In modern industrial applications,ensuring the reliability of mechanical fittings is critical for maintaining operational safety and efficiency,particularly in power grid systems where split pins serve a pivotal role ...In modern industrial applications,ensuring the reliability of mechanical fittings is critical for maintaining operational safety and efficiency,particularly in power grid systems where split pins serve a pivotal role despite being susceptible to environmental degradation and failure.Existing UAV-based inspection systems are hampered by a low representation of split pin elements and complex backgrounds,leading to challenges in accurate fault detection and timely maintenance.To address this pressing issue,our study proposes an innovative fault detection method for split pins.The approach employs a three-step process:first,cropping operations are used to accurately isolate the fittings containing split pins;second,super-resolution reconstruction is applied to enhance image clarity and detail;and finally,an improved YOLOv8 network,augmented with inner-shape IoU and local window attention mechanisms,is utilized to refine local feature extraction and annotation accuracy.Experimental evaluations on a split pin defect dataset demonstrate robust performance,achieving an accuracy rate of 72.1%and a mean average precision(mAP)of 67.7%,thereby validating the method’s effectiveness under challenging conditions.The proposed approach contributes to the field by specifically targeting the challenges associated with split pin detection in UAV-based inspections,offering a practically applicable and reliably precise method.展开更多
Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolut...Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks,Gated Recurrent Units,and a self-attention mechanism.Based on standardized data cleaning and normalization,the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately.The self-attention mechanism is then applied to assign weights to key time steps,after which the two feature streams are flattened and concatenated.Finally,a fully connected layer is used to generate the forecast.Under a training setup with mean squared error as the loss function and an adaptive optimization strategy,the proposed model consistently outperforms baseline methods across multiple error and fitting metrics,demonstrating stronger generalization capability and interpretability.The paper also provides a complete data processing and evaluation workflow,ensuring strong reproducibility and practical applicability.展开更多
Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal di...Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.展开更多
Metal droplet deposition is a kind of additive manufacturing(3D Printing)technique that fabricates near-net part through droplets deposition with lower cost and higher efficiency.This paper proposed a solution to prob...Metal droplet deposition is a kind of additive manufacturing(3D Printing)technique that fabricates near-net part through droplets deposition with lower cost and higher efficiency.This paper proposed a solution to problems of electric power fittings that large inventories,high procurement costs,low manufacturing efficiency and transportation cost.Using additive Manufacturing technique-metal droplet deposition,electric power fittings fabricated on power construction site.This paper describes the manufacturing process of typical thin-walled samples(the structure optimized based on additive manufacturing principle)and ball head rings of electric power fittings.Aiming at the integral AM forming for ball and ball socket electric power fitting workpiece,a novel easy removal forming support material(ceramics and gypsum mixed with UV cured resin)have been developed.Here this support material was used to fabricate nested integral workpieces.Dimensional accuracy and microstructure of the test pieces were analyzed.The error of the height and width of the forming workpiece is within 5%.No obvious overlap trace(such as overlap line and cracks)observed,and the internal microstructure is equiaxial crystal.The average density of the component is 99.51%,which measured by drainage method and 13.39%higher than the cast raw material.展开更多
The paper proposes a novel H∞ load frequency control(LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback(DOF) tracking-regulator cont...The paper proposes a novel H∞ load frequency control(LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback(DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multiarea power systems which also include uncertainties and timevarying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality(LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed(and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.展开更多
Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could n...Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.展开更多
In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route d...In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route distribution.However,existing routing algorithms do not take into account the degree of importance of services,thereby leading to load unbalancing and increasing the risks of services and networks.A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems.First,the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices,service load,and service characteristics.Second,service weights are determined with modified relative entropy TOPSIS method,and a balanced service routing determination algorithm is proposed.Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.展开更多
Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and thos...Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and those with high dynamic performance typically have low energy efficiency. In this paper, the variants of secondary control（VSC） with power recovery are developed to solve this problem for loading hydraulic driving devices that operate under variable pressure, unlike classical secondary control（CSC） that operates in constant pressure network. Hydrostatic secondary control units are used as the loading components, by which the absorbed mechanical power from the tested device is converted into hydraulic power and then fed back into the tested system through 4 types of feedback passages（FPs）. The loading subsystem can operate in constant pressure network, controlled variable pressure network, or the same variable pressure network as that of the tested device by using different FPs. The 4 types of systems are defined, and their key techniques are analyzed, including work principle, simulating the work state of original tested device, static operation points, loading performance, energy efficiency, and control strategy, etc. The important technical merits of the 4 schemes are compared, and 3 of the schemes are selected, designed, simulated using AMESim and evaluated. The researching results show that the investigated systems can simulate the given loads effectively, realize the work conditions of the tested device, and furthermore attain a high power recovery efficiency that ranges from 0.54 to 0.85, even though the 3 schemes have different loading performances and energy efficiencies. This paper proposes several loading schemes that can achieve both high dynamic performance and high power recovery efficiency.展开更多
Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.展开更多
In order to test the klystrons operated at a frequency of 3.7 GHz in a continuous wave （CW） mode, a type of water load to absorb its power up to 750 kW is presented. The distilled water sealed with an RF ceramic win...In order to test the klystrons operated at a frequency of 3.7 GHz in a continuous wave （CW） mode, a type of water load to absorb its power up to 750 kW is presented. The distilled water sealed with an RF ceramic window is used as the absorbent. At a frequency range of 70 MHz, the VSWR （Voltage Standing Wave Ratio） is below 1.2, and the rise in temperature of water is about 30 ℃ at the highest power level.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
Load flow is an important tool used by power engineers for planning, to determine the best operation for a power system and exchange of power between utility companies. In order to have an efficient operating power sy...Load flow is an important tool used by power engineers for planning, to determine the best operation for a power system and exchange of power between utility companies. In order to have an efficient operating power system, it is necessary to determine which method is suitable and efficient for the system’s load flow analysis. A power flow analysis method may take a long time and therefore prevent achieving an accurate result to a power flow solution because of continuous changes in power demand and generations. This paper presents analysis of the load flow problem in power system planning studies. The numerical methods: Gauss-Seidel, Newton-Raphson and Fast Decoupled methods were compared for a power flow analysis solution. Simulation is carried out using Matlab for test cases of IEEE 9-Bus, IEEE 30-Bus and IEEE 57-Bus system. The simulation results were compared for number of iteration, computational time, tolerance value and convergence. The compared results show that Newton-Raphson is the most reliable method because it has the least number of iteration and converges faster.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
The pseudo-excitation method combined with the integral transform method (PEM-ITM) is presented to investigate the ground vibration of a coupled track-soil system induced by moving random loads. Commonly in the track ...The pseudo-excitation method combined with the integral transform method (PEM-ITM) is presented to investigate the ground vibration of a coupled track-soil system induced by moving random loads. Commonly in the track model, the rail, sleepers, rail pads, and ballast are modelled as an infinite Euler beam, discretely distributed masses, discretely distributed vertical springs, and a viscoelastic layer, respectively. The soil is regarded as a homogenous isotropic half-space coupled with the track using the boundary condition at the surface of the ground. By introducing a pseudo-excitation, the random vibration analysis of the coupled system is converted into a harmonic analysis. The analytical form of evolutionary power spectral density responses of the simplified coupled track-soil system under a random moving load is derived in the frequency/wavenumber domain by PEM-ITM. In the numerical examples, the effects of different parameters, such as the moving speed, the soil properties, and the coherence of moving loads, on the ground response are investigated.展开更多
Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural ne...Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.展开更多
In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are ...In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.展开更多
This paper designs a decentralized resilient H_(∞)load frequency control(LFC)scheme for multi-area cyber-physical power systems(CPPSs).Under the network-based control framework,the sampled measurements are transmitte...This paper designs a decentralized resilient H_(∞)load frequency control(LFC)scheme for multi-area cyber-physical power systems(CPPSs).Under the network-based control framework,the sampled measurements are transmitted through the communication networks,which may be attacked by energylimited denial-of-service(DoS)attacks with a characterization of the maximum count of continuous data losses(resilience index).Each area is controlled in a decentralized mode,and the impacts on one area from other areas via their interconnections are regarded as the additional load disturbance of this area.Then,the closed-loop LFC system of each area under DoS attacks is modeled as an aperiodic sampled-data control system with external disturbances.Under this modeling,a decentralized resilient H_(∞)scheme is presented to design the state-feedback controllers with guaranteed H∞performance and resilience index based on a novel transmission interval-dependent loop functional method.When given the controllers,the proposed scheme can obtain a less conservative H_(∞)performance and resilience index that the LFC system can tolerate.The effectiveness of the proposed LFC scheme is evaluated on a one-area CPPS and two three-area CPPSs under DoS attacks.展开更多
Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was establi...Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.展开更多
For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously u...For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.展开更多
基金Fundamental Research Funds for the Central Universities(2023MS134)。
文摘In modern industrial applications,ensuring the reliability of mechanical fittings is critical for maintaining operational safety and efficiency,particularly in power grid systems where split pins serve a pivotal role despite being susceptible to environmental degradation and failure.Existing UAV-based inspection systems are hampered by a low representation of split pin elements and complex backgrounds,leading to challenges in accurate fault detection and timely maintenance.To address this pressing issue,our study proposes an innovative fault detection method for split pins.The approach employs a three-step process:first,cropping operations are used to accurately isolate the fittings containing split pins;second,super-resolution reconstruction is applied to enhance image clarity and detail;and finally,an improved YOLOv8 network,augmented with inner-shape IoU and local window attention mechanisms,is utilized to refine local feature extraction and annotation accuracy.Experimental evaluations on a split pin defect dataset demonstrate robust performance,achieving an accuracy rate of 72.1%and a mean average precision(mAP)of 67.7%,thereby validating the method’s effectiveness under challenging conditions.The proposed approach contributes to the field by specifically targeting the challenges associated with split pin detection in UAV-based inspections,offering a practically applicable and reliably precise method.
