The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.
Smart Microgrid (MG) effectively contributes to supporting the electrical power systems as a whole and reducing the burden on the utility grid by the use of unconventional energy generation resources, in addition to backup Diesel Generators (DGs) for reliability increasing. In this paper, potential had been done on day-ahead scheduling of diesel generators and reducing the energy cost reached to the consumers side to side with renewable energy resources, where economical energy and cost-effective MG has been used based on optimization agent called Energy Management System (EMS). Improved Particle Swarm Optimization (IPSO) technique has been used as an optimization method to reduce fuel consumption and obtain the lowest energy cost as well as achieving the best performance to the energy system. Three scenarios are adopted to prove the efficiency of the proposed method. The first scenario uses a 24 hour time horizon to investigate the performance of the model, the second scenario uses two DGs and the third scenario depends on a 48-hour time horizon to validating the performance. The superiority of the proposed method is illustrated by comparing it with PSO and simulation results show using the proposed method can reducing the fuel demand and the energy cost by satisfying the user’s preference.
Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.
In this paper, we develop an analytical energy efficiency model using dual switched branch diversity receiver in wireless sensor networks in fading environments. To adapt energy efficiency of sensor node to channel variations, the optimal packet length at the data link layer is considered. Within this model, the energy efficiency can be effectively improved for switch-and-stay combiner (SSC) receiver with optimal switching threshold. Moreover, to improve energy efficiency, we use error control of Bose-Chaudhuri-Hochquengh (BCH) coding for SSC-BPSK receiver node compared to one of non-diversity NCFSK receiver of sensor node. The results show that the BCH code for channel coding can improve the energy efficiency significantly for long link distance and various values of high energy consumptions over Rayleigh fading channel.
The hybrid AC/DC microgrid is considered to be more and more popular in power systems as increasing loads. In this study, it is presented that the hybrid AC/DC microgrid is modeled with some renewable energy sources (e.g. solar energy, wind energy) in the residential of the consumer in order to meet the demand. The power generation and consumption are undergoing a major transformation. One of the tendencies is to integrate microgrids into the distribution network with high penetration of renewable energy resources. In this paper, a new distributed coordinated control is proposed for hybrid microgrid, which could apply to both grid-connected mode and islanded mode with hybrid energy resources and variable loads. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. Also, the maximum power point tracking technique is applied to both photovoltaic stations and wind turbines to extract the maximum power from the hybrid power system during the variation of the environmental conditions. Finally, a simulation model is built with a photovoltaic, wind turbine, hybrid microgrid as the paradigm, which can be applied to different scenarios, such as small-sized commercial and residential buildings. The simulation results have verified the effectiveness and feasibility of the introduced strategy for a hybrid microgrid operating in different modes
Energy constraint has become the major challenge for designing wireless sensor networks. Network lifetime is considered as the most substantial metric in these networks. Routing technique is one of the best choices for maintaining network lifetime. This paper demonstrates implementation of new methodology of routing in WSN using firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor networks routing. For network cutoff avoidance while maximize net lifetime energy exhaustion must be balanced. Balancing energy consumption is the key feature for rising nets lifetime of WSNs. This routing technique involves determination of optimal route from node toward sink to make energy exhaustion balance in network and in the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime.
Correct calculations of losses are important for several reasons. There are two basic methods that can be used to calculate technical energy losses, a method based on subtraction of metered energy purchased and metered energy sold to customers and a method based on modeling losses in individual components of the system. For considering the technical loss in distribution system included: transmission line losses, power transformer losses, distribution line losses and low-voltage transformer losses. This work presents an evaluation of the power losses in Kirkuk electric distribution system area and submit proposals and appropriate solutions and suggestions to reduce the losses . A program under Visual Basic was designed to calculate and evaluate electrical energy losses in electrical power systems.
The ability to harvest energy from the environment represents an important technology area that promises to eliminate wires and battery maintenance for many important applications and permits deploying self powered devices. This paper suggests the use of a solar energy harvester to charge mobile phone devices. In the beginning, a comprehensive overview to the energy harvesting concept and technologies is presented. Then the design procedure of our energy harvester was detailed. Our prototype solar energy harvester proves its efficiency to charge the aimed batteries under sunlight or an indoor artificial light.
The growth in energy consumption and the lack of access to the electricity network in remote areas, rising fossil fuel prices, the importance of using renewable energy in these areas is increasing. The integration of these resources to provide local loads has introduced a concept called microgrid. Optimal utilization of renewable energy systems is one of their most important issues. Due to the high price of equipment such as wind turbine, solar panels and batteries, capacity sizing of the equipment is vital. In this paper, presents an algorithm based on techno-economic for assessment optimum design of a renewable energy system including photovoltaic system, batteries and wind turbine is presented.
The power theft is one of the main problems facing the electric energy sector in Iraq, where a large amount of electrical energy is lost due to theft. It is required to design a system capable of detecting and locating energy theft without any human interaction. This paper presents an effective solution with low cost to solve power theft issue in distribution lines. Master meter is designed to measures the power of all meters of the homes connected to it. All the measured values are transmitted to the server via GPRS. The values of power for all energy meters within the grid are also transmitted. The comparison between the power of the master meter and all the other meters are transmitted to the server. If there is a difference between the energy meters, then a theft is happened and the server will send a signal via GSM to the overrun meter to switch off the power supply. Raspberry pi is used as a server and equipped and programmed to detect the power theft.
