Search Results (16)

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally. Full article

In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset. Full article

The occurrence of an electric arc is an inherent possibility when working in a live line. Thermal risk is considered one of the most relevant risks associated with this type of event, so every worker who performs daily activities in the electricity distribution sector is subject to this risk to a lesser or greater degree. The quantification of the thermal risk is based on estimating the energy generated by the electric arc, and focuses on a surface called incident energy. Therefore, the incident energy estimate is used to define thermal protection strategies applicable to workers who carry out activities in the distribution sector. In light of this, the present work proposes a review of incident energy estimation methods for electric arcs in electric power distribution grids, highlighting the applicability of each of these methods in terms of voltage, short circuit type, and environment. Finally, five case studies are presented in which adequate methods are used to estimate the incident energy. Their results concerning incident energy and selected protective equipment (PPE) are compared. From the review of existing incident energy estimation methods in the literature and the case studies presented, the importance of selecting a suitable method for the type of system analyzed, considering electric, spatial, and intervention aspects, is observed. Finally, considering that the primary objective of incident energy estimation is to determine the need and selection of arc-rated PPE, the selected estimation method must be aligned and integrated with the clothing selection procedures and applicable PPE. Full article

A 10 kV distribution network is a crucial piece of infrastructure to guarantee enterprises’ and households’ access to electricity. Stripping cables is one of many power grid maintenance procedures that are now quickly expanding. However, typical cable-stripping procedures are manual and harmful to workers. Although numerous automated solutions for grid maintenance have been created, none of them focus on cable stripping, and most of them have large dimensions to guarantee multi-functions. In this paper, a new cable-stripping robot for the 10 kV power system is introduced. The design of a live working cable-stripping robot that is appropriate for installing insulating rods is introduced, taking into account the working environment of 10 kV overhead lines and the structural characteristics of overhead cables. The robot is managed by an auxiliary remote control device. A cascade PID control technology based on the back propagation neural network (BPNN) method was developed, as the stripper robot’s whole system is nonlinear and the traditional PID controller lacked robustness and adaptability in complex circumstances. To validate the structural feasibility of the cable-stripping robot, as well as the working stability and adaptability of the BPNN–PID controller, a 95 mm² cable-stripping experiment are carried out. A comparison of the BPNN–PID controller with the traditional PID method revealed that the BPNN–PID controller has a greater capacity for speed tracking and system stability. This robot demonstrated its ability to replace manual stripping procedures and will be used for practical routine power maintenance. Full article

Frontline power grid workers are always facing plenty of stressors such as aerial work and high job demands, which may lead them to be less satisfied with their job. Therefore, this study aims to investigate frontline power grid workers’ job satisfaction (JS) and explore how it can be improved by its relationship with personality traits and work–family support (WFS). Data from 535 frontline power grid workers were collected from two power supply bureaus in Guangdong Province, China. Structural equation modeling (SEM) was adopted to examine the structural relationship between personality traits taken as independent variables, JS as dependent variable, and WFS as mediator. The bootstrap method was used to test the significance of indirect effects. Results suggested the overall job satisfaction of our sample is 3.34 ± 0.55 on a scale ranging from 1 to 5, and significantly correlated with personality traits and WFS. Moreover, the results of SEM and bootstrap indicated that WFS partially mediates the effect of neuroticism on JS and fully mediates the effect of conscientiousness and extraversion on JS. These findings shed light on how personality traits and environmental factors jointly impact JS and highlight the important role of WFS among frontline power grid workers. Full article

Quality in healthcare service is essential in giving rural dwellers a good standard of living. It has been established that many rural locations in Sub-Saharan Africa away from the grid connection have difficulty accessing electricity. The inaccessibility of reliable energy and essential medical equipment was the leading barrier to improved healthcare delivery in these rural locations. The deficiency of basic medical equipment to power essential services due to limited or unreliable electricity access has reduced rural healthcare workers’ care capabilities, resulting in higher mortality rates. This paper, therefore, reviews the existing energy solutions for rural healthcare facilities, thereby analysing different approaches and the geographical energy mix and ascertaining the effectiveness of various techniques and energy mix as solutions to effective healthcare delivery in healthcare centres. Hybrid Renewable Energy Sources (HRES) microsystems, like microgrids incorporated with solar panels and battery, is identified to ensure higher and more reliable energy access in rural healthcare centres. At the same time, the adoption of Demand Side Management (DSM) in the HRES deployment in countryside healthcare facilities is reported to decrease the initial cost of installation and improve efficiency. Lastly, in improving energy access, rural electrification planning is achieved through modelling tools related to energy access modelling. Full article

