Background: Nanotechnology explores a variety of promising approaches in the area of material sciences on a molecular level, and silver nanoparticles (AgNPs) are of leading interest in the present scenario. This review is a comprehensive contribution in the field of green synthesis, characterization, and biological activities of AgNPs using different biological sources. Methods: Biosynthesis of AgNPs can be accomplished by physical, chemical, and green synthesis; however, synthesis via biological precursors has shown remarkable outcomes. In available reported data, these entities are used as reducing agents where the synthesized NPs are characterized by ultraviolet-visible and Fourier-transform infrared spectra and X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Results: Modulation of metals to a nanoscale drastically changes their chemical, physical, and optical properties, and is exploited further via antibacterial, antifungal, anticancer, antioxidant, and cardioprotective activities. Results showed excellent growth inhibition of the microorganism. Conclusion: Novel outcomes of green synthesis in the field of nanotechnology are appreciable where the synthesis and design of NPs have proven potential outcomes in diverse fields. The study of green synthesis can be extended to conduct the in silco and in vitro research to confirm these findings.

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Low frequency oscillation (LFO) within a power system is an important issue. Loss of inter-connected machine synchronism, reactive load increase, and faults and disturbances lead to a LFO in a range between 0.1-2 Hz. LFOs limit power transfer over long transmission lines, thus degrading the quality of supply. Moreover, conventional local control, namely, the power system stabilizer, is unable to mitigate LFOs. Furthermore, continuation of LFOs will lead to complete system collapse (blackouts). Considering the above issues, there is a pressing need to incorporate advanced control systems to damp inter-area LFOs. We propose an adaptive-supplementary unified power flow control (UPFC) for two inter-connected areas of a power system. Our work is novel in its design of a supplementary control system using a neural network based on a feedback linearization auto-regression average model. With the above proposed control scheme, the stability of power system is enhanced in terms of: (1) effective LFO damping; (2) power transfer; (3) improvement in the dynamic parameters of the system; (4) active and reactive power support; (5) loss minimization; and (6) demand-supply management. To justify these claims, we implement our proposed controller on a two-area (four machine) system. Critical analysis of the system is conducted under symmetrical grid faults. The result of the proposed control scheme justifies the performance enhancement of above parameters, as per grid-code requirements, compared with conventional a proportional integral (PI) controller.

In this paper, a model reference controller (MRC) based on a neural network (NN) is proposed for damping oscillations in electric power systems. Variation in reactive load, internal or external perturbation/faults, and asynchronization of the connected machine cause oscillations in power systems. If the oscillation is not damped properly, it will lead to a complete collapse of the power system. An MRC base unified power flow controller (UPFC) is proposed to mitigate the oscillations in 2-area, 4-machine interconnected power systems. The MRC controller is using the NN for training, as well as for plant identification. The proposed NN-based MRC controller is capable of damping power oscillations; hence, the system acquires a stable condition. The response of the proposed MRC is compared with the traditionally used proportional integral (PI) controller to validate its performance. The key performance indicator integral square error (ISE) and integral absolute error (IAE) of both controllers is calculated for single phase, two phase, and three phase faults. MATLAB/Simulink is used to implement and simulate the 2-area, 4-machine power system.

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