In modern power systems, conventional energy production units are being replaced by clean and environmentally friendly renewable energy resources (RESs). Integrating RESs into power systems presents numerous challenges, notably the need for enhanced grid stability and reliability. RES-dominated power systems fail to meet sufficient demand due to insufficient inertia responses. To address this issue, various virtual inertia emulation techniques are proposed to bolster power system stability amidst the increased integration of renewable energy sources into the grid. This review article explores state-of-the-art virtual inertia support strategies tailored to accommodate the increased penetration of RESs. Beginning with an overview of this study, it explores the existing virtual inertia techniques and investigates the various methodologies, including control algorithms, parameters, configurations, key contributions, sources, controllers, and simulation platforms. The promising virtual inertia control strategies are categorised based on the techniques used in their control algorithms and their applications. Furthermore, this review explains evolving research trends and identifies promising avenues for future investigations. Emphasis is placed on addressing key challenges such as dynamic response characteristics, scalability, and interoperability with conventional grid assets. The initial database search reveals 1529 publications. Finally, 106 articles were selected for this study, adding 6 articles manually for the review analysis. By synthesising current knowledge and outlining prospective research directions, this review aims to facilitate the current state of research paths concerning virtual inertia control techniques, along with the categorisation and analysis of these approaches, and showcases a comprehensive understanding of the research domain, which is essential for the sustainable integration of renewable energy into modern power systems via power electronic interface.

In this paper, we numerically debrief an ultra-high sensitive surface plasmon resonance (SPR) biosensor utilizing thin layers of graphene oxide (GO) that have not been addressed adequately till now. By the deposition of GO on top of the metal-dielectric heterostructure, our proposed sensor can achieve higher sensitivity and higher Quality Factor (QF) simultaneously which has not been possible by the existing models to our knowledge. Because of its large surface area and sp ² inside of an sp ³ matrix which is capable of confining π electrons, GO can form strong covalent bonds with biomolecules and hence enhanced light-material interaction that made researchers contemplate to achieve increased sensitivity and figure of merit. Both the transfer matrix method and finite element method are exploited to perform extensive numerical analysis for optimizing the structure considering its sensitivity, full width half maximum (FWHM), and QF. This paper examines six different configurations of multilayer heterostructure containing prism-adhesive-metal-BaTiO ₃ /BP-Gr/GO/MXene-sensing medium, and a noticeably enhanced performance is achieved using GO with a maximum sensitivity of 372 deg/RIU and QF of 88.11 RIU ^-1 in the range of refractive index (RI) 1.330 to 1.353. Moreover, the possibility of designing a tunable SPR sensor to operate at a broader range of analyte’s RI is investigated, and 414 deg/RIU with 119.27 RIU ^-1 QF at 1.407 RI is achieved. The Electric field distribution, effects of different layers, and fabrication feasibility of the proposed sensor are explored, it is envisaged that this can be an appropriate apparatus for highly sensitive, rapid, and noninvasive label-free biosensing useful in experimental sensing protocols.

In isolated networked microgrid with high penetration of solar and wind-based generation, maintaining system stability and achieving optimal dynamic performance poses significant challenges due to reduced mechanical inertia traditionally provided by synchronous generators. This paper introduces a novel Resilient Back Propagation Bayesian Neural network-based virtual inertia control strategy to enhance frequency response and overall stability of the considered network microgrid. By leveraging robust control techniques, the proposed approach provides virtual inertia to respond effectively to varying system conditions and disturbances, improving system robustness and minimising the isolated networked microgrid’s tie-line power and frequency deviations. Comprehensive simulations, including case studies under varying disturbance conditions such as load fluctuations and renewable energy variations, prove the efficacy of the introduced virtual inertia control strategy. The proposed method outperforms conventional proportional integral derivative, and artificial bee colony-optimised proportional integral derivative control techniques in key dynamic performance metrics. It achieves the lowest integral time absolute error of 18.3912, compared to 30.8946 for proportional integral derivative and 20.8212 for optimised proportional integral derivative control techniques, demonstrating superior frequency response. Additionally, it achieves a mean square error of 4.0897e-07, significantly lower than 4.239e-06 for neural network-based fractional order proportional integral derivative control and 3.072e-06 for feed-forward neural network, confirming improved accuracy. Results indicate improved dynamic performance metrics, including faster frequency stabilization and reduced overshoot with minimal tie-line power and frequency deviation, compared to proportional integral derivative and optimal control techniques. The strategy’s adaptability and computational efficiency offer practical benefits for real-world implementation, contributing to more resilient and efficient microgrid operations.