Cluster Analysis and Model Comparison Using Smart Meter Data.

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values

Advanced power routing framework for optimal economic operation and control of solar photovoltaic-based islanded microgrid

© 2019 Institution of Engineering and Technology. All rights reserved. Energy sharing through a microgrid (MG) is essential for islanded communities to maximise the use of distributed energy resources (DERs) and battery energy storage systems (BESSs). Proper energy management and control strategies of such MGs can offer revenue to prosumers (active consumers with DERs) by routing excess energy to their neighbours and maintaining grid constraints at the same time. This paper proposes an advanced power-routing framework for a solarphotovoltaic (PV)-based islanded MG with a central storage system (CSS). An optimisation-based economic operation for the MG is developed that determines the power routing and energy sharing in the MG in the day-ahead stage. A modified droop controller-based real-time control strategy has been established that maintains the voltage constraints of the MG. The proposed power-routing framework is verified via a case study for a typical islanded MG. The outcome of the optimal economic operation and a controller verification of the proposed framework are presented to demonstrate the effectiveness of the proposed powerrouting framework. Results reveal that the proposed framework performs a stable control operation and provides a profit of 57 AU$/day at optimal conditions

Electrothermal modeling and analysis of polypyrrole-coated wearable e-textiles

The inhomogeneity of the resistance of conducting polypyrrole-coated nylon–Lycra and polyester (PET) fabrics and its effects on surface temperature were investigated through a systematic experimental and numerical work including the optimization of coating conditions to determine the lowest resistivity conductive fabrics and establish a correlation between the fabrication conditions and the efficiency and uniformity of Joule heating in conductive textiles. For this purpose, the effects of plasma pre-treatment and molar concentration analysis of the dopant anthraquinone sulfonic acid (AQSA), oxidant ferric chloride, and monomer pyrrole was carried out to establish the conditions to determine the sample with the lowest electrical resistance for generating heat and model the experiments using the finite element modeling (FEM). Both PET and nylon-Lycra underwent atmospheric plasma treatment to functionalize the fabric surface to improve the binding of the polymer and obtain coatings with reduced resistance. Both fabrics were compared in terms of average electrical resistance for both plasma treated and untreated samples. The plasma treatment induced deep black coatings with lower resistance. Then, heat-generating experiments were conducted on the polypyrrole (PPy) coated fabrics with the lowest resistance using a variable power supply to study the distribution and maximum value of the temperature. The joule heating model was developed to predict the heating of the conductive fabrics via finite element analysis. The model was based on the measured electrical resistance at different zones of the coated fabrics. It was shown that, when the fabric was backed with neoprene insulation, it would heat up quicker and more evenly. The average electrical resistance of the PPy-PET sample used was 190 Ω, and a maximum temperature reading of 43 °C was recorded. The model results exhibited good agreement with thermal camera data

New Design of Solar Photovoltaic and Thermal Hybrid System for Performance Improvement of Solar Photovoltaic

© 2020 Ridwone Hossain et al. Solar photovoltaic (PV) and solar thermal systems are most widely used renewable energy technologies. Theoretical study indicates that the energy conversion efficiency of solar photovoltaic gets reduced about 0.3% when its temperature increases by 1°C. In this regard, solar PV and thermal (PVT) hybrid systems could be a solution to draw extra heat from the solar PV panel to improve its performance by reducing its temperature. Here, we have designed a new type of heat exchanger for solar PV and thermal (PVT) hybrid systems and have studied the performance of the system. The PVT system has been investigated in comparison with an identical solar PV panel at outdoor condition at Dhaka, Bangladesh. The experiments show that the average improvement of open circuit voltage (Voc) is 0.97 V and the highest improvement of Voc is 1.3 V. In addition, the overall improvement of output power of solar PV panel is 2.5 W

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

Global variation in anastomosis and end colostomy formation following left-sided colorectal resection

