This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity fa...This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric...Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the Support Vector Machine(SVM)algorithm.This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran.The participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each group.The study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the models.The collected data were fed into SPSS 18,followed by conducting linear regression and paired samples t-test.It was revealed that,in the first model(SPL<70 dBA),the frequency of 8 KHz had the greatest impact(with a weight of 33%),while noise had the smallest influence(with a weight of 5%).The accuracy of this model was 100%.In the second model(70<SPL<80 dBA),the frequency of 4 KHz had the most profound effect(with a weight of 21%),whereas the frequency of 250 Hz had the lowest impact(with a weight of 6%).The accuracy of this model was 100%too.In the third model(SPL>85 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%too.In the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the frequency of 500 Hz had the smallest effect(with a weight of 4%).The accuracy of this model was found to be 94%.According to the modeling conducted using the SVM algorithm,the frequency of 4 KHz has the most profound effect on predicting changes in hearing loss.Given the high accuracy of the obtained model,this algorithm is an appropriate and powerful tool to predict and model hearing loss.展开更多
This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devic...This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.展开更多
This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and e...This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss.GA fitness function is introduced including the active power losses,reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable.GA fitness function is subjected to voltage constraints,active and reactive power losses constraints and DG size constraint.展开更多
In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify ...In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.展开更多
Aim To Put forward an improve algorithm for real-time connection admission control (CAC) decision. Methods converted equations of Cell loss ratio (CLR) upper bound were derived based on typical approaches to reduce co...Aim To Put forward an improve algorithm for real-time connection admission control (CAC) decision. Methods converted equations of Cell loss ratio (CLR) upper bound were derived based on typical approaches to reduce computing load. Moreover, the effects of usage parameter control (UPC) policing and bandwidth allocation functions were combined to deduce improver equations. computing load and admission region of the new algorithm were analyzed, while numerical examples and comparisons with classical non-parametic method were also included. Results Computing burden was reduced. More accurate estimation of CLR was obtained. The admission region of new algorithm was larger than typical non-parametric approach and was variable according to the burst length.Conclusion The calculation burden of new algorithm for CLR estimation is lighter than classical non-parametric approach. In addition to that, just as many information as what we can get in connection setup process are used for CAC decision. So it is not only a practicable but also an effective method that can be used in real-time processing.展开更多
Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This ...Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.展开更多
Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the...Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGP-EGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance. Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.展开更多
This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint mo...This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.展开更多
The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such a...The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.展开更多
Due to the large rotor eddy current loss and low thermal conductivity of carbon fiber sleeve,the high temperature usually occurs in high speed permanent magnet machines(HSPMMs)at the rated operation condition,resultin...Due to the large rotor eddy current loss and low thermal conductivity of carbon fiber sleeve,the high temperature usually occurs in high speed permanent magnet machines(HSPMMs)at the rated operation condition,resulting in irreversible demagnetization of the permanent magnet(PM).To obtain low rotor temperature,two novel rotor structures with low rotor eddy current loss are proposed in this paper.With the output torque and air gap flux density unchanged,the performance of HSPMMs with the two proposed rotor structures are analyzed based on finite element algorithm(FEA),including eddy current loss and temperature.Finally,the appropriate parameters of the proposed rotor structures are selected,and the electromagnetic(EM)performance,rotor stress and temperature are compared with those of the conventional rotor structure.Index Terms-Eddy current loss,finite element algorithm(FEA),electromagnetic(EM)performance,high speed permanent magnet machines(HSPMMs).展开更多
With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization p...With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.展开更多
Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key ro...Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.展开更多
