One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.

This study assesses quad-band metamaterial perfect absorbers (MPAs) based on a double X-shaped ring resonator for electromagnetic interference (EMI) shielding applications. EMI shielding applications are primarily concerned with the shielding effectiveness values where the resonance is uniformly or non-sequentially modulated depending on the reflection and absorption behaviour. The proposed unit cell consists of double X-shaped ring resonators, a dielectric substrate of Rogers RT5870 with 1.575 mm thickness, a sensing layer, and a copper ground layer. The presented MPA yielded maximum absorptions of 99.9%, 99.9%, 99.9%, and 99.8% at 4.87 GHz, 7.49 GHz, 11.78 GHz, and 13.09 GHz resonance frequencies for the transverse electric (TE) and transverse magnetic (TM) modes at a normal polarisation angle. When the electromagnetic (EM) field with the surface current flow was investigated, the mechanisms of quad-band perfect absorption were revealed. Moreover, the theoretical analysis indicated that the MPA provides a shielding effectiveness of more than 45 dB across all bands in both TE and TM modes. An analogous circuit demonstrated that it could yield superior MPAs using the ADS software. Based on the findings, the suggested MPA is anticipated to be valuable for EMI shielding purposes.

In the field of lighter substitute materials, sandwich plate models of composite and hybrid foam cores are used in this study. Three core structures: composite core structure and then the core is replaced by a structure of a closed and open repeating cellular pattern manufactured with 3D printing technology. It finally integrated both into one hybrid open-cell core filled with foam and employed the same device (WBW-100E) to conduct the three-point bending experiment. The test was conducted based on the international standard (ASTM-C 393-00) to perform the three-point bending investigation on the sandwich structure. Flexural test finding, with the hybrid polyurethane/polytropic acid (PUR/PLA) core, the ultimate bending load is increased by 127.7% compared to the open-cell structure core. In addition, the maximum deflection increased by 163.3%. The simulation results of three-point bending indicate that employing a hybrid combination of PUR-PLA led to an increase of 382.3%, and for PUR–TPU by 111.8%; however, the highest value recorded with PUR/PLA, which has the slightest stress error among the tests. Also, it is reported that when the volume fraction of reinforced aluminum particles is increased, the overall deformation becomes more sufficient, and the test accuracy improves; for example, rising from 0.5% to 3%, the midspan deflection of composite (foam-Al) is increased by 40.34%. There were noticeable improvements in mechanical properties in the 2.5% composite foam-Al.

Accuracy and speed of fault detection are crucial to the performance of DC transmission systems. In this paper, a novel approach is proposed for fault detection, classification, and localization in high-voltage DC transmission lines (HVDC-TLs). The proposed approach for protecting HVDC-TLs by designing and operating a distance protection scheme has been constructed using a fuzzy inference system and training an adaptive neuro-fuzzy inference system. A fuzzy inference system model is proposed to detect faults, classify them, and determine the zone where the fault occurred. The transmission line is divided into three zones to facilitate fault location identification. An adaptive neuro-fuzzy inference system is then trained to determine the fault location per kilometer. The proposed distance protection scheme identifies faults with high fault resistance; it can be successfully used to estimate the fault area and locate faults in HVDC-TLs using the concept of fuzzy inference. A monopolar DC transmission line system was modeled and operated, and several faults were simulated using PSCAD and MATLAB software. As clarified from the various simulation experiments, the proposed approach has performed better than the existing techniques and recently published related works.

Accurate and energy-efficient temperature regulation in electric furnace systems remains a challenging control problem due to nonlinear dynamics, significant thermal inertia, and inevitable time delays. Conventional proportional–integral–derivative (PID) and PID–acceleration (PIDA) controllers, though widely used, often exhibit degraded performance under such conditions, particularly when implemented in a single-degree-of-freedom. To address these limitations, this study proposes, for the first time, a two-degree-of-freedom (2-DOF) PIDA controller tailored for electric furnace temperature control. The controller structure allows independent tuning of set-point tracking and disturbance rejection by introducing separate feedforward paths in the proportional and derivative channels while maintaining integral and acceleration actions on the error signal. To optimize the controller parameters, the recently developed greater cane rat algorithm (GCRA) is employed for the first time in this context. A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. Results demonstrate that the proposed method consistently achieves faster settling times, reduced overshoot, and near-zero steady-state error, while maintaining robustness under external disturbances and measurement noise. For instance, in the nominal case, the method yields an overshoot of 1.8382% and a settling time of 3.4542 s, outperforming PFA, HOA, L-SHADE, and PSO. Robustness tests under load disturbances and measurement noise confirm stable operation with minimal performance degradation, achieving less than 2.5% overshoot and under 4 s settling time across all evaluated scenarios. These findings highlight the potential of the GCRA-based 2-DOF PIDA controller as a high-precision and energy-efficient solution for temperature regulation in industrial time-delay systems.