In this study, we analyzed the impact of multi-terminal direct current (MTDC) system on the integration of renewable energy resources into the Korean power system. Due to the large-scale renewable energy plants planned to be integrated into the power system, line congestion is expected in the southern part of power system. Given the difficulty in constructing AC transmission lines due to social conflicts, we proposed an alternative solution using an offshore multi-terminal DC offshore transmission system. Firstly, we calculate the effective renewable energy plant generation capacity based on annual wind and solar radiation data. Next, we conduct PSS/E simulations to minimize future line congestion in the Korean power grid. The offshore terminal is designed to transfer the power generated in southern Korea and is verified using different terminal rating cases. The simulation result, including contingency analysis, demonstrate that transferring 80% of the generated renewable power achieves the best line flow condition. Therefore, the MTDC system is a possible candidate for integrating future renewable energy systems into the Korean power grid.

The coronavirus (COVID-19) is identified at first in Wuhan in December 2019. The apparition of the COVID-19 virus is widely spread to concern all countries worldwide. The World Health Organization (WHO) on March 11 declare COVID-19 a pandemic. This Virus causes a serious infection of the respiratory system. Its high transmission constitutes great problems and challenges. The WHO proposes many actions to limit the spread of the virus such as quarantine and decrease or halt flights between states. The actions taken by states in airports are to detect suspicious persons with COVID-19. We aimed to provide a Computer-Aided Diagnosis (CAD) framework to predict suspicious COVID-19 person. This prediction identifies suspicious persons who suffer from shortness breath which is the main symptom of this disease. Extract shortness breath anomaly through the estimated heart rate from face based-video is the main contribution of the present paper. We developed a Smart Recognition COVID-19 (SRC) system to estimate the breath score. In conclusion, our study achieves an accurate breath score. The error is about 1 breath per minute. The proposed solution is of great importance because it helps managers in the airport to predict suspicious COVID-19 passengers.

This paper proposes a SUGPDS model based on Detection and Isolation algorithm and smart sensors, namely micro phasor measurement unit, smart sensing and switching device, phasor data concentrator, and ZigBee technology, etc. for the identification, classification, and isolation of the various fault occurs in the underground power cable in the distribution system. The proposed SUGPDS is a quick and smart tool in supervising, managing, and controlling various faults and issues and maintaining the reliability, stability, and uninterrupted flow of electricity. First, the SUGPDS model is analyzed using a distributed parameter approach. Then, the proper arrangement of the system required for the implantation of SUGPDS is demonstrated using figures. The Phasor data concentrator plays an essential role in developing the detection and classification report for identification and classification. Finally, smart sensing and switching device installed at a different location isolated the faulty phase from a healthy network. This approach helps to decrease power consumption. Hence, SUGPDS has super abilities compared to the underground power distribution system. The effectiveness of the proposed method and model is demonstrated via figures and tables.

An examination of the human gait is feasible using inertial sensing. The embedded accelerometer and gyroscope in an inertial measurement unit can evaluate physical activity-based sports and this unit is relatively affordable compared to global positioning systems or video recording quantification. This study developed a cost-effective sports monitoring investigation method with an inertial sensor attached to the right leg of the athletes. In total, four parameters were simultaneously tracked to assess the entire sensor performance in real-time. The accelerometer measured the typical leg angle when walking and running, whereas the gyroscope processed the raw data to obtain the stride frequency from the time-domain data. Moreover, a comparison between the accelerometer and gyroscope was presented while simultaneously attaining the signal to convert the time-domain data to frequency results. Also, the number of strides and linear velocity was expressed as results in this study. To confirm the results, a statistical hypothesis test was implemented for all obtained results. The results indicated that the inertial sensing instrumentation used in this study is promising and could be an affordable alternative option for a sports monitoring system.

This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.

Magnetic energy loss of SiFe steel represents a key factor for the efficiency of soft magnetic machine cores. Traditionally, they are operated with 50 Hz (or 60 Hz), a frequency value that yields rather balanced portions of hysteresis loss and eddy current loss. In equivalent circuits of transformers, tends to be represented by a magnetic power resistance , as a constant. For the most important case of sinusoidal induction of 50 Hz, this would correspond to an instantaneous magnetization power function () that is sinusoidal as well, however, with 100 Hz (or 120 Hz). On the other hand, from complex, non-linear mechanisms of hysteresis, it is obvious that () should be strongly non-sinusoidal, even for exactly sinusoidal (). So far, almost all corresponding instantaneous investigations were restricted to calculated modelling of loss portions and transient modelling. On the other hand, for the first time, the present study was focussed on functions () as measured at IEC-standardized samples of industrially relevant steel. Practical evaluations are discussed with respect to the revealed "history" of magnetization processes, as well as for product characterization. For these tasks, a novel digitized "Low-mass Single Sheet Tester" was developed that was applied for both non-oriented steel (NO) and grain-oriented steel (GO), for 50 Hz. Interpretations proved to be favoured by relating () to total , according to an instantaneous power ratio. As a result, both steel types revealed strongly non-sinusoidal power functions, with short durations of negative . Negative proved to be most pronounced for NO steel, as a measure for the onset of reversible turns of atomic moments. As a consequence, () comprises strong upper harmonics of 200 Hz and even 300 Hz. Based on theoretical considerations, we split () in a dissipative loss power function () and in a potential energy power function (). Finally, we used () to determine the corresponding power resistance () that proves to be a distinctly nonlinear function as well. It resembles a rectified co-sinus, also exhibiting short negative spikes that reflect the crystallographic dis-orientation of the polycrystalline material.

This paper investigates the formation control problem for multiple nonholonomic wheeled mobile robots using distributed estimators and a biologically inspired approach. The formation pattern of the system adopts leader-follower structure and the communication topology among the multi-robot system is modelled by an undirected graph. In our proposed methodology, first, we develop an adaptive trajectory tracking control for the leader robot to follow the desired trajectory. Second, a distributed estimator is designed for each follower mobile robot, which uses its own information to estimate the leader's states, such as position, orientation, and linear velocity. Then, distributed formation tracking control laws are designed based on the distributed estimator. Furthermore, a bioinspired controller is developed to address the impractical velocity jump problem. The closed-loop system stability is analysed with the Lyapunov stability theory showing that tracking errors are asymptotically converge to zero. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods.

This paper describes a basic study on the measurement of return loss and change in Smith chart using a microstrip patch antenna (MPA) with concentration transition to perform non-invasive blood glucose measurements. To evaluate blood glucose level changes in the human body, the concentration of measurements was changed 10 times to an equivalent of 100 mg/dL in a range of 0-1000 mg/dL to reflect a concentration of 400 mg/dL, which is the fatal level of diabetes. Five types of MPAs were fabricated that formed resonant frequencies in the 1, 2, 3, 4, and 5 GHz bands, and were used in the experiment. Each MPA constituted a sharp narrow band characteristic and induced a large change in return loss. By measuring the return loss and Smith chart to evaluate the concentration change at resonant frequencies, the return loss was observed to change by an average of 0.058 dB for every 100 mg/dL change, and the impedance magnitudes and phase angles analyzed through the Smith chart were observed to have a certain tendency, which confirmed that they were changed. This study shows that for performing non-invasive measurements of blood sugar level, measuring the change in return loss can provide a more stable and reliable measurement compared to the two methods that are simultaneously used to view the samples.

This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.

With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.