scholarly journals Halt Optimization Strategy for a Space Manipulator with a Joint-Locked Failure

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Gang Chen ◽  
Lanpu Li ◽  
Yingzhuo Fu ◽  
Bonan Yuan ◽  
Junting Fei
Keyword(s):  
Optimization Model ◽  
Optimal Configuration ◽  
Optimization Strategy ◽  
Configuration Optimization ◽  
Space Manipulator ◽  
Motion Optimization ◽  
Disturbance Torque

Aiming at a space manipulator with a joint-locked failure, a halt optimization strategy is proposed in this paper. Firstly, a halt configuration optimization model (HCOM) is constructed, to select an optimal configuration where the kinematic ability of the manipulator is the best. Secondly, considering the constraint of joint running parameters and the disturbance torque of the base, we construct and solve the halt motion optimization model (HMOM), which can achieve a steady halt and ensure the safety of the manipulator. The correctness and effectiveness of the proposed strategy in this paper are verified with a 7-DOF space manipulator. This strategy firstly puts forward the idea of halt configuration optimization and realizes the minimum global disturbance torque of the base in the halt process.

Air Material Order Optimization Strategy Based on Aircraft Availability

2014 ◽  
Vol 926-930 ◽  
pp. 739-742
Author(s):  
Ya Qun He ◽  
Ji Jun Li ◽  
Hong Yang Guo ◽  
Hui Gao ◽  
Tao Wang
Keyword(s):  
Optimization Model ◽  
Optimization Strategy ◽  
Marginal Analysis ◽  
Model Derivation

To solve the problem of air material order problems of the station, this paper established under funding constraints to aircraft availability as a measure of the air material order optimization model, derivation the method of marginal analysis to solve the model, and use examples to illustrate the effectiveness and practicality of the method.

scholarly journals An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4391
Author(s):  
Zhiyong Li ◽  
Shiping Pu ◽  
Yougen Chen ◽  
Renyong Wei
Keyword(s):  
Optimization Model ◽  
Power Loss ◽  
Optimal Solution ◽  
Loss Ratio ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Line Voltage ◽  
Multi Objective ◽  
Double Frequency ◽  
Voltage Ripple

Setting reasonable circuit parameters is an important way to improve the quality of inverters, including waveform quality and power loss. In this paper, a circuit system of line voltage cascaded quasi-Z-source inverter (LVC-qZSI) is built. On this basis, the double frequency voltage ripple ratio and power loss ratio are selected as optimization targets to establish a multi-objective optimization model of LVC-qZSI parameters. To simplify the calculation, an integration optimization strategy of LVC-qZSI parameters based on GRA-FA is proposed. Where, the grey relation analysis (GRA) is used to simplify the multi-objective optimization model. In GRA, the main influence factors are selected as optimization variables by considering the preference coefficient. Then, firefly algorithm (FA) is used to obtain the optimal solution of the multi-objective optimization model. In FA, the weights of objective functions are assigned based on the principle of information entropy. The analysis results are verified by simulation. Research results indicate that the optimization strategy can effectively reduce the double frequency voltage ripple ratio and power loss ratio. Therefore, the strategy proposed in this paper has a superior ability to optimize the parameters of LVC-qZSI, which is of great significance to the initial values setting.

A Model for Seat Allocation of Railway Passenger Transport Based on OD’s Priority

2014 ◽  
Vol 989-994 ◽  
pp. 2344-2348 ◽  
Author(s):  
Ying Ting Zhu ◽  
Fu Zhang Wang ◽  
Xing Hua Shan ◽  
Xiao Yan Lv
Keyword(s):  
Revenue Management ◽  
Inventory Control ◽  
Optimization Model ◽  
Control Method ◽  
Optimization Strategy ◽  
Passenger Transport ◽  
Railway Passenger Transport ◽  
Seat Allocation ◽  
Railway Passenger ◽  
Seat Inventory Control

Based on the seat inventory control method of revenue management in airline, the author researches the optimization strategy on the seat inventory control in railway passenger transport. The author proposes one optimization model for seats allocation, which is to allocate seats by calculating the value to determine the order of getting seats for each OD under the analysis of the interaction among all trains running on the same rail lines and those trains’ demands intensity. The experiment results show that this new model can be used to allocate seats for the train with multiple stations rapidly and appropriately. In comparison with the method without considering the value of each OD, the model based on OD’s value can get better results.