In workplaces such as steel, power grids, and construction, firefighters and other workers often encounter non-uniform high-temperature environments, which significantly increase the risk of local heat stress and local heat discomfort for the workers. In this paper, a multi-segment human bioheat model is developed to predict the human thermal response in asymmetric high-temperature environments by considering the sensitivity of the modeling to angular changes in skin temperature and the effects of high temperatures on human thermoregulatory and physiological responses simultaneously. The extended model for asymmetric high-temperature environments is validated with the current model results and experimental data. The result shows that the extended model predicts the human skin temperature more accurately. Under non-uniform high-temperature conditions, the local skin temperature predictions are highly consistent with the experimental data, with a maximum difference of 2 °C. In summary, the proposed model can accurately predict the temperature of the human core and skin layers. It has the potential to estimate human physiological and thermoregulatory responses under uniform and non-uniform high-temperature environments, providing technical support for local heat stress and local thermal discomfort protection. Full article

Developing and underdeveloped countries face innumerable problems related to the accessibility and quality of energy that put the lives of patients, health-care infrastructures, and health workers at risk. Current approaches, such as grid power, unsustainable energy sources such as diesel or gas, and mobile health clinics, have proven insufficient to address this issue. In response, access to reliable health care and electricity has undergone multiple transformations in the last decade, especially in remote and rural areas. Good health and clean energy are two of the 17 United Nations Sustainable Development Goals, originally designed to be a “shared blueprint for peace and prosperity for people and the planet, now and into the future.” Unfortunately, little is known about the interaction between health-care access and energy access in developing and underdeveloped countries, mainly in remote or rural areas. For this reason, this study conducts a review of the literature, including current approaches, challenges, and opportunities for the implementation of solar energy in health centers. As a result, several challenges and opportunities in three impact areas are presented: (1) operational, (2) environmental, and (3) economic. This study delivers detailed information that allows the implementation of solar energy in the health-care sector (in a more effective manner) by sharing best practices. Full article

Power grid outdoor workers are usually exposed to hot environments and could suffer the threats to occupational health and safety like heat strain and injury. In order to predict and assess the thermophysiological responses of grid workers in the heat, the clothing thermal insulation of grid worker ensembles was measured by a thermal manikin and a multi-segment human bioheat model was employed to evaluate the thermophysiological response parameters of grid workers such as core temperature, skin temperature and sweat loss. The results show that working in a hot environment can cause a obvious increase in core temperature and skin temperature of grid workers, and the acceptable maximum working time of grid workers varies greatly in different hot environments. A reasonable work organization strategy can effectively decrease the core temperature and sweat loss, increasing the duration of acceptable maximum working time for grid workers. This study is helpful to assess heat-related risks of grid workers and support power grid companies to rationalize work organization strategies and personal protection guidelines. Full article

It has been proven that the gas trifluoromethanesulfonyl fluoride (CF₃SO₂F) has good dielectric performance and the potential to replace the potent greenhouse gas sulfur hexafluoride (SF₆), which is the most commonly used insulating gas in high-voltage electrical equipment. However, there are many key biochemical properties, such as toxicity, that the new eco-friendly insulating gas will need to obtain. It could protect the operator and equipment and help the chemical engineering development of this gas in the power grid industry. In this study, according to Horn’s method, an acute toxic gas inhalation test was carried out. The results showed that the lethal concentration of 50% (LC50) for female rats was 27.1 g/m³, and that for male rats was 23.3 g/m³. The behavioral and vital sign changes in the rats were recorded. Pathological sections of the main organs revealed that the heart, lungs, spleen, and eyes suffered the most damage from the gas. This research also provides scientific suggestions for the protection of electrical workers exposed to the insulating gas CF₃SO₂F. Full article

