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Financial risk constrained remote controlled switch deployment in distribution networks

IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第7期12卷 1547-1553页

作者： Izadi, Milad Safdarian, Amir Sharif Univ Technol Dept Elect Engn Ctr Excellence Power Syst Control & Management Tehran Iran

Recently, diverse technologies and apparatus are launched to enhance service reliability in the distribution level. In this regard, remote controlled switches (RCSs) can play a prominent role in declining interruption durations and costs. The stochastic nature of contingencies, however, imposes considerable uncertainty on the achievements of RCS deployment. This study presents a model to consider the financial risk induced by the uncertainty in an RCS placement problem. The risk awareness of distribution companies is scrutinised through an effective risk measure, i.e. conditional value at risk. In the model, the number and location of RCSs are determined such that the system expected cost is minimised and the risk is managed. The model is treated as a multi-objective problem with two conflicting objectives. The problem is then solved via a non-dominated sorting genetic algorithm ii. This algorithm results in a set of non-dominated solutions. Finally, a fuzzy decision making method is applied to select the best placement strategy from the set. The presented model is applied on RBTS-Bus4, where the findings reveal the importance of considering the risk in an RCS placement problem as well as the capability of the method in doing so.

关键词： power distribution reliability risk management switches power distribution economics stochastic processes electricity supply industry minimisation genetic algorithms fuzzy set theory decision making financial risk constrained remote controlled switch deployment distribution network service reliability enhancement contingency stochastic nature RCS deployment RCS placement problem distribution companies risk awareness system expected cost minimisation risk management multiobjective problem nondominated sorting genetic algorithm ii fuzzy decision making method best placement strategy selection RBTS-Bus4

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MS-guided many-objective evolutionary optimisation for test suite minimisation

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IET SOFTWARE 2018年第6期12卷 547-554页

作者： Zheng, Wei Wu, Xiaoxue Cao, Shichao Lin, Jun Northwestern Polytech Univ Sch Software & Microelect 127 West Youyi Rd Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Automat 127 West Youyi Rd Xian Shaanxi Peoples R China

Test suite minimisation is a process that seeks to identify and then eliminate the obsolete or redundant test cases from the test suite. It is a trade-off between cost and other value criteria and is appropriate to be described as a many-objective optimisation problem. This study introduces a mutation score (MS)-guided many-objective optimisation approach, which prioritises the fault detection ability of test cases and takes MS, cost and three standard code coverage criteria as objectives for the test suite minimisation process. They use six classical evolutionary many-objective optimisation algorithms to identify efficient test suite, and select three small programs from the Software-Artefact Infrastructure Repository (SIR) and two larger program space and gzip for experimental evaluation as well as statistical analysis. The experiment results of the three small programs show non-dominated sorting genetic algorithm ii (NSGA-ii) with tuning was the most effective approach. However, MOEA/D-PBI and MOEA/D-WS outperform NSGA-ii in the cases of two large programs. On the other hand, the test cost of the optimal test suite obtained by their proposed MS-guided many-objective optimisation approach is much lower than the one without it in most situation for both small programs and large programs.

关键词： statistical analysis Pareto optimisation minimisation genetic algorithms program testing statistical analysis nondominated sorting genetic algorithm ii program space software-artefact infrastructure repository MS-guided many-objective optimisation approach optimal test suite test cost NSGA-ii test suite minimisation process standard code coverage criteria mutation score-guided many-objective optimisation approach many-objective optimisation problem redundant test cases obsolete test cases MS-guided many-objective evolutionary optimisation

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Resilience-Oriented Optimal Operation Strategy of Active Distribution Network

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ENERGIES 2019年第17期12卷 3380页

作者： Wang, Jun Zheng, Xiaodong Tai, Nengling Wei, Wei Li, Lingfang Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China China State Shipbldg Corp Ltd Inst 708 Shanghai 200011 Peoples R China Yunnan Power Grid Corp Kunming 650000 Yunnan Peoples R China

The ability to withstand extreme disasters has a profound impact on the distribution network operation. This paper proposes a novel optimal operation strategy for an active distribution network to enhance system resilience.. The objectives in the proposed optimal strategy include, the resilience, operation cost, and its pollutant emissions. According to the existence of uncontrollable distributed energy resources in the active distribution network, the problem which takes the uncertainty most into account, is this multi-objective optimization problem. Thus, it can be treated as a min-max dual robust optimization problem. Benders decomposition is employed to decouple the problem, then non-dominated sorting genetic algorithm ii is applied to search the multi-objective optimal solution which has an extremely low CPU time. The modified standard IEEE 34-node system, with different distributed energy resources types, is employed, as a studied case, to demonstrate the effectiveness of the proposed optimal operation strategy. The simulation results illustrate that, compared to other economic-oriented robust optimal operation models, the proposed strategy can enhance system resilience without a significant increase in the operation cost and pollutant emissions.