In this research we study the elevations of cities and the water resources specially at the dams reservoirs and the distance between them(dams & cities), we use the Google Earth program to determine these elevations and calculate the difference between the average level (elevation) of water at the dam and the average level of cities, which we want to supply it by water, in order to save electrical power by using the energy of supplied water through pipe line from dams to the cities, the pressure of supplied water must be calculated from the difference in elevations(head). The saving of energy can be achieved by two ways. The first is the energy saving by reduce the consumed power in the pumping water from river, which is used for different purposes. The second is the hydroelectric power generated by establishing a micro hydroelectric generator on the pipe line of the water supplied.
In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fast- changing power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy.
The rapid progress in mobile computing necessitates energy efficient solutions to support substantially diverse and complex workloads. Heterogeneous many core platforms are progressively being adopted in contemporary embedded implementations for high performance at low power cost estimations. These implementations experience diverse workloads that offer drastic opportunities to improve energy efficiency. In this paper, we propose a novel per core power gating (PCPG) approach based on workload classifications (WLC) for drastic energy cost minimization in the dark silicon era. Core of our paradigm is to use an integrated sleep mode management based on workloads classification indicated by the performance counters. A number of real applications benchmark (PARSEC) are adopted as a practical example of diverse workloads, including memory- and CPU-intensive ones. In this paper, these applications are exercised on Samsung Exynos 5422 heterogeneous many core system showing up to 37% to 110% energy efficient when compared with our most recent published work, and ondemand governor, respectively. Furthermore, we illustrate low-complexity and low-cost runtime per core power gating algorithm that consistently maximize IPS/Watt at all state space.
This work presents a wireless communication network (WCN) infrastructure for the smart grid based on the technology of Worldwide Interoperability for Microwave Access (WiMAX) to address the main real-time applications of the smart grid such as Wide Area Monitoring and Control (WAMC), video surveillance, and distributed energy resources (DER) to provide low cost, flexibility, and expansion. Such wireless networks suffer from two significant impairments. On one hand, the data of real- time applications should deliver to the control center under robust conditions in terms of reliability and latency where the packet loss is increased with the increment of the number of industrial clients and transmission frequency rate under the limited capacity of WiMAX base station (BS). This research suggests wireless edge computing using WiMAX servers to address reliability and availability. On the other hand, BSs and servers consume affected energy from the power grid. Therefore, the suggested WCN is enhanced by green self-powered based on solar energy to compensate for the expected consumption of energy. The model of the system is built using an analytical approach and OPNET modeler. The results indicated that the suggested WCN based on green WiMAX BS and green edge computing can handle the latency and data reliability of the smart grid applications successfully and with a self-powered supply. For instance, WCN offered latency below 20 msec and received data reliability up to 99.99% in the case of the heaviest application in terms of data.
Energy consumption problems in wireless sensor networks are an essential aspect of our days where advances have been made in the sizes of sensors and batteries, which are almost very small to be placed in the patient's body for remote monitoring. These sensors have inadequate resources, such as battery power that is difficult to replace or recharge. Therefore, researchers should be concerned with the area of saving and controlling the quantities of energy consumption by these sensors efficiently to keep it as long as possible and increase its lifetime. In this paper energy-efficient and fault-tolerance strategy is proposed by adopting the fault tolerance technique by using the self-checking process and sleep scheduling mechanism for avoiding the faults that may cause an increase in power consumption as well as energy-efficient at the whole network. this is done by improving the LEACH protocol by adding these proposed strategies to it. Simulation results show that the recommended method has higher efficiency than the LEACH protocol in power consumption also can prolong the network lifetime. In addition, it can detect and recover potential errors that consume high energy.
The Intelligent Control of Vibration Energy Harvesting system is presented in this paper. The harvesting systems use a me- chanical vibration to generate electrical energy in a suitable form for use. Proportional-Integrated-derivative controller and Fuzzy Logic controller have been suggested; their parameters are optimized using a new heuristic algorithm, the Camel Trav- eling Algorithm(CTA). The proposed circuit Simulink model was constructed in Matlab facilities, and the model was tested under various operating conditions. The results of the simulation using the CTA was compared with two other methods.
