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Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

Due to their impaired immune systems, lung cancer (LC) patients are especially sensitive to COVID-19 and are more susceptible to it as well as its related effects. The diagnosis, treatment and aftercare of LC patients are exceedingly difficult and time-consuming throughout an epidemic due to a multitude of factors. In these situations, the care of LC patients using cutting-edge technologies offers the potential to enhance the diagnosis, treatment, and advancements using machine learning (ML) algorithms and artificial intelligence (AI). The researchers might be able to differentiate between lung problems brought on by the corona virus and those brought on by, for example, chemotherapy and radiation, using therapeutic and imaging data as well as ML techniques. AI ensures that the appropriate individuals are enrolled in LC clinical research more effectively and rapidly than in the past, when it was done in a conventional and time-consuming manner. To effectively treat cancer patients and find new, more potent treatments, it is critical to move past traditional research approaches and make use of artificial intelligence and machine learning (AIML). When applied to various patient populations, AI based algorithms can swiftly identify lung cancer CT scans with COVID-19-linked pneumonia and accurately distinguish non-COVID connected pneumonia, which is significant for thoughtful mechanisms of an outbreak that is significant to AI. It is possible to use the present challenges and projected futures in this study to direct the best application of AI and ML in an epidemic. [ABSTRACT FROM AUTHOR]

The primary objective of wireless electric charging pollution is to transmit power through a specific area using an electromagnetic field. It is essential to change the battery charging procedure in order to improve reliability because electric vehicles are a better option to reduce continuous pollution. Electric vehicle batteries can be charged wirelessly or by plugging them into plugs at charging stations. Static or dynamic charging systems can be used to perform wireless power transmission. The car can be charged even while it is moving with the help of a dynamic charging system. Power can be delivered from the source through the transformer windings to the rechargeable batteries using inductive power transfers. Battery charging stations of this type can be set up for pre planned trips. In addition to increasing the adoption of electric vehicles, this will also boost their efficiency and durability over long distances. In particular, the research paper offers an assessment of potential wireless charging technologies and future EV development. [ABSTRACT FROM AUTHOR]

Non-orthogonal signal-based systems are a type of communication system that uses signals that are not mutually perpendicular (i.e., not orthogonal) to transmit information. These types of systems can increase the spectral efficiency of communication systems by allowing for more data to be transmitted in the same bandwidth. Groups of signals with non-orthogonal waveforms can increase spectral efficiency, but they also increase the potential for interference. Spectrally efficient frequency division multiplexing (SEFDM) is a well-studied waveform that was originally proposed for use in wireless systems but has since found application in millimeter wave communications at 60 GHz, optical access network architecture, and longdistance optical fiber transmission. However, non-orthogonal signal-based systems are also more susceptible to interference from other sources, which can degrade the quality of the transmitted signal. To address this problem, this paper suggests using fuzzy learning techniques to cancel out interference and improve the signal-to-noise ratio. Fuzzy learning is a type of machine learning that uses fuzzy logic (FL) to handle uncertainty and imprecision in data. By using FL techniques to cancel out interference, the non-orthogonal signal-based optical communication (OC) system could potentially achieve better performance in noisy environments. Overall, this research topic has the potential to contribute to the development of more efficient and reliable OC systems that can operate in challenging environments. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to make a comparative analysis of Total Cross Tied (TCT), Bridge Link (BL), Honey Comb (HC) and Magic Square (MS) topologies. Due to shading, number of power peaks is obtained in power vs voltage curve which leads in enhancing power losses. For weakening this effect of shading on PV array, reconfiguration technique is adopted named as MS with same electrical connections as of TCT. This relocation of rows and column elements assist in extracting uniform difference in current. Different shading scenarios are considered and applied on each configuration to extract power output. In addition to power output, Fill Factor (FF), losses due to mismatch and efficiency parameters are also calculated for effective analysis. For testing the validity of new technique, four shading patterns are used and performance is evaluated. The results are verified by simulation via MATLAB/SIMULINK software. By the results, it is clear that MS is far better among all configurations. [ABSTRACT FROM AUTHOR]

Summary: Review transcript presents copious solar tracking methods to investigate their output potential and also outline tracker type, construction, cost, and design parameter. The solar follower systems encompassed five classifications based on the tracking methods are active, passive, semi‐passive, manual, and chronological tracking are also reviewed with their application and feasibility. These all method‐based research paper are compared on the basis of complexity of circuit, cost, tracking based on (radiation or astronomical equation), degree of freedom, control strategy, accuracy, energy intact, power or energy gain, tracing error, and panel efficiency. Basically, one axis and two axis sun following system review revels compare study against stable system which addressed their suitability and capability of maximum energy generation and MPPT technologies. [ABSTRACT FROM AUTHOR]

The paper presents the performance analysis‐based reliability estimation of a self‐excited induction generator (SEIG) using the Monte‐Carlo simulation (MCS) method with data obtained from a self‐excited induction motor operating as a generator. The global acceptance of a SEIG depends on its capability to improve the system's poor voltage regulation and frequency regulation. In the grid‐connected induction generator, the magnetizing current is drawn from the grid, making the grid weak. In contrast, in the SEIG stand‐alone operation, an external capacitor arrangement is implemented to render the reactive power support. This capacitor arrangement is connected across the stator terminals during the stand‐alone configuration of SEIG. The capacitor serves two purposes, which include voltage build‐up and power factor improvement. Therefore, the paper deals with obtaining the minimum capacitor value required for SEIG excitation in isolated mode applications, including stand‐alone wind power generation. The SEIG performance characteristics have been evaluated for different SEIG parameters. The simulation and experimental results are then compared and found satisfactory. Then, SEIG reliability is estimated considering the MCS method utilizing SEIG excitation's failure and success rates during experimental work in the laboratory. Finally, the SEIG reliability evaluation is performed considering different wind speeds. [ABSTRACT FROM AUTHOR]