The design and analysis of a fuel cell vehi-cle-to-grid(FCV2G)system with a high voltage conver-sion interface is proposed.The system aims to maximize the utilization of fuel cell vehicles(FCVs)as distributed energy r...The design and analysis of a fuel cell vehi-cle-to-grid(FCV2G)system with a high voltage conver-sion interface is proposed.The system aims to maximize the utilization of fuel cell vehicles(FCVs)as distributed energy resources,allowing them to actively participate in the energy market.The proposed FCV2G system has FCVs,power electronics interfaces,and the electrical grid.The power electronics interfaces are responsible for con-verting the low-voltage output of the fuel cell stack into high-voltage DC power,and ensuring efficient power transfer between the FCVs and the grid.To optimize the operation of the FCV2G system,the momentum search algorithm(MSA)is employed.By applying MSA,the FCV2G system can achieve optimal power dispatch,con-sidering factors such as energy efficiency,grid stability,and economic feasibility.The proposed method is tested in MATLAB.The best MSA and dynamic load profile solu-tions are run for 24 h and the results show that 100%import of FCVs 51.0%more than 100%electric vehicle.Peak-cutting and vehicle-to-grid service revenue are 30.5%and 95.0%greater,respectively.Low discharge loss,high capacity,and high discharge power are the main advantages of FCVs.The benchmark FCVs ratio of 15%is used for sensitivity analysis.The findings reveal that the overall advantages of FCV2G are improved.Index Terms—Continuous conduction mode,DC-DC converter,discontinuous conduction mode,fuel cell vehi-cle,utility-grids,vehicle-to-grid.展开更多
Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(S...Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(SA)is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine(PMSM).The parameters of direct torque controllers(DTC)for the drive are automatically adjusted by the optimization algorithm.Advantages of the PI-Fuzzy-SA algorithm are retained when used together.It also improves the rate of system convergence.Speed response improvement and har-monic reduction is achieved with SA-based DTC for PMSM.This mechanism is known to be faster than other algorithms.Also,it is observed that as compared to other algorithms,the projected algorithm yields a reduced total harmonic distor-tion.As a result of the employment of Space Vector Modulation(SVM)techni-que,the system is resistant to changes in motor specifications and load torque.Through MATLAB&Simulink simulation,the experiment is done and the per-formance is calculated for the controller.展开更多
In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent...In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop展开更多
The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy source...The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy sources.However,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS.In this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of BESS.Moreover,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation model.In addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its performance.The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial building.Moreover,the scenario reduction technique is used to handle the uncertainty associated with generation and load demand.To validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management strategies.The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS.Moreover,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.展开更多
Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resu...Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.展开更多
The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic curre...The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.展开更多
Permanent magnet brushless DC motors are used for various low-power applications,namely domestic fans,washing machines,mixer grinders and cooling fan applications.This paper focuses on selecting the best laminating ma...Permanent magnet brushless DC motors are used for various low-power applications,namely domestic fans,washing machines,mixer grinders and cooling fan applications.This paper focuses on selecting the best laminating material for the interior permanent magnet brushless DC(IPM BLDC)motor used in the cooling fan of automobiles.Various laminating materials,namely M19-29GA,M800-65A and M43,are tested using finite element analysis.The machine's vital performance metrics,namely the stator current,torque ripple,and hysteresis loss were analyzed in selecting the laminating material.The designed motor is also modelled as a mathematical model from the computed lumped parameters.The performance of the machines was validated through electromagnetic and thermal analysis.展开更多
Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substant...Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.展开更多
Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented challenges.NOMA employs powerdomainmultiplexing t...Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented challenges.NOMA employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency misalignments.Unfortunately,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless networks.Therefore,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high PAPR.Additionally,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)gain.In the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned resources.The simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional framework.Hence,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC waveform.The key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting side.Thus,a huge count of mobile devices could be supported under specific restrictions.DHTNOMA-UFMC can be regarded as the most effective applications for 5G Mobile andWireless Networks.However,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.展开更多