The paper dwells on the unified power quality indexes characterizing the phenomenon of voltage unbalance in three-phase systems. Voltage unbalance is one of the commonest occurrences in the town mains of 0.38 kV volta...The paper dwells on the unified power quality indexes characterizing the phenomenon of voltage unbalance in three-phase systems. Voltage unbalance is one of the commonest occurrences in the town mains of 0.38 kV voltage. The phenomenon describes as inequality of vector magnitude of phase voltage and shearing angle between them. Causes and consequences of the voltage unbalance in distribution networks have been considered. The algorithm, which allows switching one-phase load, has been developed as one of the methods of reducing the unbalance level. The algorithm is written in the function block diagram programming language. For determining the duration and magnitude of the unbalance level it is proposed to introduce the forecasting algorithm. The necessary data for forecasting are accumulated in the course of the algorithm based on the Function Block Diagram. The algorithm example is given for transforming substation of the urban electrical power supply system. The results of the economic efficiency assessment of the algorithm implementation are shown in conclusion. The use of automatic switching of the one-phase load for explored substation allows reducing energy losses (active electric energy by 7.63%;reactive energy by 8.37%). It also allows improving supply quality to a consumer. For explored substation the average zero-sequence unbalance factor has dropped from 3.59% to 2.13%, and the negative-sequence unbalance factor has dropped from 0.61% to 0.36%.展开更多
Joule losses in the power grids are a factor in the degradation of power grid equipment (lines and transformers), but also a shortfall for power companies, which must maximize their revenues. This is why in this artic...Joule losses in the power grids are a factor in the degradation of power grid equipment (lines and transformers), but also a shortfall for power companies, which must maximize their revenues. This is why in this article we present a study on the optimization of joule losses in a meshed electricity network interconnected to a very high voltage power line crossing Congolese territory for nearly one thousand five hundred (1500) kilometers. The value of interconnections no longer needs to be demonstrated in view of these technical, economic and social advantages. The object of this study is to assess the optimal node where the interconnection can be achieved which would cause fewer joule losses in the networks to be interconnected on this line. Indeed, it can have several possible nodes where the interconnection can be carried out, so it is necessary to simulate each configuration in order to evaluate the losses using the hybrid method including the genetic algorithm to provide different configurations and the Newton Raphson algorithm. Advanced integrating FACTS devices in this case STATCOM to perform load flow. The results are interesting because by injecting electrical energy to the network or to the line to the same selected, the joule losses are within the standards provided by the International Electrotechnical Commission.展开更多
In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identif...In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.展开更多
文摘This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
基金This study stemmed from a research project(code number:96000838)which was sponsored by the Institute for Futures Studies in Health at Kerman University of Medical Sciences.
文摘Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the Support Vector Machine(SVM)algorithm.This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran.The participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each group.The study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the models.The collected data were fed into SPSS 18,followed by conducting linear regression and paired samples t-test.It was revealed that,in the first model(SPL<70 dBA),the frequency of 8 KHz had the greatest impact(with a weight of 33%),while noise had the smallest influence(with a weight of 5%).The accuracy of this model was 100%.In the second model(70<SPL<80 dBA),the frequency of 4 KHz had the most profound effect(with a weight of 21%),whereas the frequency of 250 Hz had the lowest impact(with a weight of 6%).The accuracy of this model was 100%too.In the third model(SPL>85 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%too.In the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the frequency of 500 Hz had the smallest effect(with a weight of 4%).The accuracy of this model was found to be 94%.According to the modeling conducted using the SVM algorithm,the frequency of 4 KHz has the most profound effect on predicting changes in hearing loss.Given the high accuracy of the obtained model,this algorithm is an appropriate and powerful tool to predict and model hearing loss.
基金supported by Borujerd Branch,Islamic Azad University Iran
文摘This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.
文摘This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss.GA fitness function is introduced including the active power losses,reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable.GA fitness function is subjected to voltage constraints,active and reactive power losses constraints and DG size constraint.
基金the Sichuan Science and Technology Program(No.2022YFS0557)the National Natural Science Foundation of China(No.61972271)。
文摘In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.
文摘Aim To Put forward an improve algorithm for real-time connection admission control (CAC) decision. Methods converted equations of Cell loss ratio (CLR) upper bound were derived based on typical approaches to reduce computing load. Moreover, the effects of usage parameter control (UPC) policing and bandwidth allocation functions were combined to deduce improver equations. computing load and admission region of the new algorithm were analyzed, while numerical examples and comparisons with classical non-parametic method were also included. Results Computing burden was reduced. More accurate estimation of CLR was obtained. The admission region of new algorithm was larger than typical non-parametric approach and was variable according to the burst length.Conclusion The calculation burden of new algorithm for CLR estimation is lighter than classical non-parametric approach. In addition to that, just as many information as what we can get in connection setup process are used for CAC decision. So it is not only a practicable but also an effective method that can be used in real-time processing.
