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A novel multi objective Loading Pattern Optimization by gravitational search algorithm (GSA) for WWER1.000 core

PROGRESS IN NUCLEAR ENERGY 2016年 93卷 1-11页

作者： Aghaie, M. Mahmoudi, S. M. Shahid Beheshti Univ GC Dept Engn POB *** Tehran Iran Grad Univ Adv Technol Fac Elect & Comp Engn Kerman Iran

In this paper, a novel multi objective optimization algorithm, gravitational search algorithm (GSA), is developed in order to implement in the Loading Pattern Optimization (LPO) of a nuclear reactor core. In recent decades several metaheuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. Regarding the coupled behavior of Neutronic and Thermal-Hydraulic (NTH) dynamics in a nuclear reactor core, proper loading pattern of fuel assemblies (FAs) depends on NTH aspects, simultaneously. Thus, obtaining optimal arrangement of FAs, in a core to meet special objective functions is a complex problem. gravitational search algorithm (GSA) is constructed based on the law of Gravity and the notion of mass interactions, using the theory of Newtonian physics and searcher agents are the collection of masses. In this work, for multi objective optimization, the NTH aspects are included in fitness function. Neutronic goals include increasing multiplication factor (Kell), decreasing of power picking factor (PPF) and flattening of the power density, also thermal-hydraulic (TH) goals include increasing critical heat flux (CHF) and decreasing average of fuel centers temperature. Therefore, at the first step, GSA method is compared with other metaheuristic algorithms on Shekel's Foxholes problem. In the second step for finding the best core pattern and implementation of the objectives listed, GSA algorithm has been performed for case of WWER1000 reactor. For the NTH calculations, PARCS (Purdue advanced reactor core simulator) and COBRA-EN codes are implemented, respectively. The results demonstrate that GSA algorithm have promising performance and can propose for other optimization problems of nuclear engineering field. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Loading Pattern Optimization gravitational search algorithm Neutronic Thermal-hydraulic WWER1000 PARCS COBRA-EN

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Optimal SSSC design for damping power systems oscillations via gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第Nov.期82卷 161-168页

作者： Abd Elazim, S. M. Ali, E. S. Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm namely gravitational search algorithm (GSA) based on the law of gravity and mass interactions is illustrated for designing Static Synchronous Series Compensator (SSSC) for single and multimachine power systems. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in tuning SSSC compared with Bacteria Foraging (BF) and Genetic algorithm (GA). Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power systems stability over a wide range of loading conditions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： SSSC gravitational search algorithm Bacteria foraging Genetic algorithm Damping oscillations Power systems

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Loadability enhancement with FACTS devices using gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第Jun.期78卷 470-479页

作者： Bhattacharyya, Biplab Kumar, Sanjay Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： FACTS devices Active power loss Operating cost Loadability gravitational search algorithm

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Hybridized gravitational search algorithm for Short-Term Hydrothermal Scheduling

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IETE JOURNAL OF REsearch 2016年第4期62卷 468-478页

作者： Gouthamkumar, N. Sharma, Veena Naresh, R. Natl Inst Technol Dept Elect Engn Hamirpur Himachal Prades India

This paper presents a new approach for solving the short-term hydrothermal scheduling (STHTS) problem using a disruption operator in an oppositional gravitational search algorithm. The nonlinear and non-convex nature of the STHTS problem coupled with the cascading nature of reservoirs, water transport delays and scheduling time linkages makes the solution of this optimization problem quite difficult using the conventional optimization methods. Here, an opposition-based learning concept is introduced in a gravitational search algorithm to improve the quality of the current population towards global optimal solutions and a disruption operator is integrated to accelerate the convergence of solutions. This method is evaluated on two test systems consisting of four hydro and an equivalent thermal plant and four hydro and three thermal plants. The detailed statistical results prove that the proposed approach performs better in terms of production cost and smooth convergence characteristics when compared with other recently reported methods in the literature.

关键词： Disruption operator gravitational search algorithm oppositional-based learning short-term hydrothermal scheduling

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Structural damage identification using gravitational search algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2016年第4期60卷 729-747页

作者： Liu, J. K. Wei, Z. T. Lu, Z. R. Ou, Y. J. Sun Yat Sen Univ Dept Appl Mech Guangzhou 510006 Guangdong Peoples R China Jinan Univ Dept Mech & Civil Engn Guangzhou 510632 Guangdong Peoples R China

This study aims to present a novel optimization algorithm known as gravitational search algorithm (GSA) for structural damage detection. An objective function for damage detection is established based on structural vibration data in frequency domain, i. e., natural frequencies and mode shapes. The feasibility and efficiency of the GSA are testified on three different structures, i. e., a beam, a truss and a plate. Results show that the proposed strategy is efficient for determining the locations and the extents of structural damages using the first several modal data of the structure. Multiple damages cases in different types of structures are studied and good identification results can be obtained. The effect of measurement noise on the identification results is investigated.

