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Multi-objective dynamic VAR planning against fault-induced delayed voltage recovery using heuristic optimization

ELECTRICAL ENGINEERING 2024年第5期106卷 6281-6293页

作者： Bahramgiri, Maryam Ehsan, Mehdi Mozafari, S. Babak Islamic Azad Univ Dept Elect Engn Sci & Res Branch Tehran Iran Sharif Univ Technol Elect Engn Dept Tehran Iran

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm ii (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR.

关键词： Fault-induced delayed voltage recovery Short-term voltage instability Composite load model Trajectory sensitivities Bayesian regularized artificial neural network non-dominated sorting genetic algorithm ii

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A multiobjective study of the Gaussian cluster paging in the Reporting Cells strategy

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APPLIED SOFT COMPUTING 2015年 28卷 332-344页

作者： Berrocal-Plaza, Victor Vega-Rodriguez, Miguel A. Sanchez-Perez, Juan M. Univ Extremadura Dept Comp & Commun Technol Escuela Politecn Caceres 10003 Spain

Mobile location management is an important task in mobile networks. This paper presents an analysis of the Gaussian cluster paging in the Reporting Cells strategy. Reporting Cells define a location update strategy in which some network cells are configured as Reporting Cells with the aim of tracking the subscribers' movement. On the other hand, the Gaussian cluster paging is a paging scheme where we assume that the probability of finding a callee's terminal decreases as we move away from the last updated location. This paging scheme is studied in a multiobjective way for different probability thresholds, and considering time-delay constraints. With the goal of finding the best possible sets of non-dominated solutions, we propose our implementation of a well-known multiobjective evolutionary algorithm: the non-dominated sorting genetic algorithm ii. By means of an experimental study, we show that each probability threshold has its own non-dominated region in the objective space. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Mobile location management Reporting Cells scheme Gaussian paging Multiobjective evolutionary optimization non-dominated sorting genetic algorithm ii

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A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network

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ENGINEERING OPTIMIZATION 2013年第3期45卷 265-285页

作者： Lin, Yi-Kuei Yeh, Cheng-Ta Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan

Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating non-dominated sorting genetic algorithm ii and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.

关键词： stochastic-flow network multi-objective optimization non-dominated sorting genetic algorithm ii

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NSGAii-based Congestion Management in a Pool-based Electricity Market Incorporating Voltage and Transient Stability

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2013年第10期41卷 990-1001页

作者： Bhattacharjee, Tulika Chakraborty, Ajoy Kumar Natl Inst Technol Agartala Dept Elect Engn Jirania Tripura India

Congestion of transmission lines is a key challenge for the system operator in the open access regime and is usually managed by rescheduling generators and demands. The changes in schedule due to congestion management may cause stability margins to shrink to unacceptable levels. Stability issues must, therefore, be considered during congestion management. This article presents a multi-objective formulation of the congestion management problem in a pool-based electricity market with the competing objective functions of minimizing congestion management cost and maximizing voltage and transient stability margins. The non-dominated sorting genetic algorithm ii is employed to achieve the best trade off among the three objectives. A fuzzy decision maker is used to extract the best-compromise non-dominated solution. The proposed method has been tested on the New England 39-bus system, and results are compared with those of other reported multi-objective mathematical programming based approaches. The proposed non-dominated sorting genetic algorithm ii based congestion management algorithm outperforms the previously reported method. The quality of the results establishes the efficacy of the proposed approach in solving the multi-objective congestion management problem.

关键词： congestion management loading margin multi-objective non-dominated sorting genetic algorithm ii

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Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair

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ISA TRANSACTIONS 2013年第1期52卷 129-139页

作者： Saadatzi, Mohammad Nasser Poshtan, Javad Saadatzi, Mohammad Sadegh Tafazzoli, Faezeh Iran Univ Sci & Technol Tehran Iran Multimedia Univ Melaka Malaysia Polytech Univ Technol Tehran Iran