Background End colostomy rates following colorectal resection vary across institutions in high-income settings, being influenced by patient, disease, surgeon and system factors. This study aimed to assess global variation in end colostomy rates after left-sided colorectal resection. Methods This study comprised an analysis of GlobalSurg-1 and -2 international, prospective, observational cohort studies (2014, 2016), including consecutive adult patients undergoing elective or emergency left-sided colorectal resection within discrete 2-week windows. Countries were grouped into high-, middle- and low-income tertiles according to the United Nations Human Development Index (HDI). Factors associated with colostomy formation versus primary anastomosis were explored using a multilevel, multivariable logistic regression model. Results In total, 1635 patients from 242 hospitals in 57 countries undergoing left-sided colorectal resection were included: 113 (6·9 per cent) from low-HDI, 254 (15·5 per cent) from middle-HDI and 1268 (77·6 per cent) from high-HDI countries. There was a higher proportion of patients with perforated disease (57·5, 40·9 and 35·4 per cent; P < 0·001) and subsequent use of end colostomy (52·2, 24·8 and 18·9 per cent; P < 0·001) in low- compared with middle- and high-HDI settings. The association with colostomy use in low-HDI settings persisted (odds ratio (OR) 3·20, 95 per cent c.i. 1·35 to 7·57; P = 0·008) after risk adjustment for malignant disease (OR 2·34, 1·65 to 3·32; P < 0·001), emergency surgery (OR 4·08, 2·73 to 6·10; P < 0·001), time to operation at least 48 h (OR 1·99, 1·28 to 3·09; P = 0·002) and disease perforation (OR 4·00, 2·81 to 5·69; P < 0·001). Conclusion Global differences existed in the proportion of patients receiving end stomas after left-sided colorectal resection based on income, which went beyond case mix alone

EnTruVe: ENergy and TRUst-aware Virtual Machine Allocation in VEhicle Fog Computing for Catering Applications in 5G

It is undoubted that fog computing contributes in catering the latency-stringent applications of 5G, and one of the enabling technologies that fundamentally ensure the success of fog computing is virtualization as it offers isolation and platform independence. Although the emergence of vehicle-based fog (referred to as v-fog) facilities can certainly benefit from these desirable features of virtualization, there are several challenges that need to be addressed in order to realize the full potential that v-fogs can offer. One of the challenges of virtualization in v-fog is Virtual Machine (VM) migration. There are several factors that trigger a VM migration in a v-fog such as vehicle resource depletion. VM migrations would not only lead to nonessential usage of valuable resources (e.g. energy, bandwidth, memory) in the v-fogs, but also incur various overheads and performance degradation throughout the whole network. Thus, minimizing VM migrations is necessary. Furthermore, to ensure the seamless VM migrations between v-fogs, trust of v-fogs is required. While there exists studies of trust in the virtualization of cloud, they are irrelevant to v-fogs as v-fogs are different in nature (i.e. heterogeneous, mobile) from the cloud. Additionally, trust is not included in the decision making mechanisms of VM allocation for vehicular environments in the existing works. Moreover, as vehicle resources are constrained, their energy has to be utilized efficiently. In this paper, we propose EnTruVe, an ENergy and TRUst-aware VM allocation in VEhicle fog computing solution that aims to minimize the number of VM migration while reducing VM processing associated energy consumption as much as possible. The VM allocation algorithm in EnTruVe provides a larger selection pool of v-fogs that meets the VMs requirements (e.g. trust, latency), thereby ensuring higher chances of success of VM allocation. Using Analytic Hierarchy Process (AHP), the proposed EnTruVe solution evaluates the v-fogs based on a set of metrics (e.g. energy consumption, end-to-end latency) to select the optimal v-fog for a VM allocation. Results obtained demonstrate that EnTruVe has the least number of VM migrations and it is the most energy efficient solution. Additionally, it shows that EnTruVe provides the highest utilization of v-fogs of up to 57.6% in comparison to other solutions as the number of incoming requests increases

The High Frequency Magnetic-Link with Distributed HTS YBCO Windings for Power Converter Applications

High frequency magnetic-link (HFML) is widely used in medium/high voltage power electronic converters for various applications. In this paper, a 440 V, 100 kHz compact HFML is designed with ANSYS/Maxwell software environment where the amorphous material is used as the magnetic core. The HFML is analyzed with solid toroidal cores with various winding configurations. A comparison of the HFML is performed for both the conventional copper winding and superconducting winding. Yttrium barium copper oxide material-based superconductor tape is applied to obtain the maximum power of the HFML. In addition, suitable winding configuration is selected to ensure possible maximum electrical power transfer while minimizing the magnetic saturation in the core. Simulation results show the electrical parameters of the HFML along with its magnetic flux density and power. The proposed HFML design with distributed winding is validated experimentally with a small-scale prototype where it is found that the maximum power transfer capability is increased more than 48% than that of the conventional one

Design and optimization of ECG modeling for generating different cardiac dysrhythmias

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (&gt;0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used