In developing nations, such as Iraq, supplying power to isolated and rural border areas that are not connected to the grid continues to be a problem. At present, fossil fuels, which are significant causes of pollution, supply around 80% of the world’s energy demands. Nonetheless, drastically reducing reliance on fossil fuels has many reasons, including depleting global fossil fuel supplies, increasing costs and growing energy needs. The present study examines the electrical requirements of the Al-Teeb area, a city situated in the eastern region of Iraq, close to the Iranian border. This region has not been researched despite its tourism and oil significance. Despite the unpredictable expansion of many isolated locations in Iraq in recent years, the number of generation stations has not changed. Supplying energy to these places will require considerable time and money. Photovoltaics (PV), wind turbines (WTs), diesel generators (DGs), batteries and converters combined on the basis of their compatibility under three distinct scenarios comprise the system’s components. Considering the lowest net present cost (NPC) and cost of energy (COE) of all the examined scenarios, PV, WTs, batteries and DGs are the most economical solutions for the Al-Teeb area. Number of PV (1,215), number of WTs (59), number of DGs (13), number of batteries (3,138), number of converters (47), COE (0.155 US$/kWh), NPC (14.2 million US$) and initial capital cost (4.91 million US$) are revealed by the results. Finally, the results are confirmed using another global optimization method, namely, modified particle swarm optimization.
Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA) board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional- integral (PI) controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker .
Wireless sensor networks have many limitations such as power, bandwidth, and memory, which make the routing process very complicated. In this research, a wireless sensor network containing three moving sink nodes is studied according to four network scenarios. These scenarios differ in the number of sensor nodes in the network. The RPL (Routing Protocol for low power and lossy network) protocol was chosen as the actual routing protocol for the network based on some routing standards by using the Wsnet emulator. This research aims to increase the life of the network by varying the number of nodes forming it. By using different primitive energy of these nodes, this gives the network to continue working for the longest possible period with low and fair energy consumption between the nodes. In this work, the protocol was modified to make the sink node move to a specific node according to the node’s weight, which depends on the number of neighbors of this node, the number of hops from this node to the sink node, the remaining energy in this node, and the number of packets generated in this node. The simulation process of the RPL protocol showed good results and lower energy consumption compared to previous researches.
Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node's centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.
This paper concerns with exploitation of renewable energy sources for meeting energy requirements of remote locations. It presents an investigation which is based on a practical project that was executed in collaboration between academia and industry. It involves design and installation of a prototype integrated renewable energy system which consists of two 15 kW wind turbines, electrolyser, fuel cell system (FCS) and the associated control equipment. It was installed at the furthest island of Shetland, North of Scotland, U.K. The philosophy used in designing this system is summarised as follows: During times of high wind, the electricity generated by wind turbines is normally greater than that required by site electrical load. The excessive amount of generated electricity is stored into Hydrogen by utilising an electrolyser which is then used to generate the deficient electric power by the FCS at times of low wind.
With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.
The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.
Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.
Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli- Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.
The tremendous development in the field of communications is derived from the increasing demand for fast transmission and processing of huge amounts of data. The non-orthogonal multiple access (NOMA) system was proposed to increase spectral efficiency (SE) and improve energy efficiency (EE) as well as contribute to preserving the environment and reducing pollution. In the NOMA system, a user may be considered as a relay to the others that support the coverage area based on adopting the reuse of the frequency technique. This cooperation enhances the spectral efficiency, however, in the cell, there are other users that may affect the spectral allocation that should be taken into consideration. Therefore, this paper is conducted to analyze the case when three users are available to play as relies upon. The analyses are performed in terms of the transmitted power allocation in a fair manner, and the system's performance is analyzed using the achievable data rates and the probability of an outage. The results showed an improvement in throughputs for the second and third users, as its value ranged from 7.57 bps/Hz to 12.55 bps/Hz for the second user and a quasi-fixed value of 1,292 bps/Hz for the third user at the transmitted power ranging from zero to 30 dBm.
Non-Orthogonal Multiple Access (NOMA) has been promised for fifth generation (5G) cellular wireless network that can serve multiple users at same radio resources time, frequency, and code domains with different power levels. In this paper, we present a new simulation compression between a random location of multiple users for Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) that depend on Successive Interference Cancellation (SIC) and generalized the suggested joint user pairing for NOMA and beyond cellular networks. Cell throughput and Energy Efficiency (EE) are gained are developed for all active NOMA user in suggested model. Simulation results clarify the cell throughput for NOMA gained 7 Mpbs over OMA system in two different scenarios deployed users (3 and 4). We gain an attains Energy Efficiency (EE) among the weak power users and the stronger power users.
The drastic increase of residential load consumption in recent years result in over loading feeder lines and transformers for the Iraqi northern area distribution system especially in the city of Mosul. Solution for this problem require up to date research consumers load study to find the proper solution to stop excess overload in the transformers and the feeders. This paper include the regional survey for samples of consumers representing typical types of different standard of living and energy consumption by distributing questioners contain list of information such as load type in daily use. Also current readings are recorded for the individual consumer for the months of the year 2006. In addition to those readings, energy consumption is recorded once every two months. The registered readings are used in conjunction with the list of questionnaires to find a sample (for different loads) that coincide with the list of questionnaires for current and energy readings. Resulting in the feasibility of using the sample to know the peak value of current for any consumer even if he is not included in the list of questionnaires and for any new consumer, since it become possible to decide the size of the transformers and feeder lines, to overcome the problem of overloading in any part of the distribution system. The Artificial Neural Network (ANN) is used in this paper to find the above mentioned sample.
