The buckling behavior of a typical structure consisting of a micro constantan wire and a polymer membrane under coupled electrical-mechanical loading was studied. The phenomenon that the constantan wire delaminates fr...The buckling behavior of a typical structure consisting of a micro constantan wire and a polymer membrane under coupled electrical-mechanical loading was studied. The phenomenon that the constantan wire delaminates from the polymer membrane was observed after unloading. The interfacial toughness of the constantan wire and the polymer membrane was estimated. Moreover, several new instability modes of the constantan wire could be further triggered based on the buckle-driven delamination. After electrical loading and tensile loading, the constantan wire was likely to fracture based on buckling. After electrical loading and compressive loading, the constantan wire was easily folded at the top of the buckling region. On the occasion, the constantan wire buckled towards the inside of the polymer membrane under electrical-compressive loading. The mechanisms of these instability modes were analyzed.展开更多
Electric load simulator(ELS) systems are employed for electric power steering(EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering...Electric load simulator(ELS) systems are employed for electric power steering(EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator’s acceleration.展开更多
The effect of electrostatic tractions on the fracture behavior of a dielectric material under mechanical and/or electric loading is analyzed,by studying a pre-cracked parallel-plate capacitor,and illustrated by plots....The effect of electrostatic tractions on the fracture behavior of a dielectric material under mechanical and/or electric loading is analyzed,by studying a pre-cracked parallel-plate capacitor,and illustrated by plots.The results indicate that electrostatic tractions on the electrodes compress the material in front of the crack tip,while electrostatic tractions on the crack faces have the tendency to close the crack and stretch the material behind the crack tip.Mechanical load is the driving force to propagate the crack,while applied electric field retards crack propagation due to the electrostatic tractions.As a direct consequence,the fracture criterion is composed of two parts:the energy release rate must exceed a critical value and the mechanical load must be higher than the critical value for crack opening.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical ...In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical model is proposed,resulting in coupled governing integral equations that account for interfacial normal and shear stresses.To numerically solve these integral equations,an expansion method using orthogonal Chebyshev polynomials is employed.The results provide insights into the interfacial stresses,axial force,as well as axial and vertical deformations of the PQC film.Additionally,fracture criteria,including stress intensity factors,mode angles,and the J-integral,are evaluated.The solution is compared with the membrane theory,neglecting the normal stress and bending deformation.Finally,the effects of stiffness and aspect ratio on the PQC film are thoroughly discussed.This study serves as a valuable guide for controlling the mechanical response and conducting safety assessments of PQC film systems.展开更多
As a matured technique used in many fields,the distributed computer system is still a new management method for the aeronautical electrical power distribution system in our country. In this paper, a novel aircraft ele...As a matured technique used in many fields,the distributed computer system is still a new management method for the aeronautical electrical power distribution system in our country. In this paper, a novel aircraft electrical power distribution system based on the distributed computer system is proposed. The principles, features and structure of the aircraft electrical power distribution system and the distributed computer system named electrical load management system (ELMS) are studied. The ELMS composed of four electrical load management centers (ELMCs) and two power source processors (PSPs) operates in the 1553B buses. Principles of the ELMCs and the PSPs are introduced. With the application of the distributed computer system, the aircraft electrical power distribution system is simple, adaptable and flexible.展开更多
Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand resp...Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.展开更多
Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep le...Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.展开更多
The electrical conductivity, compression sensibility, workability and cost are factors that affect the application of conductive smart materials in civil structures. Consequently, the resistance and compression sensib...The electrical conductivity, compression sensibility, workability and cost are factors that affect the application of conductive smart materials in civil structures. Consequently, the resistance and compression sensibility of magnetic-concentrated fly ash （MCFA） mortar were investigated using two electrode method, and the difference of compression sensibility between MCFA mortar and carbon fiber reinforced cement （CFRC） under uniaxial loading was studied. Factors affecting the compression sensibility of MCFA mortar, such as MCFA content, loading rate and stress cycles, were analyzed. Results show that fly ash with high content of Fe3O4 can be used to prepare conductive mortar since Fe3O4 is a kind of nonstoichiometric oxide and usually acts as semiconductor. MCFA mortar exhibits the same electrical conductivity to that of CFRC when the content of MCFA is more than 40% by weight of sample. The compression sensibility of mortar is improved with the increase of MCFA content and loading rate. The compression sensibility of MCFA mortar is reversible with the circling of loading. Results show that the application of MCFA in concrete not only provides excellent performances of electrical-functionality and workability, but also reduces the cost of conductive concrete.展开更多
Various forecasting tools exist for planners of national networks that are based on historical data. These are used to make decisions at the national level to meet a countries commitment to CO2 emission targets. Howev...Various forecasting tools exist for planners of national networks that are based on historical data. These are used to make decisions at the national level to meet a countries commitment to CO2 emission targets. However, at a local community level, the guidance is not easily understood by planners. This work presents for the first time a methodology for the generation of realistic domestic electricity load profiles for different types of UK households for small communities. The work is based on a limited set of data, and has been compared with measurement. Daily load profiles from individual dwelling to community can be predicted using this method. Results have been presented, and discussed.展开更多
Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consump...Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.展开更多
Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban ...Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.展开更多
Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundan...Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.展开更多
[ Objective] The research aimed to study relationship between summer electricity and meteorological factors. [ Method] Electrical load characteristics in Changsha during 2007 -2010 were analyzed. Correlation analysis ...[ Objective] The research aimed to study relationship between summer electricity and meteorological factors. [ Method] Electrical load characteristics in Changsha during 2007 -2010 were analyzed. Correlation analysis between electrical load and meteorological factors （daily average temperature, the maximum temperature, the minimum temperature, rainfall, wind, relative humidity and atmospheric pressure） during July - September of 2007 -2010 was conducted. [ Result] Changes of the meteorological factors could directly affect electrical load, and temperature was the first influence factor. Prediction model of summer electrical load in Changsha was established by regression analysis method.[ Conclusion] The research could provide reference basis for prediction of the electrical load in Changsha.展开更多
Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the perfor...Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.展开更多
The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and use...The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.展开更多
In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access ...In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.展开更多
The velocity dispersion and attenuation of shear horizontal（SH） waves in a layered piezoelectric structure loaded with viscous liquid is studied,where the（1- x）Pb（Mg（1/3）Nb（2/3））O（3-x）PbTiO3[PMN-xPT]single...The velocity dispersion and attenuation of shear horizontal（SH） waves in a layered piezoelectric structure loaded with viscous liquid is studied,where the（1- x）Pb（Mg（1/3）Nb（2/3））O（3-x）PbTiO3[PMN-xPT]single crystal is chosen as the piezoelectric layer.The PMN-xPT is being polarized along[011]c and[001]c so that the macroscopic symmetries are mm 2 and 4 mm,respectively.For the nonconductive liquid,the electrically open and shorted conditions at the interface between the liquid and the piezoelectric layer are considered.The phase velocity equations are derived analytically.The effects of the electrically boundary condition,the viscous coefficient and mass density of liquid as well as the thickness of the PMN-xPT layer on the phase velocity and attenuation are graphically illustrated.The results show that the phase velocity for the[011]c polarized PMN-0.29 PT is much smaller than that for the[001]c polarized PMN-0.33 PT,and the effects of viscous coefficient and piezoelectric layer thickness on the phase velocity for the[011]c case are stronger than that for the[001]c case.In addition,the electrical boundary conditions have an obvious influence on the propagation behaviors.These results can be useful for the designs and applications of acoustic wave devices and liquid biosensors.展开更多
基金Projects(2010CB631005,2011CB606105)support by the National Basic Research Program of ChinaProjects(11232008,91216301,11227801,11172151)supported by the National Natural Science Foundation of ChinaProject supported by Tsinghua University Initiative Scientific Research Program
文摘The buckling behavior of a typical structure consisting of a micro constantan wire and a polymer membrane under coupled electrical-mechanical loading was studied. The phenomenon that the constantan wire delaminates from the polymer membrane was observed after unloading. The interfacial toughness of the constantan wire and the polymer membrane was estimated. Moreover, several new instability modes of the constantan wire could be further triggered based on the buckle-driven delamination. After electrical loading and tensile loading, the constantan wire was likely to fracture based on buckling. After electrical loading and compressive loading, the constantan wire was easily folded at the top of the buckling region. On the occasion, the constantan wire buckled towards the inside of the polymer membrane under electrical-compressive loading. The mechanisms of these instability modes were analyzed.
基金Supported by National Natural Science Foundation of China (Grant No. 51505178)China Postdoctoral Science Foundation (Grant No. 2014M561289)。
文摘Electric load simulator(ELS) systems are employed for electric power steering(EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator’s acceleration.
基金supported by the Hong Kong Research Grants Council(Grant No. 622610)supported by the Nanotechnology Program at HKUST
文摘The effect of electrostatic tractions on the fracture behavior of a dielectric material under mechanical and/or electric loading is analyzed,by studying a pre-cracked parallel-plate capacitor,and illustrated by plots.The results indicate that electrostatic tractions on the electrodes compress the material in front of the crack tip,while electrostatic tractions on the crack faces have the tendency to close the crack and stretch the material behind the crack tip.Mechanical load is the driving force to propagate the crack,while applied electric field retards crack propagation due to the electrostatic tractions.As a direct consequence,the fracture criterion is composed of two parts:the energy release rate must exceed a critical value and the mechanical load must be higher than the critical value for crack opening.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
基金Supported by the National Natural Science Foundation of China (Nos. 11902293 and 12272353)。
文摘In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical model is proposed,resulting in coupled governing integral equations that account for interfacial normal and shear stresses.To numerically solve these integral equations,an expansion method using orthogonal Chebyshev polynomials is employed.The results provide insights into the interfacial stresses,axial force,as well as axial and vertical deformations of the PQC film.Additionally,fracture criteria,including stress intensity factors,mode angles,and the J-integral,are evaluated.The solution is compared with the membrane theory,neglecting the normal stress and bending deformation.Finally,the effects of stiffness and aspect ratio on the PQC film are thoroughly discussed.This study serves as a valuable guide for controlling the mechanical response and conducting safety assessments of PQC film systems.
