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.

Microgrids, pivotal in modern power systems, face unique protection challenges due to bidirectional power flows, dynamic topologies, and inverter-based resources (IBRs). This systematic review critically analyzes 55 peer-reviewed studies (2020-2024) to evaluate microgrid protection challenges, methods, and emerging solutions. We synthesize findings from IEEE Xplore, MDPI, Springer, Wiley, and Taylor & Francis databases using the PRISMA framework. Key challenges include bidirectional power flow (36% of studies), low fault currents (33%), and protection coordination failures (62%). Conventional methods like overcurrent and distance protection struggle with adaptability, while adaptive, communication-assisted, and intelligent strategies show promise but face complexity and cybersecurity risks. AI-driven methodologies achieve a fault-detection accuracy of 99.2%. However, they require substantial datasets and considerable computational resources. Resilience frameworks remain inadequately developed, with merely 10% of the literature addressing critical metrics such as reliability and recovery. This review underscores the imperative for hybrid solutions and the establishment of standardised resilience metrics. It also highlights the importance of validating findings in real-world contexts to ensure the practical application of theoretical progress. Future investigations should emphasise the importance of cybersecurity measures, the implementation of modular enhancements for legacy systems, and the development of explainable artificial intelligence to reconcile theoretical progress with practical application.

The optimization of electromechanical systems, such as those involving induction motors and gearboxes, is crucial for improving energy efficiency, system performance, and reliability in industrial applications. This paper presents an advanced methodology for optimizing the energy efficiency of electromechanical systems, integrating both mechanical and electrical subsystems to minimize the system's overall weight, energy losses, and transient response time. The optimization problem is approached holistically, considering the interdependence of various system parameters and applying multi-objective optimization techniques to address conflicting objectives. The analysis focuses on optimizing the gearbox speed ratio to minimize the relative weight of the motor-gearbox system while maintaining operational efficiency. A systemic approach, utilizing convex surrogate modeling and multi-stage gearboxes, is proposed to improve the scalability of the solution. The results demonstrate the existence of optimal gearbox speed ratios for various motor sizes and configurations, offering insights into the best design choices for minimizing system weight and optimizing performance. These findings apply to a range of real-world systems, including electric vehicles and industrial machinery, where minimizing weight and optimizing energy efficiency are critical for improving overall system performance and reducing operational costs.

Especially during grid faults, grid code requirements for wind power integration have become a key element in improving grid efficiency and reliability. Under severe problems for network operation, wind turbines are expected to continue operating and supporting the grid during frequency restoration. This paper presents simulation results of a fixed-speed grid-connected wind turbine under various short-circuit current contributions. Fault analysis is carried out by studying the grid side line to ground fault, double line fault, double line to ground fault, and three phase fault involving ground and without ground. The obtained current waveforms are analyzed to explain the behavior, such as the rate of decay and peak values. Variations of active and reactive power during post-fault conditions and faulty conditions are investigated. Moreover, recommendations for switchgear and protection equipment are performed.

This paper explores the transient behavior of an induction motor–gearbox–load system during startup and braking, emphasizing the impact of key dynamic parameters such as inertia, torque, and gearbox ratio. A comprehensive mathematical formulation is developed to analyze transient time, leading to the derivation of an optimal gearbox ratio that minimizes acceleration and deceleration durations. Both analytical and graphical evaluations reveal that deviations from this optimal ratio significantly extend transient times, increasing energy losses. The findings demonstrate that high-speed induction motors deliver superior transient performance while maintaining compact size and reduced weight. Moreover, the study shows that optimization calculations can be simplified without sacrificing accuracy, improving computational efficiency. These insights contribute to the optimization of electromechanical drive systems, enhancing their dynamic response in real-world applications.

This paper explores the transient behavior of an induction motor-gearbox-load system during startup and braking, emphasizing the impact of key dynamic parameters such as inertia, torque, and gearbox ratio. A comprehensive mathematical formulation is developed to analyze transient time, leading to the derivation of an optimal gearbox ratio that minimizes acceleration and deceleration durations. Both analytical and graphical evaluations reveal that deviations from this optimal ratio significantly extend transient times, increasing energy losses. The findings demonstrate that high-speed induction motors deliver superior transient performance while maintaining compact size and reduced weight. Moreover, the study shows that optimization calculations can be simplified without sacrificing accuracy, improving computational efficiency. These insights contribute to the optimization of electromechanical drive systems, enhancing their dynamic response in real-world applications.