Research on Construction Method of Optimization Model between Customer Requirements and Product Technical Characteristics Configuration

2011 ◽  
Vol 55-57 ◽  
pp. 2139-2146
Author(s):  
Xiao An Yang ◽  
Hao Chen ◽  
Bing Bing Wang ◽  
Xiao Hong Liu ◽  
Wei She Zhang
Keyword(s):  
Optimization Model ◽  
Triangular Fuzzy Number ◽  
Customer Requirements ◽  
Configuration Optimization ◽  
Bp Network ◽  
Transaction Data ◽  
Product Function ◽  
Electric Bicycle ◽  
Data History ◽  
The Cost

The degree of satisfying the customer requirements was determined by the diversity of product function and form, in order to improve the satisfaction, the mapping relation between customer requirements and technical characteristics should be analyzed to construct configuration optimization model. This paper integrated triangular fuzzy number, BP network and GA to propose a method of constructing optimization model of product technical characteristics configuration corresponding to customer requirements, the model was established based on customer transaction data history. This method acquired the information of customer requirements and preference and that of purchased products’ function and form characteristic by analyzing and extracting customer transaction information, then applied BP network to express the mapping relations between customer requirements and product technical characteristics. The optimization model took the cost and time as constraint with the customer requirements as target, finally used genetic algorithm with MATLAB to solve. The electric bicycle was taken to illustrate the build process.

scholarly journals An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5718
Author(s):  
Kalim Ullah ◽  
Sajjad Ali ◽  
Taimoor Ahmad Khan ◽  
Imran Khan ◽  
Sadaqat Jan ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Carbon Emission ◽  
Renewable Energy Sources ◽  
Operating Cost ◽  
Energy Sources ◽  
Optimization Strategy ◽  
Model Based ◽  
Demand Response Programs ◽  
Operation Cost

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

scholarly journals Optimization and Experiment of Mass Compensation Strategy for Built-In Mechanical On-Line Dynamic Balancing System

2020 ◽  
Vol 10 (4) ◽  
pp. 1464
Author(s):  
Zhan Wang ◽  
Bo Zhang ◽  
Ke Zhang ◽  
Guodong Yue
Keyword(s):  
Residual Stress ◽  
Optimization Model ◽  
Dynamic Balance ◽  
Maximum Amplitude ◽  
Optimization Strategy ◽  
Compensation Strategy ◽  
Dynamic Balancing ◽  
Balance System ◽  
Before And After ◽  
On Line

In order to solve the problem of low precision and efficiency in the balancing process due to the movement of balance counterweights in a built-in mechanical on-line dynamic balance system, an optimization strategy for the mass compensation of the mechanical on-line dynamic balancing system is proposed, and a mass compensation optimization model is established. The optimization model takes the phase of counterweight movement as the optimization variable and the residual stress under dynamic balance as the optimization objective. Through the optimization model, the movement phase of the counterweight can be calculated, and the counterweight can be moved to a balanced position that significantly reduces the degree of unbalance. An experiment platform was built to carry out comparison experiments under different rotating speeds and unbalance levels. By comparing the residual stress, amplitude, and dynamic balancing time of the spindle before and after the balance, the accuracy of the phase of the counterweight that is calculated by the optimization model is verified. The optimized dynamic balance compensation strategy and the unoptimized were compared by experiments at different rotating speeds. The experimental results showed that, compared to the unoptimized balance, the amplitude of the spindle after optimizing balance with a dynamic balancing device can decrease by 30.39% on average, with its maximum amplitude decreasing by up 50.18%, and the balancing time can decrease by 31.72% on average, with its maximum balancing time decreasing by up to 43.86%. The research results showed that an optimization strategy can effectively improve dynamic balance efficiency and greatly reduce vibration amplitude, which provides the necessary theoretical basis for improving the running precision of the spindle system.

