Grid-Integrated Dual Wind Turbine System Using SEPIC Converter with Whale Optimized PI Controller

Main objectives of the study are to progress a dual independent doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) for stable and efficient power delivery to an AC microgrid while ensuring grid stability and compliance with power quality standards. In order to achieve the set goals, the following tasks were accomplished: integration of a Pulse Width Modulation (PWM) rectifier for AC to DC conversion, implementation of a SEPIC converter for voltage boosting, tuning of proportional integral (PI) controller parameters using whale optimization algorithm (WOA) for dynamic DC voltage regulation, and design of a 3Φ Voltage Source Inverter (VSI) for efficient management of active and reactive power to the grid. The scientific novelty of the proposed work is the inclusion of dual independent DFIG system with SEPIC converters and optimized PI controllers. The most important results are the demonstration of consistent DC voltage stabilization, improved power quality under varying wind conditions, and an overall system efficiency of 97%, verified through MATLAB simulations. These attained outcomes are found to be more efficient when compared to other existing converters and optimized controllers thereby satisfying the objectives of meeting desired voltage demands of grid and achieving highly stabilized output power. The significance of obtained results is the establishment of an advanced DFIG-WECS-based wind energy system capable of enhancing grid performance, ensuring reliable integration of renewable energy, and maintaining power quality and stability in compliance with modern grid standards

Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope: A Large Cohort Validation

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (ALVSD) affects 7 million globally, leading to delayed diagnosis and treatment, high mortality, and substantial downstream health care costs. Current detection methods for ALVSD are inadequate, necessitating the development of improved diagnostic tools. Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD. OBJECTIVES: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. METHODS: 2,960 adults undergoing echocardiography from 4 U.S. health care networks were enrolled in this multicenter observational study. Patient data were captured using a digital stethoscope, and echocardiograms were performed within 1 week of data collection. The algorithm\u27s performance was compared against echocardiographic EF (EF measurements, categorizing EF as normal and mildly reduced [\u3e40%] or moderate and severely reduced [≤40%]). RESULTS: The CNN model demonstrated an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 77.5%, specificity of 78.3%, positive predictive value of 20.3%, and negative predictive value of 98.0%. Among those with an abnormal artificial intelligence screen but EF \u3e40% (false positives), 25% had an EF between 41%-49% and 63% had conduction/rhythm abnormalities. Subgroup analyses indicated consistent performance across various demographics and comorbidities. CONCLUSIONS: The CNN model, utilizing a digital stethoscope, offers a noninvasive and scalable method for early detection of individuals with EF ≤40%. This technology has the potential to facilitate early diagnosis and treatment of heart failure, thereby improving patient outcomes

Self Learning Machines using Artificial Neural Networks, Genetic Algorithms and Fuzzy Logic

One of the buzzword in the Information technology is Artificial Intelligence (AI) The future is Artificial Intelligence, which will transform the real world objects into highly intelligent virtual objects. We aim to come up with highly advanced sophisticated technology that can boost the machines with intelligence and also keeping us informed of the state of things. The main objective of this paper is to provide advanced real time usage of combining