Quantum dot solar cells are currently the subject of research in the fields of renewable energy, photovoltaics and optoelectronics, due to their advantages which enables them to overcome the limitations of traditional solar cells. The inability of ordinary solar cells to generate charge carriers, which is prevents them from contributing to generate the current in solar cells. This work focuses on modeling and simulating of Quantum Dot Solar Cells based on InAs/GaAs as well as regular type of GaAs p-i-n solar cells and to study the effect of increasing quantum dots layers at the performance of the solar cell. The low energy of the fell photons considers as one of the most difficult problems that must deal with. According to simulation data, the power conversion efficiency increases from (12.515% to 30.94%), current density rises from 16.4047 mA/cm2 for standard solar cell to 39.4775 mA/cm2) using quantum dot techniques (20-layers) compared to traditional type of GaAs solar cell. Additionally, low energy photons’ absorption range edge expanded from (400 to 900 nm) for quantum technique. The results have been modeled and simulated using (SILVACO Software), which proved the power conversion efficiency of InAs/GaAs quantum dot solar cells is significantly higher than traditional (p-i-n) type about (247%).
This paper presents a method of generating realistic electricity load profile data for the UK domestic buildings. The domestic space features have been investigated excluding the heating and hot water systems. A questionnaire survey was conducted and the feedback were collected from a number of occupants at different intervals of times on daily bases in order to establish the probabilistic record of the estimated use of electrical appliances. The model concept of this study also considers the results of previous investigations such as that available in public reports and statistics as input data elements to predict the global domestic energy consumption. In addition, the daily load profile from individual dwelling to community can be predicted using this method. The result of the present method was compared to available published data and has shown reasonable agreement.
Vehicular network security had spanned and covered a wide range of security related issues. However solar energy harvesting Road Side Unit (RSU) security was not defined clearly, it is this aspect that is considered in this paper. In this work, we will suggest an RSU security model to protect it against different internal and external threats. The main goal is to protect RSU specific data (needed for its operation) as well as its functionality and accessibility. The suggested RSU security model must responds to many objectives, it should ensure that the administrative information exchanged is correct and undiscoverable (information authenticity and privacy), the source (e.g., VANET server) is who he claims to be (message integrity and source authentication) and the system is robust and available (using Intrusion Detection System (IDS)). In this paper, we suggest many techniques to strength RSU security and they were prototyped using an experimental model based on Ubicom IP2022 network processor development kit .
Gait as a biometric can be used to identify subjects at a distance and thus it receives great attention from the research community for security and surveillance applications. One of the challenges that affects gait recognition performance is view variation. Much work has been done to tackle this challenge. However, the majority of the work assumes that gait silhouettes are captured by affine cameras where only the height of silhouettes changes and the difference in viewing angle of silhouettes in one gait cycle is relatively small. In this paper, we analyze the variation in gait recognition performance when using silhouettes from projective cameras and from affine cameras with different distance from the center of a walking path. This is done by using 3D models of walking people in the gallery set and 2D gait silhouettes from independent (single) cameras in the probe set. Different factors that affect matching 3D human models with 2D gait silhouettes from single cameras for view-independent gait recognition are analyzed. In all experiments, we use 258 multi-view sequences belong to 46 subjects from Multi-View Soton gait dataset. We evaluate the matching performance for 12 different views using Gait Energy Image (GEI) as gait features. Then, we analyze the effect of using different camera configurations for 3D model reconstruction, the GEI from cameras with different settings, the upper and lower body parts for recognition and different GEI resolutions. The results illustrate that low recognition performance is achieved when using gait silhouettes from affine cameras while lower recognition performance is obtained when using gait silhouettes from projective cameras.
Energy exchange between AC grid and DC supply that is a part of a hybrid electric micro-grid takes place using various power converter designs. The single-phase, single-stage, AC-DC power dual active bridge converter is one option. The phase-shift modulation is used to regulate energy flow in both directions. The topology of one stage AC-DC dual active bridge converter based in bidirectional switching modules has been introduced. This paper next introduces the analysis of the AC side current considering basic modulation functions and suggests an optimum phase-shifted modulation strategy. The proposed modulation function provides minimum harmonics distortion. A simulation study is presented to compare the proposed strategy to the basic sinusoidal and triangular modulation techniques. The results show that the modified modulation reduces the average THD by about 55% and 39% compared to the standard sinusoidal and triangular modulation strategies respectively and ensures linear relationship between the transferred power and magnitude control coefficient.
In different modern and future wireless communication networks, a large number of low-power user equipment (UE) devices like Internet of Things, sensor terminals, and smart modules have to be supported over constrained power and bandwidth resources. Therefore, wireless-powered communication (WPC) is considered a promising technology for varied applications in which the energy harvesting (EH) from radio frequency radiations is exploited for data transmission. This requires efficient resource allocation schemes to optimize the performance of WPC and prolong the network lifetime. In this paper, harvest-then-transmit-based WP non-orthogonal multiple access (WP-NOMA) system is designed with time-split (TS) and power control (PC) allocation strategies. To evaluate the network performance, the sum rate and UEs’ rates expressions are derived considering power-domain NOMA with successive interference cancellation detection. For comparison purposes, the rate performance of the conventional WP orthogonal multiple access (WP-OMA) is derived also considering orthogonal frequency-division multiple access and time-division multiple access schemes. Intensive investigations are conducted to obtain the best TS and PC resource parameters that enable maximum EH for higher data transmission rates compared with the reference WP-OMA techniques. The achieved outcomes demonstrate the effectiveness of designed resource allocation approaches in terms of the realized sum rate, UE’s rate, rate region, and fairness without distressing the restricted power of far UEs.
Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as envi- ronmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Coop- eration Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.
In this paper, the benefits of using Magnetic Pulse machine which is belong to Non-conventional machine instead of conventional machine. Magnetic Pulse Technology is used for joining dissimilar metals, and for forming and cutting metals. It is a non contact technique. Magnetic field is used to generate impact magnetic pressure for welding and forming the work piece by converted the electrical energy to mechanical energy. It is enable us to design previously not possible by welding dissimilar materials and allowing to welds light and stronger materials together. It can be used to weld metallic with non metallic materials to created mechanical lock on ceramics, polymers, rubbers and composites. It is green process; there is no heat, no radiation, no gas, no smoke and sparks, therefore the emissions are negligible.
Microgrids (ℳ-grids) can be thought of as a small-scale electrical network comprised of a mix of Distributed Generation (DG) resources, storage devices, and a variety of load species. It provides communities with a stable, secure, and renewable energy supply in either off-grid (grid-forming) or on-grid (grid-following) mode. In this work, a control strategy of coordinated power management for a Low Voltage (LV) ℳ-grid with integration of solar Photovoltaic (PV), Battery Energy Storage System (BESS) and three phase loads operated autonomously or connected to the utility grid has been created and analyzed in the Matlab Simulink environment. The main goal expressed here is to achieve the following points: (i) grid following, grid forming modes, and resynchronization mode between them, (ii) Maximum Power Point Tracking (MPPT) from solar PV using fuzzy logic technique, and active power regulator based boost converter using a Proportional Integral (PI) controller is activated when a curtailment operation is required, (iii) ℳ-grid imbalance compensation (negative sequence) due to large single-phase load is activated, and (iv) detection and diagnosis the fault types using Discrete Wavelet Transform (DWT). Under the influence of irradiance fluctuation on solar plant, the proposed control technique demonstrates how the adopted system works in grid- following mode (PQ control), grid- formation, and grid resynchronization to seamlessly connect the ℳ-grid with the main distribution system. In this system, a power curtailment management system is introduced in the event of a significant reduction in load, allowing the control strategy to be switched from MPPT to PQ control, permitting the BESS to absorb excess power. Also, in grid-following mode, the BESS's imbalance compensation mechanism helps to reduce the negative sequence voltage that occurs at the Point of Common Coupling (PCC) bus as a result of an imbalance in the grid's power supply. In addition to the features described above, this system made use of DWT to detect and diagnose various fault conditions.
This paper presents a new optimization algorithm called corrosion diffusion optimization algorithm (CDOA). The proposed algorithm is based on the diffusion behavior of the pitting corrosion on the metal surface. CDOA utilizes the oxidation and reduction electrochemical reductions as well as the mathematical model of Gibbs free energy in its searching for the optimal solution of a certain problem. Unlike other algorithms, CDOA has the advantage of dispensing any parameter that need to be set for improving the convergence toward the optimal solution. The superiority of the proposed algorithm over the others is highlighted by applying them on some unimodal and multimodal benchmark functions. The results show that CDOA has better performance than the other algorithms in solving the unimodal equations regardless the dimension of the variable. On the other hand, CDOA provides the best multimodal optimization solution for dimensions less than or equal to (5, 10, 15, up to 20) but it fails in solving this type of equations for variable dimensions larger than 20. Moreover, the algorithm is also applied on two engineering application problems, namely the PID controller and the cantilever beam to accentuate its high performance in solving the engineering problems. The proposed algorithm results in minimized values for the settling time, rise time, and overshoot for the PID controller. Where the rise time, settling time, and maximum overshoot are reduced in the second order system to 0.0099, 0.0175 and 0.005 sec., in the fourth order system to 0.0129, 0.0129 and 0 sec, in the fifth order system to 0.2339, 0.7756 and 0, in the fourth system which contains time delays to 1.5683, 2.7102 and 1.80 E-4 sec., and in the simple mass-damper system to 0.403, 0.628 and 0 sec., respectively. In addition, it provides the best fitness function for the cantilever beam problem compared with some other well-known algorithms.
In coordination of a group of mobile robots in a real environment, the formation is an important task. Multi- mobile robot formations in global knowledge environments are achieved using small robots with small hardware capabilities. To perform formation, localization, orientation, path planning and obstacle and collision avoidance should be accomplished. Finally, several static and dynamic strategies for polygon shape formation are implemented. For these formations minimizing the energy spent by the robots or the time for achieving the task, have been investigated. These strategies have better efficiency in completing the formation, since they use the cluster matching algorithm instead of the triangulation algorithm.
For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed- distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co-occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM- based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, Homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods.