Optimal Configuration and Field Experiments for the Photovoltaic System of a Solar-Powered Hose-Drawn Traveler

2019 ◽  
Vol 62 (6) ◽  
pp. 1789-1801
Author(s):  
Dan Li ◽  
Delan Zhu ◽  
Maosheng Ge ◽  
Shoujun Wu ◽  
Ruixin Wang ◽  
...  
Keyword(s):  
Power Supply ◽  
Optimization Model ◽  
Annual Cost ◽  
Field Experiments ◽  
Transmission Efficiency ◽  
Optimal Configuration ◽  
Total Power ◽  
Generation System ◽  
Photovoltaic Generation ◽  
Battery Capacity

Abstract. High energy consumption is one of the disadvantages of hose-drawn travelers due to the use of water turbines. This study proposes a photovoltaic-powered electric motor instead of a water turbine to achieve high transmission efficiency. A stand-alone photovoltaic generation system (PVGS) was designed for a hose-drawn traveler. To achieve cost savings, a sizing optimization model was built for the PVGS. In the optimization model, the minimum annual cost of the system, which includes the initial capital, replacement, installation, operation, and maintenance costs, is taken as the objective function. The constraints include the battery’s state of charge (SOC) and the power supply reliability, which is composed of the load loss of power supply probability (LPSP) and the energy excess percentage (EXC). The total power produced by the PV panels and the total battery capacity are the decision variables. The optimization model of the PVGS is solved through a particle swarm optimization (PSO) algorithm based on a penalty function. The model is then applied to calculate the optimal configuration of a JP75-300 hose-drawn traveler. Comparisons between the optimal configuration and other six configuration schemes were conducted to verify the optimal solution results. Furthermore, field experiments were performed to test the performance. Finally, the effects of meteorological conditions, driving velocity, and LPSP on the optimal configuration and the annual cost of the PVGS are discussed. The results show that the optimal configuration of this PVGS are 432 W total power from PV panels and 172 Ah total battery capacity, and the optimization model results are the optimal configuration based on comparisons. The optimal configuration met the power requirements of the hose-drawn traveler for four days of field experiments, indicating that the optimal configuration is feasible.HighlightsA photovoltaic-powered electric motor instead of a water turbine was used for high transmission efficiency.An optimization model was built to define the optimal configuration of the photovoltaic generation system (PVGS).The optimal configuration decreased the annual cost of the PVGS while ensuring power supply reliability.Meteorological conditions, driving velocity, and LPSP are key factors affecting the annual cost of the PVGS. Keywords: Driving power requirements, Field experiments, Hose-drawn traveler, Optimization model, Particle swarm optimization, Photovoltaic generation system.

scholarly journals Superstructure-Based Optimization of Vapor Compression-Absorption Cascade Refrigeration Systems

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 428
Author(s):  
Sergio F. Mussati ◽  
Tatiana Morosuk ◽  
Miguel C. Mussati
Keyword(s):  
Heat Transfer ◽  
Optimization Model ◽  
Double Effect ◽  
Total Heat ◽  
Optimal Configuration ◽  
Vapor Compression ◽  
Heat Transfer Area ◽  
Refrigeration System ◽  
Cascade System ◽  
Total Heat Transfer

A system that combines a vapor compression refrigeration system (VCRS) with a vapor absorption refrigeration system (VARS) merges the advantages of both processes, resulting in a more cost-effective system. In such a cascade system, the electrical power for VCRS and the heat energy for VARS can be significantly reduced, resulting in a coefficient of performance (COP) value higher than the value of each system operating in standalone mode. A previously developed optimization model of a series flow double-effect H2O-LiBr VARS is extended to a superstructure-based optimization model to embed several possible configurations. This model is coupled to an R134a VCRS model. The problem consists in finding the optimal configuration of the cascade system and the sizes and operating conditions of all system components that minimize the total heat transfer area of the system, while satisfying given design specifications (evaporator temperature and refrigeration capacity of −17.0 °C and 50.0 kW, respectively), and using steam at 130 °C, by applying mathematical programming methods. The obtained configuration is different from those reported for combinations of double-effect H2O-LiBr VAR and VCR systems. The obtained optimal configuration is compared to the available data. The obtained total heat transfer area is around 7.3% smaller than that of the reference case.

scholarly journals An Optimal Allocation Strategy for Multienergy Networks Based on Double-Layer Nondominated Sorting Genetic Algorithms

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Min Mou ◽  
Da Lin ◽  
Yuhao Zhou ◽  
Wenguang Zheng ◽  
Jiongming Ruan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Double Layer ◽  
Optimization Model ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Optimization Strategy ◽  
Allocation Strategy ◽  
Photovoltaic Panel ◽  
Wind Power Generator ◽  
Nondominated Sorting

Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.

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