The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medicalfield.In that case,the image proces-sing is performed to improve the image data,wherein it inhibits ...The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medicalfield.In that case,the image proces-sing is performed to improve the image data,wherein it inhibits unintended dis-tortion of image features or it enhances further processing in various applications andfields.This helps to show better results especially for diagnosing diseases.Of late the early prediction of cancer is necessary to prevent disease-causing pro-blems.This work is proposed to identify lung cancer using lung computed tomo-graphy(CT)scan images.It helps to identify cancer cells’affected areas.In the present work,the original input image from Lung Image Database Consortium(LIDC)typically suffers from noise problems.To overcome this,the Gaborfilter used for image processing is highly enhanced.In the next stage,the Spherical Iterative Refinement Clustering(SIRC)algorithm identifies cancer-suspected areas on the CT scan image.This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages.These new methods help to remove unwanted por-tions of the CT image and better utilization the image.The subspace extraction of features approach is beneficial for evaluating lung cancer.This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures(CCPNN).By using the feature values from the fourth step of the procedure,the proposed CCPNN tends to categorize the lesion in the lung nodular site.The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall(%),precision(%),F-measure(percent),and accuracy(%).Finally,the incorrect classification ratios are determined to compare the trained networks’effectiveness,through these parameters of CCPNN,it obtains the outstanding per-formance of 98.06%and it has provided the lowest false ratio of 1.8%.展开更多
Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivale...Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivalent system inertia. Thus, it is important that wind turbines also contribute to system frequency control. This paper examines the dynamic contribution of doubly fed induction generator （DFIG）-based wind turbine in system frequency regulation. The modified inertial support scheme is proposed which helps the DFIG to provide the short term transient active power support to the grid during transients and arrests the fall in frequency. The frequency deviation is considered by the controller to provide the inertial control. An additional reference power output is used which helps the DFIG to release kinetic energy stored in rotating masses of the turbine. The optimal speed control parameters have been used for the DFIG to increases its participation in frequency control. The simulations carried out in a two-area interconnected power system demonstrate the contribution of the DFIG in load frequency control.展开更多
The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the system.Despite its advantages,...The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the system.Despite its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy consumption.One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds.To address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog node.The OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog nodes.In our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA algorithm.Specifically,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other algorithms.As a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.展开更多
Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone...Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.展开更多
This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and alu...This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)electrodes.Thisdevice yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good repeatability.This paper physically explained the conduction mechanism in the layered SnO_(2)nanofiber-basedmemristor.The conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron carriers.Energyband structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this paper.An machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the fabricateddevice.We have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated memristor.Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)score.The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory predictions.Lastly,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.展开更多
Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,t...Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.展开更多
Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control...Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.展开更多
Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)c...Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.展开更多
Instrument calibration is vital to a successful control system because signal inputs to the system controllers come from such instruments. This paper presents a method for actualizing a standard low-cost loop calibrat...Instrument calibration is vital to a successful control system because signal inputs to the system controllers come from such instruments. This paper presents a method for actualizing a standard low-cost loop calibrator for the famous 4-20 mA electrical signaling scheme. The loop calibrator generates a linear current signal from 4 to 20 mA over a 250 ? typical process instrument load for calibration. The realization of the loop calibrator relies on a voltage-to-current converter to build a constant current source. The voltage controlled constant current source is built from discrete components and an op-amp to keep the cost low. Results from simulations and the prototype demonstrate the performance of the 4-20 mA loop calibrator which utilizes a greatly reduced number of components. The cost of these components is approximately 34% of the least expensive calibrator sampled, though other production costs are not included. This conclusion reinforces the fact that loop calibrators can be cheaper.展开更多
Combined estimation of state and feed-back gain for optimal load frequency control is proposed.Load frequency control(LFC)addresses the problem of controlling system frequency in response to disturbance,and is one of ...Combined estimation of state and feed-back gain for optimal load frequency control is proposed.Load frequency control(LFC)addresses the problem of controlling system frequency in response to disturbance,and is one of main research areas in power system operation.A well acknowledged solution to this problem is feedback stabilization,where the Linear Quadratic Regulator(LQR)based controller computes the feedback gain K from the known system parameters and implements the control,assuming the availability of all the state variables.However,this approach restricts control to cases where the state variables are readily available and the system parameters are steady.Alternatively,by estimating the states continuously from available measurements of some of the states,it can accommodate dynamic changes in the system parameters.The paper proposes the technique of augmenting the state variables with controller gains.This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter.This results in producing controller gains that are capable of controlling the system in response to changes in load demand,system parameter variation and measurement noise.展开更多
Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV pane...Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.展开更多
文摘The design and analysis of a fuel cell vehi-cle-to-grid(FCV2G)system with a high voltage conver-sion interface is proposed.The system aims to maximize the utilization of fuel cell vehicles(FCVs)as distributed energy resources,allowing them to actively participate in the energy market.The proposed FCV2G system has FCVs,power electronics interfaces,and the electrical grid.The power electronics interfaces are responsible for con-verting the low-voltage output of the fuel cell stack into high-voltage DC power,and ensuring efficient power transfer between the FCVs and the grid.To optimize the operation of the FCV2G system,the momentum search algorithm(MSA)is employed.By applying MSA,the FCV2G system can achieve optimal power dispatch,con-sidering factors such as energy efficiency,grid stability,and economic feasibility.The proposed method is tested in MATLAB.The best MSA and dynamic load profile solu-tions are run for 24 h and the results show that 100%import of FCVs 51.0%more than 100%electric vehicle.Peak-cutting and vehicle-to-grid service revenue are 30.5%and 95.0%greater,respectively.Low discharge loss,high capacity,and high discharge power are the main advantages of FCVs.The benchmark FCVs ratio of 15%is used for sensitivity analysis.The findings reveal that the overall advantages of FCV2G are improved.Index Terms—Continuous conduction mode,DC-DC converter,discontinuous conduction mode,fuel cell vehi-cle,utility-grids,vehicle-to-grid.
文摘Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(SA)is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine(PMSM).The parameters of direct torque controllers(DTC)for the drive are automatically adjusted by the optimization algorithm.Advantages of the PI-Fuzzy-SA algorithm are retained when used together.It also improves the rate of system convergence.Speed response improvement and har-monic reduction is achieved with SA-based DTC for PMSM.This mechanism is known to be faster than other algorithms.Also,it is observed that as compared to other algorithms,the projected algorithm yields a reduced total harmonic distor-tion.As a result of the employment of Space Vector Modulation(SVM)techni-que,the system is resistant to changes in motor specifications and load torque.Through MATLAB&Simulink simulation,the experiment is done and the per-formance is calculated for the controller.
文摘In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop
基金supported in part by the Department of Science and Technology,Government of India,New Delhi,India“Internet of Things(IoT)Research of Interdisciplinary Cyber-Physical Systems Program”(No.DST/ICPS/CLUSTER/IoT/2018/General)。
文摘The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy sources.However,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS.In this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of BESS.Moreover,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation model.In addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its performance.The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial building.Moreover,the scenario reduction technique is used to handle the uncertainty associated with generation and load demand.To validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management strategies.The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS.Moreover,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.
文摘Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.
文摘The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.
文摘Permanent magnet brushless DC motors are used for various low-power applications,namely domestic fans,washing machines,mixer grinders and cooling fan applications.This paper focuses on selecting the best laminating material for the interior permanent magnet brushless DC(IPM BLDC)motor used in the cooling fan of automobiles.Various laminating materials,namely M19-29GA,M800-65A and M43,are tested using finite element analysis.The machine's vital performance metrics,namely the stator current,torque ripple,and hysteresis loss were analyzed in selecting the laminating material.The designed motor is also modelled as a mathematical model from the computed lumped parameters.The performance of the machines was validated through electromagnetic and thermal analysis.
文摘Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.
基金This work was supported by SUT Research and Development Funds and by Thailand Science Research and Innovation(TSRI).Also,this work was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University,Saudi Arabia.In addition,support by the Taif University Researchers Supporting Project number(TURSP-2020/77),Taif University,Taif,Saudi Arabia.
文摘Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented challenges.NOMA employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency misalignments.Unfortunately,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless networks.Therefore,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high PAPR.Additionally,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)gain.In the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned resources.The simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional framework.Hence,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC waveform.The key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting side.Thus,a huge count of mobile devices could be supported under specific restrictions.DHTNOMA-UFMC can be regarded as the most effective applications for 5G Mobile andWireless Networks.However,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.