关键词： resilience-oriented active distribution network robust optimal operation strategy nondominated sorting genetic algorithm ii

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Optimal Design of a New Compliant XY Micropositioning Stage for Nanoindentation Tester Using Efficient Approach of Taguchi Method, Response Surface Method and NSGA-ii 4

Optimal Design of a New Compliant XY Micropositioning Stage ...

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4th International Conference on Green Technology and Sustainable Development (GTSD)

作者： Minh Phung Dang Thanh-Phong Dao Hieu Giang Le Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781538651261

A new compliant XY micropositioning stage is designed for locating a material sample during nanoindentation tester. This paper aims to design and optimize the proposed stage. The working travel of proposed stage is amplified via using the four-lever amplification mechanism. To improve the performances of the stage, the geometric parameters are optimized by an integration method of Taguchi method (TM), response surface method (RSM) and nondominated sorting genetic algorithm ii (NSGA-ii). Firstly, the number of experiment of four design variables were created by the TM. Subsequently, the output displacement and the first natural frequency are retrieved by finite element analysis (FEA). Then, the mathematical equations between four design variables and both responses is established by using the RSM. Eventually, based on mathematical equations, the NSGA-ii is adopted to determine the optimal parameters. The results indicated that the optimized y-axis displacement was about 3.862 mm and the first natural frequency was approximately 45.983. The results of FEA validations are in a good agreement with the predicted results from the proposed approach. This result indicated that the integrated proposed approach ensures a high reliability for engineering optimization problems.

关键词： Compliant mechanism XY micropositioning stage Taguchi method Response surface method nondominated sorting genetic algorithm ii

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A Parametric Evaluation Approach Based on SFA for System Reliability Redundancy Allocation Plan

A Parametric Evaluation Approach Based on SFA for System Rel...

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Annual Reliability and Maintainability Symposium (RAMS)

作者： Xiao, Boping Pan, Fenglian Beihang Univ Sch Reliabil & Syst Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

ISBN: (纸本)9781538628690

The significance of this paper is to propose a new method for evaluating the efficiency of reliability redundant allocation. This method considers the random interference factor and the non-efficiency of the redundant allocation process, which makes the results more reasonable and reliable. However, in this paper, the model only considers the serial system, the parallel system and series - parallel system is not considered. Therefore, in the future work, we can extend the applicability of the model.

关键词： Redundancy Allocation optimization nondominated sorting genetic algorithm ii Stochastic Frontier Analysis

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Stochastic Disassembly Sequence Optimization for Profit and Energy Consumption

Stochastic Disassembly Sequence Optimization for Profit and ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Fu, Yaping Zhou, MengChu Guo, Xiwang Qi, Liang Qingdao Univ Sch Business Qingdao Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Liaoning Shihua Univ Coll Comp & Commun Engn Fushun Peoples R China Shandong Univ Sci & Technol Dept Comp Sci & Tech Qingdao Peoples R China

ISBN: (纸本)9781538666500

Industrial products' reuse, recovery and recycling are very important because of their environmental and economic benefits. Effective disassembly sequencing can improve recovery revenue and reduce environment impact. In this work, a stochastic dual-objective disassembly sequencing problem is establishers which includes maximizing disassembly profit and minimizing energy consumption. Two popular and classical multi-objective evolutionary algorithms, i.e., nondominated sorting genetic algorithm ii and multi-objective evolutionary algorithm based on decomposition, are used to deal with this problem. By conducting simulation experiments on several numerical cases and analyzing experimental results with wellknown performance metrics, i.e., inverted generational distance and hypervolume, this work concludes that both can be used to obtain highly desired solutions.