关键词： damage identification gravitational search algorithm vibration data frequency domain modal assurance criteria

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The Application of Artificial Bee Colony and gravitational search algorithm in Reservoir Optimization

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WATER RESOURCES MANAGEMENT 2016年第7期30卷 2497-2516页

作者： Ahmad, Asmadi Razali, Siti Fatin Mohd Mohamed, Zawawi Samba El-shafie, Ahmed Natl Univ Malaysia Bangi Malaysia Univ Malaya Kuala Lumpur Malaysia

This paper presented the application of Artificial Bee Colony (ABC) and gravitational search algorithm (GSA) in reservoir optimization. ABC is an algorithm based on the foraging behaviour of bee while GSA imitates the gravitational processes. These algorithms were used to minimize the irrigation release deficit for Timah Tasoh Dam located at the Northern part of Peninsular Malaysia. Results proved the superiority of the ABC compared to GSA with regards to faster convergence rate, stability, higher reliability and lower vulnerability indexes, while GSA is better in the resiliency indicator measure. Finally, both algorithms can be used to solve reservoir optimization problem with their own unique capability and to improve the performance of the reservoir compared to the existing reservoir standard operation procedure.

关键词： Reservoir optimization Artificial bee colony gravitational search algorithm Reservoir performance measure

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gravitational search algorithm and Its Variants

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2016年第8期30卷 1639001-1639001页

作者： Siddique, Nazmul Adeli, Hojjat Univ Ulster Sch Comp & Intelligent Syst Northland Rd Londonderry Co BT48 7JL Londonderry North Ireland Ohio State Univ Dept Biomed Engn 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Civil 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Environm 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Geodet Engn 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Neurosci 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Neurol 470 Hitchcock Hall2070 Neil Ave Columbus OH 43210 USA

gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best fitness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a specified number of iterations after which the best fitness becomes the global fitness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.

关键词： gravitational search algorithm nature-inspired computing gravitational kinematics metaheuristic algorithm

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Optimal Rescheduling of Real Power to Mitigate Congestion with Incorporation of Wind Farm Using gravitational search algorithm in Deregulated Environment

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY REsearch 2017年第4期7卷 1731-1740页

作者： Paul, Kaushik Kumar, Niranjan Agrawal, S. NIT Jamshedpur Elect & Elect Engn Adityapur 831014 India

In deregulated power system environment, the congestion is considered as one of the vital issues concerning the system's security and reliability. The Independent System Operator (ISO) bears the task to manage the congestion in the open access electricity market. This article puts forward an efficient Congestion Management (CM) technique with the embodiment of wind farm as a renewable resource alongside the implementation of an efficient and reliable meta-heuristic technique. The proposed CM approach is established contemplating the Bus Sensitivity Factor (BSF) and the Generator Sensitivity Factors (GSF). The positioning of the wind farm is optimally achieved considering the BSF. The GSF values are computed to sort out the most sensitive generators for participating in the CM problem. The gravitational search algorithm (GSA) is introduced in order to optimally minimize the active power yield of the generators taking part in the process of CM. The GSA is one of the latest meta-heuristic algorithms based on the Newton's Laws of gravitational forces. The result obtained by GSA is contrasted with the outcomes reported in the past literatures. Modified 39-bus New England system is considered for the implementation of the potency of the proposed approach of CM with the inclusion of wind farm as a renewable resouce.

关键词： Congestion Management gravitational search algorithm Optimization Wind farm Renewable Resource

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Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 177-191页

作者： Li, Chaoshun Chang, Li Huang, Zhengjun Liu, Yi Zhang, Nan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

The hydraulic turbine governing system (HTGS) is a crucial control system of hydroelectric generating units (HGUs). Parameter identification of HTGS is an important issue for the modeling and control of HGUs. The parameter identification problem of HTGS is more difficult if the elastic water hammer model is considered in the system, and existing algorithms are not effective to solve it. To solve this new problem, a modified gravitational search algorithm (MGSA) has been proposed in which modifications have been made to improve the performance of the GSA from two aspects. First, the constant attenuation factor is replaced by a hyperbolic function to generate a better gravitational constant to balance the global exploration and local exploitation during different searching stages. Second, agent mutation is introduced to increase the diversity of agents and to strengthen the ability to jump out of the local minima of the GSA. The performance of the MGSA has been verified by 13 typical benchmark problems, and the experimental results and statistical analysis demonstrate that the proposed MGSA significantly outperforms the standard GSA and some other popular optimization algorithms. The MGSA is then employed in the parameter identification of a nonlinear model of HTGS with an elastic water hammer, and the experimental results indicate that MGSA locates more precise parameter values than the compared methods. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Parameter identification Hydraulic turbine governing system Elastic water hammer gravitational search algorithm Modified gravitational search algorithm

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Long-term load forecasting based on gravitational search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016年第6期30卷 3633-3643页

作者： Abdi, Hamdi Beigvand, Soheil Derafshi Razi Univ Fac Engn Dept Elect Engn Kermanshah *** Iran

This paper presents a novel Long-Term Load Forecasting (LTLF) technique based on the new heuristic method, namely gravitational search algorithm (GSA). The objective of the suggested approach is establishing a more accurate LTLF model to minimize the average error of modeling. In order to estimate different fitting functions based on the proposed algorithm, two different case studies include Egyptian and Kuwaiti grids are selected. Also, the results are compared with a conventional approach, namely Least Squares (LS) method, and Particle Swarm Optimization (PSO) as a heuristic algorithm, to select the best LF model. Finally, based on the average and maximum errors arise from the estimations as a decision condition;the best function is selected for the LTLF problem.

关键词： Energy forecasting demand forecasting long-term forecasting electricity regression gravitational search algorithm

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