N2 value is used to help stabilize the power oscillation. The proposed method can automatically adjust the algorithm's step-size to obtain a fast dynamic response that adapts to weather variations, resulting in reliable steady-state output power. Secondly, the conventional variable step-size, which is based on division of the PV module power change by the PV voltage change, endures from steady-state power oscillations and dynamic problems, particularly when subjected to sudden environmental changes. In this paper, an improvement to the conventional variable step-size method is introduced, in which the step-size of the proposed method is based solely on the change in PV power in order to completely eliminate the division calculations involved in its structure. As a result, the complexity of algorithm implementation is reduced, allowing for the use of low-cost microcontrollers to reduce system costs. Simulation results are provided through MATLAB-SIMULINK to verify the performance of the novel auto-scaling variable step-size maximum power point tracking (MPPT) method. [ABSTRACT FROM AUTHOR]