The main purpose of using the suspension system in vehicles is to prevent the road disturbance from being transmitted to the passengers. Therefore, a precise controller should be designed to improve the performances of suspension system. This paper presents a modeling and control of the nonlinear full vehicle active suspension system with passenger seat utilizing Fuzzy Model Reference Learning Control (FMRLC) technique. The components of the suspension system are: damper, spring and actuator, all of those components have nonlinear behavior, so that, nonlinear forces that are generated by those components should be taken into account when designed the control system. The designed controller consumes high power so that when the control system is used, the vehicle will consume high amount of fuel. It notes that, when vehicle is driven on a rough road; there will be a shock between the sprung mass and the unsprung mass. This mechanical power dissipates and converts into heat power by a damper. In this paper, the wasted power has reclaimed in a proper way by using electromagnetic actuator. The electromagnetic actuator converts the mechanical power into electrical power which can be used to drive the control system. Therefore, overall power consumption demand for the vehicle can be reduced. When the electromagnetic actuator is used three main advantages can be obtained: firstly, fuel consumption by the vehicle is decreased, secondly, the harmful emission is decreases, therefore, our environment is protected, and thirdly, the performance of the suspension system is improved as shown in the obtained results.
Soft commutation techniques have been of great interest during the last few years in power supply switching applications. The recently developed Zero-Voltage transition (ZVT) and Zero-Current transition (ZCT) pulse width modulation (PWM) technique incorporated soft-switching function into PWM converters, so that the switching losses can be reduced with minimum voltage/current stresses and circulating energy. The ZCT technique can significantly reduce the switch turn-off loss which is usually the dominant switching loss in high-power applications. In this paper the steady state analysis and design of the ZCT PWM boost converter are introduced. Control and drive circuit have been designed to drive a 100 Watt ZCT PWM boost converter to experimentally investigate its features and characteristics.
In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.
Multi-level inverters, as a result of the significant contributions they have made to the fields of high voltage and renewable energy applications, MLI has earned a prestigious place in the field of industrial electronics applications. The use of MLI makes it possible to generate an alternating voltage from a DC voltage or from voltages that are continuously applied thanks to this capability. The quality of the produced wave depends on minimizing the level of total harmonic distortion (THD) in the ensuing output voltage. Increasing the total number of levels is required in order to bring down the THD. The bigger the number of layers, the lower the THD. On the other hand, this necessitates an increase in the number of power switches that are utilized, in addition to an increase in the number of DC sources for certain types. A greater number of levels are achieved in this work with a reduced number of switches, and the DC source necessitates the use of specialized control over the switches as well as the grading of the DC source values. In order to demonstrate that the suggested converter achieves the needed outcomes, the MATLAB simulator is utilized.
Selection of the best type and most suitable size of conductors is essential for designing and optimizing the distribution network. In this paper, an effective method has been proposed for proper selection and incorporation of conductors in the feed part of a radial electricity distribution network considering the depreciation effect of conductors. Increasing the usability of the electric energy of the power grid for the subscribers has been considered per load increment regarding the development of the country. Optimal selection and reconstruction of conductors in the power distribution radio network have been performed through a smart method for minimizing the costs related to annual losses and investment for renovation of lines by imperialist competitive algorithm (ICA) to improve the productivity of the power distribution network. Backward/forward sweep load flow method has been used to solve the load flow problem in the power distribution networks. The mentioned optimization method has been tested on DAZ feeder in Ghaleganj town as test.
The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second- order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.
This paper principally advises a simple and reliable control for Static Synchronous Compensator (STATCOM) in a stand-alone wind driven self-excited induction generator power system. The control was proposed based on instantaneous P-Q theory. The advised control enjoys the merits of robustness, reliability and simplicity. The paper also proposes a dimensioning procedure for the STATCOM that involves advising an annotative analytical expression for sizing the DC-link capacitor. This procedure has the advantages of applicability for different reactive power compensators that depend on a separate DC-link in its operation. Comprehensive simulation results in Matlab environment were illustrated for corroborating the performance of the advised control under rigorous operating scenarios. The results show the feasibility, reliability and practicability of the proposed controller.
Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA). Key words: Distributed generation, distribution network planning, multi-objective optimization, and Imperialist competitive algorithm.
Five-phase machine employment in electric drive system is expanding rapidly in many applications due to several advantages that they present when compared with their three-phase complements. Synchronous reluctance machines(SynRM) are considered as a proposed alternative to permanent magnet machine in the automotive industry because the volatilities in the permanent magnet price, and a proposed alternative for induction motor because they have no field excitation windings in the rotor, SyRM rely on high reluctance torque thus no needing for magnetic material in the structure of rotor. This paper presents dynamic simulation of five phase synchronous reluctance motor fed by five phase voltage source inverter based on mathematical modeling. Sinusoidal pulse width modulation (SPWM) technique is used to generate the pulses for inverter. The theory of reference frame has been used to transform five-phase SynRM voltage equations for simplicity and in order to eliminate the angular dependency of the inductances. The torque in terms of phase currents is then attained using the known magnetic co-energy method, then the results obtained are typical.
in recent years popularity of smart Home has been increasing due to low price and simplicity through tablet and Smartphone connectivity. It is an automation of house or home activity. Raspberry Pi3 is a small computer with digital input output capability and it was introduced in 2016; input/output ability besides the availability of all computer features make this system very suitable to be central unit can for smart home. Smart Home may contain centralize controller which control heating, lightning, ventilation in the home, HAVC( Heating, Ventilation and air conditioning),Safety locks of gates, doors and other system to provide improve comfort, better energy efficiency and security. The aim of this Paper is to develop a smart home application using RPi3, wemose-d1 and GSM. Programming has been developed in C++ in wemose-d1 and Python environment for RPi3 operation. The MQTT (Message Queuing Telemetry Transport protocol) technologic used to connect between raspberry pi3 and nodes.
