Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques.

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA’s simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA.

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.

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.