文摘As a matured technique used in many fields,the distributed computer system is still a new management method for the aeronautical electrical power distribution system in our country. In this paper, a novel aircraft electrical power distribution system based on the distributed computer system is proposed. The principles, features and structure of the aircraft electrical power distribution system and the distributed computer system named electrical load management system (ELMS) are studied. The ELMS composed of four electrical load management centers (ELMCs) and two power source processors (PSPs) operates in the 1553B buses. Principles of the ELMCs and the PSPs are introduced. With the application of the distributed computer system, the aircraft electrical power distribution system is simple, adaptable and flexible.
基金supported by China Three Gorges Corporation(Key technology research and demonstration application of large-scale source-net-load-storage integration under the vision of carbon neutrality)Fundamental Research Funds for the Central Universities(2020MS021).
文摘Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.
基金Innosuisse-Schweizerische Agentur für Innovationsförderung,Grant/Award Number:1155002544。
文摘Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.
基金the National Natural Science Foundation of China(No.51002193)
文摘The electrical conductivity, compression sensibility, workability and cost are factors that affect the application of conductive smart materials in civil structures. Consequently, the resistance and compression sensibility of magnetic-concentrated fly ash （MCFA） mortar were investigated using two electrode method, and the difference of compression sensibility between MCFA mortar and carbon fiber reinforced cement （CFRC） under uniaxial loading was studied. Factors affecting the compression sensibility of MCFA mortar, such as MCFA content, loading rate and stress cycles, were analyzed. Results show that fly ash with high content of Fe3O4 can be used to prepare conductive mortar since Fe3O4 is a kind of nonstoichiometric oxide and usually acts as semiconductor. MCFA mortar exhibits the same electrical conductivity to that of CFRC when the content of MCFA is more than 40% by weight of sample. The compression sensibility of mortar is improved with the increase of MCFA content and loading rate. The compression sensibility of MCFA mortar is reversible with the circling of loading. Results show that the application of MCFA in concrete not only provides excellent performances of electrical-functionality and workability, but also reduces the cost of conductive concrete.
文摘Various forecasting tools exist for planners of national networks that are based on historical data. These are used to make decisions at the national level to meet a countries commitment to CO2 emission targets. However, at a local community level, the guidance is not easily understood by planners. This work presents for the first time a methodology for the generation of realistic domestic electricity load profiles for different types of UK households for small communities. The work is based on a limited set of data, and has been compared with measurement. Daily load profiles from individual dwelling to community can be predicted using this method. Results have been presented, and discussed.
文摘Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.
基金Project(NRF-20110030631) supported by the National Research Foundation of Korea Grant funded by the Korean Government
文摘Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.
文摘Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.
文摘[ Objective] The research aimed to study relationship between summer electricity and meteorological factors. [ Method] Electrical load characteristics in Changsha during 2007 -2010 were analyzed. Correlation analysis between electrical load and meteorological factors （daily average temperature, the maximum temperature, the minimum temperature, rainfall, wind, relative humidity and atmospheric pressure） during July - September of 2007 -2010 was conducted. [ Result] Changes of the meteorological factors could directly affect electrical load, and temperature was the first influence factor. Prediction model of summer electrical load in Changsha was established by regression analysis method.[ Conclusion] The research could provide reference basis for prediction of the electrical load in Changsha.
基金Supported by the Major Program of National Natural Science Foundation of China(No.61432006)。
文摘Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.
文摘The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.
文摘In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.
基金supported by the National Natural Science Foundation of China(No.11272221)the National Key Basic Research Program of China(No.2012CB723300)the support from the Natural Science Foundation of Hebei Province of China(No.A2013210106)
文摘The velocity dispersion and attenuation of shear horizontal（SH） waves in a layered piezoelectric structure loaded with viscous liquid is studied,where the（1- x）Pb（Mg（1/3）Nb（2/3））O（3-x）PbTiO3[PMN-xPT]single crystal is chosen as the piezoelectric layer.The PMN-xPT is being polarized along[011]c and[001]c so that the macroscopic symmetries are mm 2 and 4 mm,respectively.For the nonconductive liquid,the electrically open and shorted conditions at the interface between the liquid and the piezoelectric layer are considered.The phase velocity equations are derived analytically.The effects of the electrically boundary condition,the viscous coefficient and mass density of liquid as well as the thickness of the PMN-xPT layer on the phase velocity and attenuation are graphically illustrated.The results show that the phase velocity for the[011]c polarized PMN-0.29 PT is much smaller than that for the[001]c polarized PMN-0.33 PT,and the effects of viscous coefficient and piezoelectric layer thickness on the phase velocity for the[011]c case are stronger than that for the[001]c case.In addition,the electrical boundary conditions have an obvious influence on the propagation behaviors.These results can be useful for the designs and applications of acoustic wave devices and liquid biosensors.
