Yinan Guo;Yao Huang;Shirong Ge;Yizhe Zhang;Ersong Jiang;Bin Cheng;Shengxiang Yang
Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.
{"title":"Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles","authors":"Yinan Guo;Yao Huang;Shirong Ge;Yizhe Zhang;Ersong Jiang;Bin Cheng;Shengxiang Yang","doi":"10.23919/CSMS.2023.0011","DOIUrl":"10.23919/CSMS.2023.0011","url":null,"abstract":"Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206015.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41492582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.
{"title":"Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints","authors":"Shulong Zhao;Qipeng Wang;Jiayi Zheng;Xiangke Wang","doi":"10.23919/CSMS.2023.0009","DOIUrl":"10.23919/CSMS.2023.0009","url":null,"abstract":"Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206018.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42538354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Portfolio optimization is a classical and important problem in the field of asset management, which aims to achieve a trade-off between profit and risk. Previous portfolio optimization models use traditional risk measurements such as variance, which symmetrically delineate both positive and negative sides and are not practical and stable. In this paper, a new model with cardinality constraints is first proposed, in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way. The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms (MOEAs). To solve the model, a Learning-Guided Evolutionary Algorithm based on I ε+ indicator (I ε+ LGEA) is developed. In I ε+ LGEA the I ε+ indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm. And a new constraint-handling method based on I ε+ indicator is also adopted to ensure the feasibility of solutions. The experimental results on five portfolio trading datasets including up to 1226 assets show that I ε+ LGEA outperforms some state-of-the-art MOEAs in most cases.
{"title":"Iε+LGEA A Learning-Guided Evolutionary Algorithm Based on Iε+ Indicator for Portfolio Optimization","authors":"Feng Wang;Zilu Huang;Shuwen Wang","doi":"10.23919/CSMS.2023.0012","DOIUrl":"10.23919/CSMS.2023.0012","url":null,"abstract":"Portfolio optimization is a classical and important problem in the field of asset management, which aims to achieve a trade-off between profit and risk. Previous portfolio optimization models use traditional risk measurements such as variance, which symmetrically delineate both positive and negative sides and are not practical and stable. In this paper, a new model with cardinality constraints is first proposed, in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way. The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms (MOEAs). To solve the model, a Learning-Guided Evolutionary Algorithm based on I\u0000<inf>ε+</inf>\u0000 indicator (I\u0000<inf>ε+</inf>\u0000LGEA) is developed. In I\u0000<inf>ε+</inf>\u0000LGEA the I\u0000<inf>ε+</inf>\u0000 indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm. And a new constraint-handling method based on I\u0000<inf>ε+</inf>\u0000 indicator is also adopted to ensure the feasibility of solutions. The experimental results on five portfolio trading datasets including up to 1226 assets show that I\u0000<inf>ε+</inf>\u0000LGEA outperforms some state-of-the-art MOEAs in most cases.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206019.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43576620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advantages of levitation/guidance self-stability, large levitation gap, and high lift-to-drag ratios, superconducting electrodynamic suspension (SC-EDS) train is becoming a viable candidate for the high-speed and ultra-high-speed rail transportation. In order to provide the basis for designing the optimization and control strategy, this paper establishes a comprehensive model for the SC-EDS train, which considers the dynamics of the bogie and car body in all directions. The obtained model reveals the complex coupling and feedback relationships among the variables, which cannot be described by the existing local models of the SC-EDS train. Simulation examples under different parameters and initial conditions are presented and discussed to demonstrate the potential use of the model given in this paper.
{"title":"Comprehensive Model Construction and Simulation for Superconducting Electrodynamic Suspension Train","authors":"Linfeng Liu;Hao Ye;Wei Dong;Junfeng Cui","doi":"10.23919/CSMS.2023.0010","DOIUrl":"10.23919/CSMS.2023.0010","url":null,"abstract":"With the advantages of levitation/guidance self-stability, large levitation gap, and high lift-to-drag ratios, superconducting electrodynamic suspension (SC-EDS) train is becoming a viable candidate for the high-speed and ultra-high-speed rail transportation. In order to provide the basis for designing the optimization and control strategy, this paper establishes a comprehensive model for the SC-EDS train, which considers the dynamics of the bogie and car body in all directions. The obtained model reveals the complex coupling and feedback relationships among the variables, which cannot be described by the existing local models of the SC-EDS train. Simulation examples under different parameters and initial conditions are presented and discussed to demonstrate the potential use of the model given in this paper.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206016.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41507103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoning Shen;Hongli Pan;Zhongpei Ge;Wenyan Chen;Liyan Song;Shuo Wang
Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection, which incorporates practical factors like the limited capacity, maximum working hours, and multiple trips of each vehicle. Considering both economy and environment, fixed costs, fuel costs, and carbon emission costs are minimized together. To solve the formulated model effectively, contribution-based adaptive particle swarm optimization is proposed. Four strategies named greedy learning, multi-operator learning, exploring learning, and exploiting learning are specifically designed with their own searching priorities. By assessing the contribution of each learning strategy during the process of evolution, an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm. Moreover, an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved. Performance of the proposed algorithm is tested on ten waste collection instances, which include one real-world case derived from the Green Ring Company of Jiangbei New District, Nanjing, China, and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets. Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.