基金supported in part by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025)the Innovation and Entrepreneurship Training Program for College students of University of Science and Technology Liaoning(S202510146005).
文摘Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks,Gated Recurrent Units,and a self-attention mechanism.Based on standardized data cleaning and normalization,the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately.The self-attention mechanism is then applied to assign weights to key time steps,after which the two feature streams are flattened and concatenated.Finally,a fully connected layer is used to generate the forecast.Under a training setup with mean squared error as the loss function and an adaptive optimization strategy,the proposed model consistently outperforms baseline methods across multiple error and fitting metrics,demonstrating stronger generalization capability and interpretability.The paper also provides a complete data processing and evaluation workflow,ensuring strong reproducibility and practical applicability.
文摘Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.
基金This research was funded by National Natural Science Foundation of China under grant number 51575313 and 51775420.This paper got help from Du Jun and Wang Xin of Xi’an Jiaotong University.
文摘Metal droplet deposition is a kind of additive manufacturing(3D Printing)technique that fabricates near-net part through droplets deposition with lower cost and higher efficiency.This paper proposed a solution to problems of electric power fittings that large inventories,high procurement costs,low manufacturing efficiency and transportation cost.Using additive Manufacturing technique-metal droplet deposition,electric power fittings fabricated on power construction site.This paper describes the manufacturing process of typical thin-walled samples(the structure optimized based on additive manufacturing principle)and ball head rings of electric power fittings.Aiming at the integral AM forming for ball and ball socket electric power fitting workpiece,a novel easy removal forming support material(ceramics and gypsum mixed with UV cured resin)have been developed.Here this support material was used to fabricate nested integral workpieces.Dimensional accuracy and microstructure of the test pieces were analyzed.The error of the height and width of the forming workpiece is within 5%.No obvious overlap trace(such as overlap line and cracks)observed,and the internal microstructure is equiaxial crystal.The average density of the component is 99.51%,which measured by drainage method and 13.39%higher than the cast raw material.
文摘The paper proposes a novel H∞ load frequency control(LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback(DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multiarea power systems which also include uncertainties and timevarying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality(LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed(and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.
基金Supported by the Science and Technology Research Project Fund of Provincial Department of Education(12531004)Project of Heilongjiang Leading Talent Echelon Talented(2012)
文摘Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.
基金supported by the State Grid project which names the simulation and service quality evaluation technology research of power communication network(No.XX71-14-046)
文摘In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route distribution.However,existing routing algorithms do not take into account the degree of importance of services,thereby leading to load unbalancing and increasing the risks of services and networks.A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems.First,the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices,service load,and service characteristics.Second,service weights are determined with modified relative entropy TOPSIS method,and a balanced service routing determination algorithm is proposed.Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.
文摘Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and those with high dynamic performance typically have low energy efficiency. In this paper, the variants of secondary control（VSC） with power recovery are developed to solve this problem for loading hydraulic driving devices that operate under variable pressure, unlike classical secondary control（CSC） that operates in constant pressure network. Hydrostatic secondary control units are used as the loading components, by which the absorbed mechanical power from the tested device is converted into hydraulic power and then fed back into the tested system through 4 types of feedback passages（FPs）. The loading subsystem can operate in constant pressure network, controlled variable pressure network, or the same variable pressure network as that of the tested device by using different FPs. The 4 types of systems are defined, and their key techniques are analyzed, including work principle, simulating the work state of original tested device, static operation points, loading performance, energy efficiency, and control strategy, etc. The important technical merits of the 4 schemes are compared, and 3 of the schemes are selected, designed, simulated using AMESim and evaluated. The researching results show that the investigated systems can simulate the given loads effectively, realize the work conditions of the tested device, and furthermore attain a high power recovery efficiency that ranges from 0.54 to 0.85, even though the 3 schemes have different loading performances and energy efficiencies. This paper proposes several loading schemes that can achieve both high dynamic performance and high power recovery efficiency.