文摘The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medicalfield.In that case,the image proces-sing is performed to improve the image data,wherein it inhibits unintended dis-tortion of image features or it enhances further processing in various applications andfields.This helps to show better results especially for diagnosing diseases.Of late the early prediction of cancer is necessary to prevent disease-causing pro-blems.This work is proposed to identify lung cancer using lung computed tomo-graphy(CT)scan images.It helps to identify cancer cells’affected areas.In the present work,the original input image from Lung Image Database Consortium(LIDC)typically suffers from noise problems.To overcome this,the Gaborfilter used for image processing is highly enhanced.In the next stage,the Spherical Iterative Refinement Clustering(SIRC)algorithm identifies cancer-suspected areas on the CT scan image.This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages.These new methods help to remove unwanted por-tions of the CT image and better utilization the image.The subspace extraction of features approach is beneficial for evaluating lung cancer.This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures(CCPNN).By using the feature values from the fourth step of the procedure,the proposed CCPNN tends to categorize the lesion in the lung nodular site.The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall(%),precision(%),F-measure(percent),and accuracy(%).Finally,the incorrect classification ratios are determined to compare the trained networks’effectiveness,through these parameters of CCPNN,it obtains the outstanding per-formance of 98.06%and it has provided the lowest false ratio of 1.8%.
文摘Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivalent system inertia. Thus, it is important that wind turbines also contribute to system frequency control. This paper examines the dynamic contribution of doubly fed induction generator （DFIG）-based wind turbine in system frequency regulation. The modified inertial support scheme is proposed which helps the DFIG to provide the short term transient active power support to the grid during transients and arrests the fall in frequency. The frequency deviation is considered by the controller to provide the inertial control. An additional reference power output is used which helps the DFIG to release kinetic energy stored in rotating masses of the turbine. The optimal speed control parameters have been used for the DFIG to increases its participation in frequency control. The simulations carried out in a two-area interconnected power system demonstrate the contribution of the DFIG in load frequency control.
文摘The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the system.Despite its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy consumption.One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds.To address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog node.The OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog nodes.In our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA algorithm.Specifically,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other algorithms.As a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.
文摘This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)electrodes.Thisdevice yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good repeatability.This paper physically explained the conduction mechanism in the layered SnO_(2)nanofiber-basedmemristor.The conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron carriers.Energyband structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this paper.An machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the fabricateddevice.We have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated memristor.Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)score.The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory predictions.Lastly,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.
文摘Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.
文摘Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.
文摘Instrument calibration is vital to a successful control system because signal inputs to the system controllers come from such instruments. This paper presents a method for actualizing a standard low-cost loop calibrator for the famous 4-20 mA electrical signaling scheme. The loop calibrator generates a linear current signal from 4 to 20 mA over a 250 ? typical process instrument load for calibration. The realization of the loop calibrator relies on a voltage-to-current converter to build a constant current source. The voltage controlled constant current source is built from discrete components and an op-amp to keep the cost low. Results from simulations and the prototype demonstrate the performance of the 4-20 mA loop calibrator which utilizes a greatly reduced number of components. The cost of these components is approximately 34% of the least expensive calibrator sampled, though other production costs are not included. This conclusion reinforces the fact that loop calibrators can be cheaper.
文摘Combined estimation of state and feed-back gain for optimal load frequency control is proposed.Load frequency control(LFC)addresses the problem of controlling system frequency in response to disturbance,and is one of main research areas in power system operation.A well acknowledged solution to this problem is feedback stabilization,where the Linear Quadratic Regulator(LQR)based controller computes the feedback gain K from the known system parameters and implements the control,assuming the availability of all the state variables.However,this approach restricts control to cases where the state variables are readily available and the system parameters are steady.Alternatively,by estimating the states continuously from available measurements of some of the states,it can accommodate dynamic changes in the system parameters.The paper proposes the technique of augmenting the state variables with controller gains.This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter.This results in producing controller gains that are capable of controlling the system in response to changes in load demand,system parameter variation and measurement noise.
文摘Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.