In the present-day decade, the world has regarded an expansion in the use of non-linear loads. These a lot draw harmonic non- sinusoidal currents and voltages in the connection factor with the utility and distribute them with the useful resource of the overall performance of it. The propagation of these currents and voltages into the grids have an effect on the electricity constructions in addition to the one of various client equipment. As a result, the electrical strength notable has come to be critical trouble for each client and distributor of electrical power. Active electrical electricity filters have been proposed as environment splendid gear for electrical power pinnacle notch enchantment and reactive electrical strength compensation. Active Power Filters (APFs) have Flipped out to be a possible wish in mitigating the harmonics and reactive electrical electricity compensation in single-phase and three-phase AC electrical energy networks with Non-Linear Loads (NLLs). Conventionally, this paper applied Ant Colony Algorithm (ACO) for tuning PI and reduce Total Harmonic Distortion (THD). The result show reduces THD at 2.33%.
In this paper we present the details of methodology pursued in implementation of an HMI and Demo Temperature Monitoring application for wireless sensor-based distributed control systems. The application of WSN for a temperature monitoring and control is composed of a number of sensor nodes (motes) with a networking capability that can be deployed for monitoring and control purposes. The temperature is measured in the real time by the sensor boards that sample and send the data to the monitoring computer through a base station or gateway. This paper proposes how such monitoring system can be setup emphasizing on the aspects of low cost, energy-efficient, easy ad-hoc installation and easy handling and maintenance. This paper focuses on the overall potential of wireless sensor nodes and networking in industrial applications. A specific case study is given for the measurement of temperature (with thermistor or thermocouple), humidity, light and the health of the WSN. The focus was not on these four types of measurements and analysis but rather on the design of a communication protocol and building of an HMI software for monitoring. So, a set of system design requirements are developed that covered the use of the wireless platforms, the design of sensor network, the capabilities for remote data access and management, the connection between the WSN and an HMI software designed with MATLAB.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.
Development of distribution systems result in higher system losses and poor voltage regulation. Consequently, an efficient and effective distribution system has become more urgent and important. Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line. This paper examines the use of different evolutionary algorithms, genetic algorithm (GA), to optimal branch conductor selection in planning radial distribution systems with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors and reliability in order to improve productivity. Furthermore, The Backward-Forward sweep iterative method was adopted to solve the radial load flow analysis. Simulations are carried out on 69-bus radial distribution network using GA approach in order to show the accuracy as well as the efficiency of the proposed solution technique.
Upkeeping the Battery State-Of-Charge (SoC) and its life are of great significance in Battery Electric Vehicle (BEV) & Hybrid Electric Vehicles (HEV). This is possible by integrating Solar Photovoltaic Panels (PPs) on the Roof-top of the BEVs & HEVs. However, unlike Solar Powered Vehicle Charging stations and other PV applications where the solar panels are installed in such a way to extract the maximum Photon energy incident on the panel, vehicle Roof-top mount Solar PPs face many challenges in extracting maximum Power due to partial shading issues especially under dynamic conditions when passing under trees, high rise buildings and cloud passages. This paper proposes a new strategy called “Super-capacitor Assisted Photovoltaic Array”. In which Photovoltaic Modules are integrated with Super-capacitors to improve the transient performance of the Photovoltaic Array system. The design of proposed Super-capacitor Assisted PV array is validated & its performance is compared with conventional PV array in Matlab/ Simulink environment.
This paper focuses on designing distributed wireless sensor network gateways armed with Intrusion Detection System (IDS). The main contribution of this work is the attempt to insert IDS functionality into the gateway node (UBICOM IP2022 network processor chip) itself. This was achieved by building a light weight signature based IDS based on the famous open source SNORT IDS. Regarding gateway nodes, as they have limited processing and energy constrains, the addition of further tasks (the IDS program) may affects seriously on its performance, so that, the current design takes these constrains into consideration as a priority and use a special protocol to achieve this goal. In order to optimize the performance of the gateway nodes, some of the preprocessing tasks were offloaded from the gateway nodes to a suggested classification and processing server and a new searching algorithm was suggested. Different measures were taken to validate the design procedure and a detailed simulation model was built to discover the behavior of the system in different environments.
The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.