This paper demonstrates the investigation of the acquired outcomes from consistent information observing a 467.2 kWp solar photovoltaic (SPV) framework commissioned on the roofs of three separate high-rise buildings, which are located at the location of 26.9585° N and 80.9992° E. Onside real-time performance for this system was investigated for three years, 2018–2020; this system contains 1460 SPV panels of 320 Wp each, having 20 PV panels per string, 09 DC/AC power conditioning units (PCU), and a SCADA (supervisory control and data acquisition) system for monitoring the other necessary parts of a grid-interactive SPV system. The outcomes of the different buildings are compared with each other to analyze the power output at the same input conditions. Hardware components of the plants with approximately the same ratings (P₂ ~ 108.8 kWp + P₃ ~ 128 kWp) are compared (with P₁ ~ 230.4 kWp). Simulation modeling of the year 2020 in PVsyst tool for generated energy, Performance Ratio (PR), and Capacity Utilization Factor (CUF) are carried out additionally and compared with the installed rooftop grid-interactive SPV system of 467.2 kWp (~P₁ + P₂ + P₃) at the site. Numerous performance parameters such as array efficiency, inverter efficiency, system efficiency, Performance Ratio (PR), and Capacity Utilization Factor (CUF) of the plant are evaluated and compared with already installed systems in different regions of the world. These points demonstrate great feedback to framework architects, workers, designers, and energy suppliers regarding the genuine limit and plausibility of the framework they can offer to clients. Moreover, one of the environmental benefits of the SPV plant is that the 467.2 kWp PV framework reduces the tremendous measure of CO₂, SO₂, and NO_X that is discharged into the air. Full article

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset. Full article

Sulfur hexafluoride (SF₆) shows excellent insulation performance as an insulating gas. It is suitable for various climate conditions due to its low boiling point (−64 °C). Therefore, it has been widely used in power grid equipment. However, its global warming potential (GWP) is 23,500 times higher than that of CO₂. Thus, it is imperative to find an environmentally friendly insulating gas with excellent insulation performance, lower GWP, and which is harmless to equipment and workers to replace SF₆. In this review, four possible alternatives, including perfluorocarbons, trifluoroiodomethane, perfluorinated ketones, and fluoronitrile are reviewed in terms of basic physicochemical properties, insulation properties, decomposition properties, and compatibility with metals. The influences of trace H₂O or O₂ on their insulation performances are also discussed. The insulation strengths of these insulating gases were comparable to or higher than that of SF₆. The GWPs of these insulating gases were lower than that of SF₆. Due to their relatively high boiling point, they should be used as a mixture with buffering gases with low boiling points. Based on these four characteristics, perfluorinated ketones (C₅F₁₀O and C₆F₁₂O) and fluoronitrile (C₄F₇N) could partially substitute SF₆ in some electrical equipment. Finally, some future needs and perspectives of environmentally friendly insulating gases are addressed for further studies. Full article

Standalone renewable energy is widely used to power irrigation systems. However, in agricultural facilities, electricity from the grid and diesel are also consumed. The design and sizing of renewable generation involves difficulties derived from the different seasonal profiles of production and demand. If the generation is 100% renewable, a considerable energy surplus is usually included. This paper is focused on a renewable energy system, which has been installed in a vineyard, located in the northeast of Spain. With energy from the photovoltaic fields, the wastewater treatment plant of the winery, a drip irrigation system and other ancillary consumptions are fed. The favourable effect of combining consumptions with different seasonal profiles is shown. The existence of some deferrable loads and the energy management strategy result in an aggregate consumption curve that is well suited to production. Besides, the required energy storage is relatively small. The surplus energy is used for the on-site production of hydrogen by the electrolysis of water. The hydrogen refuels a hybrid fuel cell electric vehicle, used for the mobility of workers in the vineyard. In summary, electricity and hydrogen are produced on-site (to meet the energy needs) from 100% renewable sources and without operating emissions. Full article

Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods. Full article