关键词： Disassembly sequence optimization nondominated sorting genetic algorithm ii multi-objective evolutionary algorithm based on decomposition energy consumption optimization

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Multiobjective Model For The Simultaneous Optimization Of Transportation Costs, Inventory Costs And Service Level In Goods Distribution

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IEEE LATIN AMERICA TRANSACTIONS 2017年第1期15卷 129-136页

作者： Arango, M. D. Zapata, J. A. Univ Nacl Colombia Sede Medellin Medellin Colombia

This paper presents a model formulation for the multiobjetive optimization of transportation costs, inventory costs and service level for the goods distribution process. The multiobjective model is composed by two objective functions, in which the transportation and inventory cost are optimized using the Inventory Routing Problem (IRP) and the service level is optimized using the number of accomplished time windows for the vehicles in the routing process. The model is solved using the nondominated sorting genetic algorithm ii (NSGAii), implementing a novel representation of the chromosome, which allows assigning inventory and routes simultaneously. The algorithm allows calculating the Pareto Frontier for the analyzed distribution case, observing good qualities in the solution, compared with what is found in scientific literature.

关键词： Multiobjective optimization nondominated sorting genetic algorithm ii Inventory Routing Problem Service level Costs of transport Costs of inventory Distribution of Goods

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Design considerations of superconducting fault current limiters for power system stability enhancement

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第9期11卷 2155-2163页

作者： Alaraifi, Surour El Moursi, Mohamed Shawky Masdar Inst Sci & Technol Elect Engn & Comp Sci Dept POB 54224 Abu Dhabi U Arab Emirates

This study identifies the type and size of the optimal shunt limiting impedance for superconducting fault current limiter (SFCL). Since the shunt impedance has been widely categorised to be either inductive or resistive element depending on the type of limiting impedance, the proposed design considerations will significantly affect the transient response and system stability. Therefore, the presented problem formulation optimises the design of SFCL by allowing the limiting shunt impedance to combine both resistive and inductive elements with an optimal ratio to tackle the security and stability challenges while functioning the SFCL. The optimisation framework is developed based on time-domain simulation and takes into account the transient stability indices to size the limiting components of SFCLs given that the main operational requirements such as fault current level and voltage limits are met. Non-dominated sorting genetic algorithm ii is used to find the Pareto fronts for different types of SFCLs. The optimisation and time-domain simulation results demonstrate great enhancement in the performance of both SFCLs when the optimal ratio between the resistive and inductive elements is chosen for the shunt limiting impedance resulted in improving the transient response and system stability.

关键词： superconducting fault current limiters power system transient stability transient response time-domain analysis genetic algorithms Pareto analysis superconducting fault current limiter power system stability enhancement optimal shunt limiting impedance SFCL resistive element inductive element transient response time-domain simulation transient stability indices fault current level voltage limits nondominated sorting genetic algorithm ii Pareto fronts

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Multiobjective optimization for collision avoidance maneuver using a genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2016年第8期230卷 1438-U13页

作者： Seong, Jae-Dong Kim, Hae-Dong Korea Univ Sci & Technol UST Dept Satellite Syst & Applicat Daejeon 305350 South Korea KARI Daejeon South Korea

In this paper, we analyze an evolutionary multiobjective heuristic algorithm for collision avoidance maneuver optimization when multiple objects in space threaten a satellite. Population-based evolutionary multiobjective heuristic algorithms can stably find optimal or suboptimal solutions on account of their robustness and flexibility. We present a collision avoidance maneuver optimization planning method using the nondominated sorting genetic algorithm ii. Our proposed method simultaneously optimizes two goals: the minimization of DeltaV consumption and maximization of the satellite maneuver cycle length. Through an experimental evaluation, we demonstrate the efficient performance of our method in terms of DeltaV consumption and maneuver cycle length under constraints, including allowable DeltaV and burn start time, with consideration of a safety threshold between the user satellite and multiple threatening objects. We perform the simulation once per each burn strategy, i.e. in an in-track direction with a single burn;in three radial, in-track, cross-track direction with a single burn;and three radial, in-track, cross-track directions with two burns. We obtain various solutions, all of which satisfy the safety threshold. For the in-track direction with the single burn strategy, the solution showed merit in the minimization of DeltaV consumption. On the other hand, the results of three radial, in-track, cross-track directions with two burns showed good performance in maximization of the maneuver cycle. We therefore contend that efficient collision avoidance maneuver optimization planning can be achieved by using a multiobjective heuristic algorithm.

关键词： Space debris avoidance maneuver metaheuristic multiobjective optimization nondominated sorting genetic algorithm ii

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Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-ii algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2016年第2期15卷 423-451页

作者： Yu, Lean Yang, Zebin Tang, Ling Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered;and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm ii (NSGA-ii), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.

关键词： Oil supply chain purchasing and distribution multi-objective optimization nondominated sorting genetic algorithm ii extreme learning machine

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