ABSTRACT The work is focused on determining the energy transfer from EV’s to grid for different operating conditions. The study has been simulated on a scaled model and later validated through experimental tests on batteries of 20 kWh, 40 kWh, 60 kWh and 80 kWh for EV’s energy rates from 130 Wh/km to 180 Wh/km. Tests have been developed to evaluate the energy transfer from driving distance between 0 and 50 km. Results have proved there is a linear dependence between energy transfer and energy rate. Experimental and simulated values have been correlated showing a 99.6% accuracy. The ratio of energy transfer to battery energy capacity depends not only on this latter parameter, but also on the energy rate of the electric vehicle. Experimental tests have shown a variation from a minimum transfer ratio of 54.1% for the highest electric vehicle energy rate and lowest battery energy capacity and power transfer to grid rate to a maximum of 88.6% for the highest power transfer to grid rate and battery energy capacity and lowest electric vehicle energy rate.
The existing power grid has undergone drastic changes within a decade, in order to deal with the increase in energy demand. With the integration of different distributed energy resources (DERs) for a group of interconnected loads within a defined electrical boundary, microgrid came into existence. However, with the increased use of effective communication, automation and monitoring skills the microgrids are technologically advanced with fast response and are referred to as ‘Smart Grids’. In smart grid, efficient and reliable communication is incorporated to improve the efficiency, sustainability, and stability of the whole system. This paper presents a review on the different types of available communication methods and protocols which are used for data communication within and outside a smart grid based power supply system.
The present time is marked by the upsurge of coronavirus (COVID-19) pandemic, which persists and has catastrophic consequences on global health and well-being. In addition to RT-PCR test, CT scan and chest X-ray have become essential in detecting and treating COVID-19 patients. Several deep learning frameworks have been put forward in recent times for the COVID-19 chest X-ray classification. Therefore, to overcome the challenges of data scarcity and lack of interpretability, also to increase the performance of COVID-19 chest X-ray classification, a first of its kind of model is proposed in which transfer learning (TL) and discriminative localisation (DL) are successfully adopted. To verify the superior classification performance of the TL-DL-based C-19CXC model, a set of experiments are conducted on widely used eight pre-trained models like MobileNet, SqueezeNet, etc., on publicly available large datasets. The MobileNet based model outcome accuracy is 98.73% followed by Xception-based framework with 98.34% accuracy.
This paper proposes an energy aware routing protocol for wireless mesh network (WMNs). In such applications the mesh nodes may not expect the fixed power supply connections for their normal operation. Hence, mesh nodes may rely on ambient energy. In such untenable cases, we require energy aware routing protocols to prevent deterioration of the network performance due to power outages of the nodes. This paper proposes an energy aware QoS-enabled routing (EAQER) protocol based on the node weight and path preference probability. The EAQER is analysed and shown superior performance compared to baseline routing protocols in terms of route request messages, route reply messages, average throughput, end to end delay, jitter, and packet delivery ratio.
International airlines have been seeing a consistent change in menu based on their client's interest. This created an impact on the design of an advanced menu that is compiled for a complete diet to provide full nourishment based on airline. Food and beverage service quality has become a feature for international airline companies in contending with one another and in keeping their esteem and noticeable quality in the choice of their specific airline travellers, as the extending wants of aircraft passengers lead to a healthy eating regimen with a customised menu in regard to flight catering. International airlines of various destinations has expanded the variety of food and refreshment with quality according to passengers, further in customising the diet with their desires, and through quality of food and beverages, in order to retain their passengers especially to get them back later on airline. The objective of this study is to identify the quality of food and beverage provided to international airline travellers. Secondly, on knowing the various expectations of passengers like onboard facilities, by upgrading the menu as per passenger's expectation and by the nature of airline administrations, in order to satisfy them with the quality of service.
Smart grid communication requires an embedded approach on IoT-based cloud, fog computing and big data. In order to provide e-health and m-health services, the allocation of tasks on resources in healthcare services is crucial. The primary need for users in the healthcare industry is the solution to the bottleneck of service level agreement (SLA) and accomplishes the quality of service (QoS) parameters. The add-on objective is to achieve effective resource utilization and satisfaction of the end-user application for effective communication and load balancing of tasks on cutting edge technologies. The machine learning approach in osmosis load balancing of tasks at the end of the fog service provider (FSP) level reduces the network utilisation time, latency, usage of energy, etc. The results proves that fog nodes are efficient than the cloud nodes, and also the experimental results proved that the proposed model is efficient than the various other existing approaches.
Implementation of a microgrid (MG) to establish an independent, efficient, and cost-effective power supply system is the need of the hour. The generation in MG can be conventional or non-conventional. Still, due to increasing power demand, high fuel prices, scarcity of fossil fuels, and degrading the environment, there is a growing demand for renewable energy sources (RS) for power generation. Multi-fuel power sources like solar, wind, fuel cells, etc., improve the adequacy, and increase supply reliability, provide a dynamic response. This paper discusses the dynamic scheduling of MGs in two different systems with distinguishing distributed generation (DG) units. Grey wolf optimisation (GWO), a meta-heuristic technique inspired by the hierarchical hunting mechanism of grey wolves, is used in this paper to solve a multi-objective problem in a dynamic environment. The performance and effectiveness of GWO are compared and validated with methods like CSA, ABC, DE, and PSO.