Electric wheelchair (EW) is subject to diverse types of terrains and slopes, but also to occupants of various weights, which causes the EW to suffer from highly perturbed dynamics. A precise multivariable dynamics of the EW is obtained using Lagrange equations of motion which models effects of slopes as output-additive disturbances. A static pre-compensator is analytically devised which considerably decouples the EW's dynamics and also brings about a more accurate identification of the EW. The controller is designed with a disturbance-observer (DOB) two-degree-of-freedom architecture, which reduces sensitivity to the model uncertainties while enhancing rejection of the disturbances. Upon disturbance rejection, noise reduction, and robust stability of the control system, three fitness functions are presented by which the DOB is tuned using a multi-objective optimization (MOO) approach namely non-dominated sorting genetic algorithm-ii (NSGA-ii). Finally, experimental results show desirable performance and robust stability of the proposed algorithm. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Electric wheelchair Dynamic modeling System identification Robust disturbance observer Characteristic loci method non-dominated sorting genetic algorithm ii Multi-objective-optimization

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Ground Motion Record Selection Using Multi-objective Optimization algorithms: A Comparative Study

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2019年第3期63卷 812-822页

作者： Kaveh, Ali Moghanni, Roya Mahdipour Javadi, Seyed Mohammad Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran *** Iran

Performing time history dynamic analysis using site-specific ground motion records according to the increasing interest in the performance-based earthquake engineering has encouraged studies related to site-specific Ground Motion Record (GMR) selection methods. This study addresses a ground motion record selection approach based on three different multi-objective optimization algorithms including Multi-Objective Particle Swarm Optimization (MOPSO), non-dominated sorting genetic algorithm ii (NSGA-ii) and Pareto Envelope-based Selection algorithm ii (PESA-ii). The method proposed in this paper selects records efficiently by matching dispersion and mean spectrum of the selected record set and target spectrums in a predefined period. Comparison between the results shows that NSGA ii performs better than the other algorithms in the case of GMR selection.

关键词： ground motion record selection multi-objective particle swarm optimization non-dominated sorting genetic algorithm ii pareto envelope-based selection algorithm

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An integrated production-distribution planning with a routing problem and transportation cost discount in a supply chain

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ASSEMBLY AUTOMATION 2019年第5期39卷 783-802页

作者： Karimi, Behzad Ghare Hassanlu, Mahsa Niknamfar, Amir Hossein Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran

Purpose The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today's global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model. Design/methodology/approach As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm ii (NSGAii) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances. Findings The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions. Originality/value The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by

关键词： Outsourcing Supply chain AHP-TOPSIS Integrated production-distribution planning Multi-objective imperialist competitive algorithm non-dominated sorting genetic algorithm ii

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Optimization of an Organic Rankine Cycle-Vapor Compression Cycle System for Electricity and Cooling Production from Low-Grade Waste Heat

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ENERGIES 2024年第22期17卷 5566页

作者： Witanowski, Lukasz Polish Acad Sci Inst Fluid Flow Machinery Turbine Dept PL-80231 Gdansk Poland

In light of the intensifying global climate crisis and the increasing demand for efficient electricity and cooling systems, the exploration of advanced power generation technologies has become crucial. This paper presents a comprehensive analysis of Organic Rankine Cycle-Vapor Compression Cycle (ORC-VCC) systems utilizing low-grade waste heat for the dual purpose of electricity and cooling production. The study focuses on systems that harness waste heat below 90 degrees C with thermal inputs up to 500 kW. An in-house Python code was developed to calculate cycle parameters and perform multi-objective optimization targeting the maximization of both ORC-VCC efficiency and power output. The optimization was conducted for 10 different cases by evaluating five working fluids across two different ambient temperatures. The analysis reveals that the optimized system achieved an impressive overall cycle efficiency exceeding 90%, demonstrating the significant potential of ORC-VCC technology in waste heat recovery applications. The non-dominated sorting genetic algorithm ii (NSGA-ii) multi-objective optimization approach was found to be particularly effective at navigating the multi-dimensional solution space and identifying the global optimum. This study provides valuable insights into system performance across a range of operating conditions and design parameters. Sensitivity analyses highlight key factors influencing cycle efficiency and power output. These findings have important implications for the development and deployment of ORC-VCC systems as a sustainable and efficient solution to meet growing energy needs while reducing greenhouse gas emissions.