{"title":"Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization","authors":"Xiaoning Shen;Hongli Pan;Zhongpei Ge;Wenyan Chen;Liyan Song;Shuo Wang","doi":"10.23919/CSMS.2023.0008","DOIUrl":"10.23919/CSMS.2023.0008","url":null,"abstract":"Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection, which incorporates practical factors like the limited capacity, maximum working hours, and multiple trips of each vehicle. Considering both economy and environment, fixed costs, fuel costs, and carbon emission costs are minimized together. To solve the formulated model effectively, contribution-based adaptive particle swarm optimization is proposed. Four strategies named greedy learning, multi-operator learning, exploring learning, and exploiting learning are specifically designed with their own searching priorities. By assessing the contribution of each learning strategy during the process of evolution, an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm. Moreover, an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved. Performance of the proposed algorithm is tested on ten waste collection instances, which include one real-world case derived from the Green Ring Company of Jiangbei New District, Nanjing, China, and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets. Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206017.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49326483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5·12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.
{"title":"Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling","authors":"Zhen Shu;Aijuan Song;Guohua Wu;Witold Pedrycz","doi":"10.23919/CSMS.2023.0006","DOIUrl":"10.23919/CSMS.2023.0006","url":null,"abstract":"Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5·12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158517.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48914409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mao Tan;Zhonglin Zhang;Yuling Ren;Irampaye Richard;Yuzhou Zhang
As the number of electric vehicles (EVs) increases, massive numbers of EVs have started to gather in commercial parking lots to charge and discharge, which may significantly impact the operation of the grid. There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots. This deviation can lead to insufficient battery energy when the EVs leave the parking lot. This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model, and the agent-based model can fully reflect the autonomy of individual EVs. Based on this simulation model, we propose an EV scheduling algorithm. The algorithm contains two main agents. The first is the power distribution center agent (PDCA), which is used to coordinate the energy output of photovoltaic (PV), energy storage system (ESS), and distribution station (DS) to solve the problem of grid overload. The second is the scheduling center agent (SCA), which is used to solve the insufficient battery energy problem due to EVs' random departures. The SCA includes two stages. In the first stage, a priority scheduling algorithm is proposed to emphasize the fairness of EV charging. In the second stage, a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner. Finally, simulation experiments are conducted in AnyLogic, and the results demonstrate the superiority of the algorithm over the classical algorithm.
{"title":"Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots","authors":"Mao Tan;Zhonglin Zhang;Yuling Ren;Irampaye Richard;Yuzhou Zhang","doi":"10.23919/CSMS.2023.0005","DOIUrl":"10.23919/CSMS.2023.0005","url":null,"abstract":"As the number of electric vehicles (EVs) increases, massive numbers of EVs have started to gather in commercial parking lots to charge and discharge, which may significantly impact the operation of the grid. There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots. This deviation can lead to insufficient battery energy when the EVs leave the parking lot. This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model, and the agent-based model can fully reflect the autonomy of individual EVs. Based on this simulation model, we propose an EV scheduling algorithm. The algorithm contains two main agents. The first is the power distribution center agent (PDCA), which is used to coordinate the energy output of photovoltaic (PV), energy storage system (ESS), and distribution station (DS) to solve the problem of grid overload. The second is the scheduling center agent (SCA), which is used to solve the insufficient battery energy problem due to EVs' random departures. The SCA includes two stages. In the first stage, a priority scheduling algorithm is proposed to emphasize the fairness of EV charging. In the second stage, a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner. Finally, simulation experiments are conducted in AnyLogic, and the results demonstrate the superiority of the algorithm over the classical algorithm.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158520.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m- dimensional Gaussian stochastic vector process $boldsymbol{Z}(t)$ is employed to model the temporal-spatial correlation of wind speeds at $m$ different sites. $ln$ general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of $boldsymbol{Z}(t)$ , which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of $boldsymbol{Z}(t)$ can be analytically obtained. $text{Then}$ , Fourier transformation is implemented to establish the cross-spectral density matrix of $boldsymbol{Z}(t)$ , and an analytical approach is proposed to generate samples of wind speeds at $m$ different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.