The ability to handle huge amount of data using big graphs and graph data structures plays a vital role in ever growing areas like IOT, social networking, e-commerce and bioinformatics applications. Querying graphs and extracting data in an effective manner is very crucial in big graph processing. This paper presents a framework that focuses on reassignment of vertices among partitions based on query graph so that entire query related information gets shifted to one partition to the extent possible leading to minimised query execution time. This technique first finds the partitions in which the query graph nodes are present and performs searching only in those partitions leading to minimised retrieval time. The proposed QRDA technique is compared with various state-of-art graph querying practices and the outcomes show that QRDA performance is better over other approaches.
In this paper, a cross-layer-based efficient bandwidth reservation mechanism (CLBRM) is proposed for next generation wireless networks. Application layer and network layer are involved in the design of the cross-layer architecture. Cross-layer architecture is used to reduce the delay. Shared database is used to implement the cross-layer architecture which allows to share the information among the application layer and the network layer. Bandwidth is reserved based on the type of requests and past history of the users. Four types of requests are used in this paper and priority is assigned among these requests. Two queues are maintained for last two priority requests and pre-emption of these requests is incorporated in order to serve the highly prioritised requests in a better way. The QoS parameters like delay, throughput, blocking probability, and dropping probability are used to evaluate the performance of the proposed mechanism. The performance of the proposed mechanism, CLBRM is compared with the data driven allocation (DDA) scheme (Fan et al., 2016) and proved to perform better.
In smart grid environment, the integrated service of IoT-based cloud infrastructure has various applications to improve the QoS parameters to achieve the service level agreement (SLA). Allocation of task at the end of the fog service provider (FSP) invites the scheduling queue and sets priority. The task is allocated into the fog nodes when a task arrives into the scheduling queue. Markovian arrival process (MAP) and Markovian Poisson process (MPP) with partial buffer shares mechanism, computes with probabilities and classify them based on the priority. The arriving of tasks on scheduling queue is through MAP, and the allocation of tasks to the fog nodes is through MPP. The method of Markovian self-similar networks is followed for non-priority tasks, and it is also allocated to the fog nodes. The tasks arrive at the scheduling queue is based on priority. It also calculates the probability of task allocated to the fog nodes using MPP for effective schedule management.
An effort has been made to investigate the enhancing performance of a diesel engine using energy and exergy analysis technique propelled with diesel and KB20 (20% Karanja biodiesel by volume blended with 80% diesel by volume). The experiments were conducted per mole of fuel basis on a 3.5 kW single cylinder; water cooled engine with 1,500 rpm. The energy analysis indicates that about 37.23% and 37.31% of input energy is converted into output work for diesel and KB20 fuel respectively. The combustion efficiency of KB20 was higher than that of diesel by 5.35%. Exergetic efficiency of diesel and KB20 was found to be 34.8%. Study indicates that KB20 biodiesel results coherence with the same energetic and exergetic performance as diesel fuel.
In recent days, the expansion of the distribution network followed by network complexity and contingencies are noted as major issues in electric power utilities. At this juncture, there is not even a distinct constituent for handling such disturbances that occurs due to natural hazards which in turn causes a difficult situation for decision making tracked by redundant state estimation. Therefore, effective monitoring in distributed networks with phasor measuring unit (PMU) involving accurate placements and identification of numerous line outages is presented. Here, a technique using ant lion optimisation (ALO) through elite methodology (EALO) for recognising accurate PMU locations considering cascaded line outages is projected. To evaluate the effectiveness of the projected method standard IEEE systems, practical Indian utility system and polish large scale bus networks are instigated. The numerical result cares about the exact state of distribution topology in a way for improving the performance of the smart grid.
In smart grid environment incorporated with IoT-based cloud infrastructure, management of efficiency is a paradigm. This procedure considers an adversary search technique (ADVST). It approaches the MINIMAX algorithm on virtual machines for management of workflows in IoT-based cloud infrastructure, which is named as a VMMINMAX algorithm. The algorithm finds the over-loaded-tasks group named as VMMAXIMISER. The under-loaded-tasks group named as VMMINIMISER and the balanced-tasks group named as VMBALANCED. The VMMINMAX algorithm is used for effective tasks management strategy, and it can be achieved, by moving the workflows from heavily-loaded-VMs to low-loaded-VMs. It balances over VMs as VMBALANCED and reduces the non-critical-workflow-tasks and effective allocation of critical-workflow-tasks, with respect to make-span and average make-span of VMs. The algorithm has been tested with different parameters such as throughput, overhead, resource utilisation, response time, scalability and performance and proved.