关键词： Organic Rankine Cycle Vapor Compression Cycle waste heat multi-objective optimization non-dominated sorting genetic algorithm ii

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Multi-objective optimal operation of a large deep reservoir during storage period considering the outflow-temperature demand based on NSGA-ii

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JOURNAL OF HYDROLOGY 2020年 586卷 124919-124919页

作者： He, Wei Ma, Chao Zhang, Jian Lian, Jijian Wang, Shan Zhao, Wenlong Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

Thermal stratification occurs in deep reservoirs and causes an outflow temperature that is significantly different from the inflow temperature, which is harmful to the ecology of downstream reaches. Optimal operation of reservoirs using the multi-objective evolutionary algorithm (MOEA), which focuses on power generation, flood control, agricultural irrigation, navigation, etc., has been performed. However, the outflow temperature has not been considered. Focusing on this deficiency, we considered Sanbanxi Reservoir as a case study and used the non-dominated sorting genetic algorithm ii (NSGA-ii) by combining it with the calibrated 1-D General Lake Model. The trade-off relationships among three objectives, namely, total power generation (TPG), root-meansquare deviation between the inflow and outflow temperatures (RMSDtem) and weighted aggregate eco-index (EI) based on indicators of hydrologic alteration (IHAs) parameters, were estimated as Pareto frontiers during the storage period from March 15 to July 15. The results demonstrated the following. 1) Through reservoir reoperation, RMSDtem could be reduced by 1.60 degrees C from 5.99 to 4.39 degrees C at the expense of reducing TPG from 7.43 x 10(8) to 6.56 x 10(8) *** (-11.8%) during a normal year. 2) The optimized operating schemes were extracted as Pareto frontiers, and the slope of the outflow discharge curve (SODC) could be a useful parameter for determining the operating scheme in practice. A large SODC yielded high TPG, a small SODC resulted in low RMSDtem, and a medium SODC yielded minimum EL The medium SODCs ranged from -0.1 to 0.4 m(3) /(s.d), -0.4 to 0.6 m(3)/(s.d), and -0.6 to 1.2 m(3)/(s.d) for dry, normal, and wet years, respectively. 3) As the yearly inflow increased, the absolute value of the SODC for the best individuals for a single objective became larger. The range of TPG and RMSDtem in the Pareto frontier expanded, and that of EI remained basically unchanged. The results of the Sanbanxi Reservoir

关键词： Multi-objective optimal operation non-dominated sorting genetic algorithm ii Total power generation Root-mean-square deviation between the inflow and outflow temperatures Weighted aggregate eco-index based on IHA parameters Slope of the outflow discharge curve

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Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2022年第2期236卷 277-289页

作者： Ma, Chenyang Wang, Wei Cai, Zhiqiang Zhao, Jiangbin Northwestern Polytech Univ Sch Mech Engn Dept Ind Engn Xian 710072 Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Ind Engn & Intelligent Mfg Xian Peoples R China

Reconfigurable systems can meet the changing requirements of system performance by several approaches, such as adjusting the system structure, improving the component performance, and reassigning components. However, it is also challengeable to find a cost-effective maintenance scheme by integrating these maintenance approaches. This article investigates the multi-objective maintenance optimization problem for reconfigurable systems with the consideration of maintenance cost and system reliability. First, the multi-objective maintenance optimization model is established to maximize the system reliability and minimize the total maintenance cost considering the constraints on budget and system performance. Second, a multi-objective Birnbaum importance is proposed to quantify the contribution of the individual component to the system reliability. The multi-objective Birnbaum importance-based non-dominated sorting genetic algorithm ii is developed to obtain the optimal maintenance scheme with the maximum system reliability and minimum maintenance cost. Finally, the performance of multi-objective Birnbaum importance-based non-dominated sorting genetic algorithm ii is proved by three numerical experiments. Experiment 1 verifies the advantage of multi-objective Birnbaum importance compared with Birnbaum importance to improve the system reliability in direct maintenance. Experiment 2 shows that the effectiveness of multi-objective Birnbaum importance is much better than that of the Birnbaum importance to enhance the performance of non-dominated sorting genetic algorithm ii in comprehensive maintenance. Experiment 3 illustrates that the performance of multi-objective Birnbaum importance-based non-dominated sorting genetic algorithm ii is better than that of other multi-objective algorithms combining with multi-objective Birnbaum importance.

关键词： Reconfigurable systems cost-effective maintenance multi-objective optimization Birnbaum importance non-dominated sorting genetic algorithm ii

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