The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

In recent years, there has been a substantial growth in renewable energy sources and among these sources, solar energy is known as one of the best energies. The increasing adoption of solar energy across various applications underscores its significance in the renewable energy landscape. The integration of large-scale photovoltaic power plants into the primary power grid necessitates efficient and reliable power conversion processes, particularly as there is a growing demand for enhanced controllability and flexibility from the grid side. Power electronic converters, bolstered by advancements in control and information technologies, play a pivotal role in facilitating large-scale power generation from solar energy. High-power multilevel inverters have emerged as a compelling solution for addressing the escalating energy requirements. This paper aims to delve into the exploration of diverse structural configurations and technical hurdles encountered in high-power multilevel inverter topologies, alongside the associated control systems and modulation techniques tailored for application in large-scale photovoltaic power plants (LS-PV-PP) systems. A comprehensive analysis of high-power multilevel inverter topologies within solar PV systems is presented herein. Subsequently, an exhaustive examination of the control methods and strategies employed in high-power multilevel inverter systems is conducted, with a comparative evaluation against alternative approaches. Lastly, the paper delves into a discussion on prominent modulation methods utilized in multilevel power inverters, assessing their performance characteristics in various operational scenarios.

This study designed a system consisting of a photovoltaic system and a DC-DC boost converter with buck-boost inverter. A multi-error method, based on model predictive control (MPC), is presented for control of the buck-boost inverter. Incremental conductivity and predictive control methods have also been used to track the maximum power of the photovoltaic system. Due to the fact that inverters are in the category of systems with fast dynamics, in this method, by first determining the system state space and its discrete time model, a switching algorithm is proposed to reduce the larger error for the converter. By using this control method, in addition to reducing the total harmonic distortion (THD), the inverter voltage reaches the set reference value at a high speed. To evaluate the performance of the proposed method, the dynamic performance of the converter at the reference voltage given to the system was investigated. The results of system performance in SIMULINK environment were simulated and analyzed by MATLAB software. According to the simulation results, we can point out the advantage of this system in following the reference signal with high speed and accuracy.

This paper introduces a new dual-input non-isolated zero-voltage switching (ZVS) dc-dc boost converter. The proposed converter hybridizes two bidirectional and unidirectional boost converters, to harvest energy from a rechargeable energy storage element and a clean energy dc generator, respectively. Hence, the proposed circuit utilizes three power switches, one power diode, two inductors and an output filtering capacitor. The ZVS of the all power switches and ZCS for the power diode is realized without any auxiliary circuit. The soft-switching performance is designed to be valid for the whole power ranges of the converter input sources and the output load. The converter power switches are switched in PWM manner with two individual duty ratios, allowing to regulate the converter output voltage and control the extracted power from the input unidirectional port through a simple and useful control system. Depending on the converter duty ratios, its basic operation principle is analyzed in two different operation cases. The proposed converter benefits from simple and compact structure, minimum number of power switches and magnetic elements and high efficiency. The behavior of the proposed converter is analytically investigated, and a low-power laboratory prototype is designed and fabricated to evaluate the converter performance in different operation conditions.

In the current era marked by the growing adoption of renewable energy sources, the use of photovoltaic-powered LED streetlights, known for their enhanced efficiency and extended lifespan, is on the rise. This lighting solution encompasses essential components such as a photovoltaic (PV) panel, an energy storage system, LED luminaires, and a controller responsible for supervising power distribution and system operations. This research introduces a novel approach involving a ZVS (zero-voltage switching) bidirectional boost converter to manage the interaction among the PV panel, LED lights, and battery storage within the system. To elevate system efficiency, a modified version of the conventional bidirectional boost converter is employed, incorporating an auxiliary circuit encompassing a capacitor, inductor, and switch. This configuration enables soft switching in both operational modes. During daytime, the converter operates in the buck mode, accumulating solar energy in the battery. Subsequently, at night, the battery discharges energy to power the LED lights through the converter’s boost operation. In this study, the PET (photo-electro-thermal) theory is harnessed, coupled with insights into heatsink characteristics and the application of a soft-switching bidirectional boost converter. This integrated approach ensures optimal driving of the LED lights at their ideal operating voltage, resulting in the generation of optimal luminous flux. The proposed LED lighting system is thoroughly examined, and theoretical outcomes are validated through simulations using the PSCAD/EMTDC version 4.2.1 software platform.