{"title":"Simulating Temporally and Spatially Correlated Wind Speed Time Series by Spectral Representation Method","authors":"Qing Xiao;Lianghong Wu;Xiaowen Wu;Matthias Rätsch","doi":"10.23919/CSMS.2023.0007","DOIUrl":"10.23919/CSMS.2023.0007","url":null,"abstract":"In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m- dimensional Gaussian stochastic vector process \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000 is employed to model the temporal-spatial correlation of wind speeds at \u0000<tex>$m$</tex>\u0000 different sites. \u0000<tex>$ln$</tex>\u0000 general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000, which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000 can be analytically obtained. \u0000<tex>$text{Then}$</tex>\u0000, Fourier transformation is implemented to establish the cross-spectral density matrix of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000, and an analytical approach is proposed to generate samples of wind speeds at \u0000<tex>$m$</tex>\u0000 different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158490.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46730014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine stator winding insulation degradation is one of the main results of machine aging. It is non-negligible once this degradation process becomes asymmetric between phases. The traditional way to determine the insulation state of health is a partial discharge test. However, this method requires the system offline, which causes production loss and extra administrative burden. This paper presents an idea for better characterizing the insulation machine's state of health using common-mode (CM) behavior in the machine-drive system. With the help of circuit decomposition methods and modeling tools, the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode (DM) quantities in the equivalent circuit. It is shown effective to represent the asymmetric aging effect from the detection of system leakage current. This paper provides an analytical method to quantify this characteristic from mathematical approaches, and a proper approximation has been made on the CM equivalent model (CEM) such that the CM behavior is accurately characterized. The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit. Researchers can adapt this method to quantify and characterize the machine insulation state of health (SOH).
{"title":"Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation","authors":"Jin Zhao;Aaron D. Brovont","doi":"10.23919/CSMS.2023.0004","DOIUrl":"10.23919/CSMS.2023.0004","url":null,"abstract":"Machine stator winding insulation degradation is one of the main results of machine aging. It is non-negligible once this degradation process becomes asymmetric between phases. The traditional way to determine the insulation state of health is a partial discharge test. However, this method requires the system offline, which causes production loss and extra administrative burden. This paper presents an idea for better characterizing the insulation machine's state of health using common-mode (CM) behavior in the machine-drive system. With the help of circuit decomposition methods and modeling tools, the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode (DM) quantities in the equivalent circuit. It is shown effective to represent the asymmetric aging effect from the detection of system leakage current. This paper provides an analytical method to quantify this characteristic from mathematical approaches, and a proper approximation has been made on the CM equivalent model (CEM) such that the CM behavior is accurately characterized. The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit. Researchers can adapt this method to quantify and characterize the machine insulation state of health (SOH).","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158518.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42562755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bin Wu;Shuangwei Bai;Bijina Rajbhandari;Bangyuan Li;Kesheng Wang
Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation. To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief, the emergency procurement model based on quantity flexibility contract is constructed. The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits. Further, it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises, focusing on the negative impact of supply disruption on the government and enterprises. The model deduction and data analysis results show that supply disruption creates a “lose-lose” situation for governments and enterprises, reducing their benefits and willingness to cooperate. Finally, a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.
{"title":"Quantity Flexibility Contract Model for Emergency Procurement Considering Supply Disruption","authors":"Bin Wu;Shuangwei Bai;Bijina Rajbhandari;Bangyuan Li;Kesheng Wang","doi":"10.23919/CSMS.2023.0002","DOIUrl":"10.23919/CSMS.2023.0002","url":null,"abstract":"Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation. To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief, the emergency procurement model based on quantity flexibility contract is constructed. The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits. Further, it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises, focusing on the negative impact of supply disruption on the government and enterprises. The model deduction and data analysis results show that supply disruption creates a “lose-lose” situation for governments and enterprises, reducing their benefits and willingness to cooperate. Finally, a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158491.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42679020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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