文摘Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
基金supported by National Natural Science Foundation of China(61533013,61273144)Scientific Technology Research and Development Plan Project of Tangshan(13130298B)Scientific Technology Research and Development Plan Project of Hebei(z2014070)
文摘In order to test the klystrons operated at a frequency of 3.7 GHz in a continuous wave （CW） mode, a type of water load to absorb its power up to 750 kW is presented. The distilled water sealed with an RF ceramic window is used as the absorbent. At a frequency range of 70 MHz, the VSWR （Voltage Standing Wave Ratio） is below 1.2, and the rise in temperature of water is about 30 ℃ at the highest power level.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
文摘Load flow is an important tool used by power engineers for planning, to determine the best operation for a power system and exchange of power between utility companies. In order to have an efficient operating power system, it is necessary to determine which method is suitable and efficient for the system’s load flow analysis. A power flow analysis method may take a long time and therefore prevent achieving an accurate result to a power flow solution because of continuous changes in power demand and generations. This paper presents analysis of the load flow problem in power system planning studies. The numerical methods: Gauss-Seidel, Newton-Raphson and Fast Decoupled methods were compared for a power flow analysis solution. Simulation is carried out using Matlab for test cases of IEEE 9-Bus, IEEE 30-Bus and IEEE 57-Bus system. The simulation results were compared for number of iteration, computational time, tolerance value and convergence. The compared results show that Newton-Raphson is the most reliable method because it has the least number of iteration and converges faster.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金the National Basic Research Program of China (Grant 2014CB046803)the National Natural Science Foundation of China (Grant 11772084).
文摘The pseudo-excitation method combined with the integral transform method (PEM-ITM) is presented to investigate the ground vibration of a coupled track-soil system induced by moving random loads. Commonly in the track model, the rail, sleepers, rail pads, and ballast are modelled as an infinite Euler beam, discretely distributed masses, discretely distributed vertical springs, and a viscoelastic layer, respectively. The soil is regarded as a homogenous isotropic half-space coupled with the track using the boundary condition at the surface of the ground. By introducing a pseudo-excitation, the random vibration analysis of the coupled system is converted into a harmonic analysis. The analytical form of evolutionary power spectral density responses of the simplified coupled track-soil system under a random moving load is derived in the frequency/wavenumber domain by PEM-ITM. In the numerical examples, the effects of different parameters, such as the moving speed, the soil properties, and the coherence of moving loads, on the ground response are investigated.
基金supported by the Major Project of Basic and Applied Research in Guangdong Universities (2017WZDXM012)。
文摘Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.
基金National Key Research and Development Program of China(Grant No.2020YFB2009702)National Natural Science Foundation of China(Grant Nos.52075055,U21A20124 and 52111530069)Chongqing Natural Science Foundation of China(Grant No.cstc2020jcyj-msxmX0780)。
文摘In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.
基金supported by the National Natural Science Foundation(NNSF)of China(62003037,61873303)。
文摘This paper designs a decentralized resilient H_(∞)load frequency control(LFC)scheme for multi-area cyber-physical power systems(CPPSs).Under the network-based control framework,the sampled measurements are transmitted through the communication networks,which may be attacked by energylimited denial-of-service(DoS)attacks with a characterization of the maximum count of continuous data losses(resilience index).Each area is controlled in a decentralized mode,and the impacts on one area from other areas via their interconnections are regarded as the additional load disturbance of this area.Then,the closed-loop LFC system of each area under DoS attacks is modeled as an aperiodic sampled-data control system with external disturbances.Under this modeling,a decentralized resilient H_(∞)scheme is presented to design the state-feedback controllers with guaranteed H∞performance and resilience index based on a novel transmission interval-dependent loop functional method.When given the controllers,the proposed scheme can obtain a less conservative H_(∞)performance and resilience index that the LFC system can tolerate.The effectiveness of the proposed LFC scheme is evaluated on a one-area CPPS and two three-area CPPSs under DoS attacks.
基金supported by National Natural Science Foundation of China(Grant 61273151).
文摘Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.
基金supportedin part by Natural Science Foundation under grant No.91338108,91438206Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.