文摘Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （No. 50579009, 70425001）.
文摘Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGP-EGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance. Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.
文摘This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.
文摘The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.
基金This work has been partly supported by the National Natural Science Foundation of China(NSFC 51877093,51807075)National Key Research and Development Program of China(Project ID:2018YFE0100200)+2 种基金Fundamental Research Funds for the Central Universities(2019kfyXMBZ031)Project funded by China Postdoctoral Science Foundation(2019M652640)Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ5009 and Scientific Research Program Funded by Shaanxi Provincial Education Department of China under Grant No.18JK0398。
文摘Due to the large rotor eddy current loss and low thermal conductivity of carbon fiber sleeve,the high temperature usually occurs in high speed permanent magnet machines(HSPMMs)at the rated operation condition,resulting in irreversible demagnetization of the permanent magnet(PM).To obtain low rotor temperature,two novel rotor structures with low rotor eddy current loss are proposed in this paper.With the output torque and air gap flux density unchanged,the performance of HSPMMs with the two proposed rotor structures are analyzed based on finite element algorithm(FEA),including eddy current loss and temperature.Finally,the appropriate parameters of the proposed rotor structures are selected,and the electromagnetic(EM)performance,rotor stress and temperature are compared with those of the conventional rotor structure.Index Terms-Eddy current loss,finite element algorithm(FEA),electromagnetic(EM)performance,high speed permanent magnet machines(HSPMMs).
基金This research is supported by the Science and Technology Program of Gansu Province(No.23JRRA880).
文摘With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.
基金funded by the Russian Foundation for Basic Research(RFBR)(No.20-07-00531).
文摘Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.
文摘The paper dwells on the unified power quality indexes characterizing the phenomenon of voltage unbalance in three-phase systems. Voltage unbalance is one of the commonest occurrences in the town mains of 0.38 kV voltage. The phenomenon describes as inequality of vector magnitude of phase voltage and shearing angle between them. Causes and consequences of the voltage unbalance in distribution networks have been considered. The algorithm, which allows switching one-phase load, has been developed as one of the methods of reducing the unbalance level. The algorithm is written in the function block diagram programming language. For determining the duration and magnitude of the unbalance level it is proposed to introduce the forecasting algorithm. The necessary data for forecasting are accumulated in the course of the algorithm based on the Function Block Diagram. The algorithm example is given for transforming substation of the urban electrical power supply system. The results of the economic efficiency assessment of the algorithm implementation are shown in conclusion. The use of automatic switching of the one-phase load for explored substation allows reducing energy losses (active electric energy by 7.63%;reactive energy by 8.37%). It also allows improving supply quality to a consumer. For explored substation the average zero-sequence unbalance factor has dropped from 3.59% to 2.13%, and the negative-sequence unbalance factor has dropped from 0.61% to 0.36%.
文摘Joule losses in the power grids are a factor in the degradation of power grid equipment (lines and transformers), but also a shortfall for power companies, which must maximize their revenues. This is why in this article we present a study on the optimization of joule losses in a meshed electricity network interconnected to a very high voltage power line crossing Congolese territory for nearly one thousand five hundred (1500) kilometers. The value of interconnections no longer needs to be demonstrated in view of these technical, economic and social advantages. The object of this study is to assess the optimal node where the interconnection can be achieved which would cause fewer joule losses in the networks to be interconnected on this line. Indeed, it can have several possible nodes where the interconnection can be carried out, so it is necessary to simulate each configuration in order to evaluate the losses using the hybrid method including the genetic algorithm to provide different configurations and the Newton Raphson algorithm. Advanced integrating FACTS devices in this case STATCOM to perform load flow. The results are interesting because by injecting electrical energy to the network or to the line to the same selected, the joule losses are within the standards provided by the International Electrotechnical Commission.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61305110)the Self-Planned Task of Institute of Robotics(Grant No.F201803)the Intelligent Systems and Natural Science Foundation of Hubei Province(Grant No.2018CFB626).
文摘In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.