Nowadays, the Wireless Sensor Network (WSN) has materialized its working areas, including environmental engineering, agriculture sector, industrial, business applications, military, intelligent buildings, etc. Sensor networks emerge as an attractive technology with great promise for the future. Indeed, issues remain to be resolved in the areas of coverage and deployment, scalability, service quality, size, energy consumption and security. The purpose of this paper is to present the integration of WSNs for IoT networks with the intention of exchanging information, applying security and configuration. These aspects are the challenges of network construction in which authentication, confidentiality, availability, integrity, network development. This review sheds some light on the potential integration challenges imposed by the integration of WSNs for IoT, which are reflected in the difference in traffic features.
Wavelet-based algorithms are increasingly used in the source coding of remote sensing, satellite and other geospatial imagery. At the same time, wavelet-based coding applications are also increased in robust communication and network transmission of images. Although wireless multimedia sensors are widely used to deliver multimedia content due to the availability of inexpensive CMOS cameras, their computational and memory resources are still typically very limited. It is known that allowing a low-cost camera sensor node with limited RAM size to perform a multi-level wavelet transform, will in return limit the size of the acquired image. Recently, fractional wavelet filter technique became an interesting solution to reduce communication energy and wireless bandwidth, for resource-constrained devices (e.g. digital cameras). The reduction in the required memory in these fractional wavelet transforms is achieved at the expense of the image quality. In this paper, an adaptive fractional artifacts reduction approach is proposed for efficient filtering operations according to the desired compromise between the effectiveness of artifact reduction and algorithm simplicity using some local image features to reduce boundaries artifacts caused by fractional wavelet. Applying such technique on different types of images with different sizes using CDF 9/7 wavelet filters results in a good performance.
Traditional friction brakes can generate problems such as high braking temperature and pressure, cracking, and wear, leading to braking failure and user damage. Eddy current brake systems (contactless magnetic brakes) are one method used in motion applications. They are wear-free, less temperature-sensitive, quick, easy, and less susceptible to wheel lock, resulting in less brake failure due to the absence of physical contact between the magnet and disc. Important factors that can affect the performance of the braking system are the type of materials manufactured for the permanent magnets. This paper examines the performance of the permanent magnetic eddy current braking (PMECB) system. Different kinds of permanent magnets are proposed in this system to create eddy currents, which provide braking for the braking system is simulated using FEA software to demonstrate the efficiency of braking in terms of force production, energy dissipation, and overall performance findings demonstrated that permanent magnets consisting of neodymium, iron, and boron consistently provided the maximum braking effectiveness. The lowest efficiency is found in ferrite, which has the second-lowest efficiency behind samarium cobalt. This is because ferrite has a weaker magnetic field. Because of this, the PMECBS based on NdFeB magnets has higher power dissipation values, particularly at higher speeds.
This work focuses on using two-dimensional finite element method to calculate apparent and incremental inductances for 40W fluorescent lamp ballast with different loading conditions based on its design documents. Seven inductance calculation techniques are adopted by calculating ballast stored magnetic energy and flux linkage using ANSYS software. The calculated results for apparent inductances show a good agreement with the design and average measured values, while calculated incremental inductances have been verified by noting the behavior of core hysteresis loop experimentally. These seven techniques establish a good base for researchers and designers to obtain an accurate inductance profile for any iron-core inductor.
This paper studies the impact of climate change on the electricity consumption by means of a fuzzy regression approach. The climate factors which have been considered in this paper are humidity and temperature, whereas the simultaneous effect of these two climate factors is considered. The impacts of other climate variables, like the wind, with a minor effect on energy consumption are ignored. The innovation which applies in this paper is the division of the year into two parts by using the temperature-day graph in the year. To index the humidity, data of the minimum humidity per day are used. For temperature, the maximum temperature of the first part of the year (warm days) and the minimum of the second part (cold days) are used. The indicator for the consumption is the daily peak load. The model results show high sensitivity to the temperature but low sensitivity to the humidity. Moreover, it is concluded that the model structure cannot be the same and for the cold par additional variables such as gas consumption should be considered.
Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN’s prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.
The dynamic performance of vertical-cavity surface emitting lasers (VCSEL) diodes can be enhanced by incorporating multiquantum-well (MQW) structure in the active region. This paper addresses the transient response of MQW-VCSEL by solving the laser rate equation in the large-signal regime. The analysis makes use of the energy band structure and optical gain spectrum obtained by applying Schrödinger equation to both conduction and valance bands. Simulation results are presented for $1.3~\mu m$ InGaAs/InP VCSEL and indicate clearly that a MQW laser has higher switching speed compared with bulk laser and this finding is more pronounced with small number of wells.
Energy limitations have become fundamental challenge for designing WSNs. Network lifetime is the most interested and important metric in WSNs. Many works have been developed for prolonging networks lifetime, in which one of the important work is the control of transmission power. This paper proposes a new fuzzy transmission power control technique that operate together with routing protocols for prolonging WSNs lifetime. Dijkstra shortest path routing is considered as the main routing protocol in this work. This paper mainly focuses on transmission power control scheme for prolonging WSNs lifetime. A performance comparison is depicted for maximum and controlled transmission power. Simulation results show an increase in network lifetime equals to 3.4776 for the proposed fuzzy control. The performance of the proposed fuzzy control technique involves a good improvement and contribution in the field of prolonging networks lifetime by using transmission power control.