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gravity search algorithm hybridized Recursive Least Square method for power system harmonic estimation

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2017年第3期20卷 874-884页

作者： Singh, Santosh Kumar Kumari, Deepika Sinha, Nilotpal Goswami, Arup Kumar Sinha, Nidul Rajeev Gandhi Mem Coll Engn & Technol Elect & Elect Engn Dept Nandyal Andhra Pradesh India Natl Cheng Kung Univ Elect & Comp Engn Dept Tainan Taiwan Natl Inst Technol Dept Elect Engn Silchar Assam India

This paper presents a new hybrid method based on gravity search algorithm (GSA) and Recursive Least Square (RLS), known as GSA-RLS, to solve the harmonic estimation problems in the case of time varying power signals in presence of different noises. GSA is based on the Newton's law of gravity and mass interactions. In the proposed method, the searcher agents are a collection of masses that interact with each other using Newton's laws of gravity and motion. The basic GSA algorithm strategy is combined with RLS algorithm sequentially in an adaptive way to update the unknown parameters (weights) of the harmonic signal. Simulation and practical validation are made with the experimentation of the proposed algorithm with real time data obtained from a heavy paper industry. A comparative performance of the proposed algorithm is evaluated with other recently reported algorithms like, Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Optimization (BFO), Fuzzy-BFO (F-BFO) hybridized with Least Square (LS) and BFO hybridized with RLS algorithm, which reveals that the proposed GSA-RLS algorithm is the best in terms of accuracy, convergence and computational time. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

关键词： gravity search algorithm Recursive Least Square Harmonic pollution Power quality

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An efficient methodology for optimal reconfiguration of electric distribution network considering reliability indices via binary particle swarm gravity search algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第9期30卷 2843-2858页

作者： Fathy, Ahmed El-Arini, Mahdi El-Baksawy, Osama Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt

One important aspect that should be achieved during the operation of the distribution network is minimizing the total active loss. This objective can be achieved by network reconfiguration in which switching events such as closing tie switches or opening sectionalizing switches are efficiently determined. This paper presents a reliable meta-heuristic algorithm for optimal reconfiguration of the distribution network which is binary particle swarm optimization gravity search algorithm (BPSOGSA). The methodology is applied on four test systems: 16-bus system, 33-bus system, 69-bus system and 119-bus system. Reliability indices, system average interruption frequency, system average interruption duration and energy not supplied, are incorporated to check the validity of the network after reconfiguration process. Comparison with other reported previous methods is performed;the power loss is reduced by 9.3242% in 16-bus system, for 33-bus system;the power loss is reduced by 31.46%. In case of 69-bus system, the power loss is reduced by 56.1761%, while for 119-bus, the power loss is reduced by 33.7216%. Additionally, the performance of the proposed BPSOGSA is the best one compared with the others for all studied cases. The obtained results prove the reliability of the proposed methodology.

关键词： Network optimal reconfiguration Binary particle swarm optimization gravity search algorithm

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Application of the gravity search algorithm to multi-reservoir operation optimization

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ADVANCES IN WATER RESOURCES 2016年第Dec.期98卷 173-185页

作者： Bozorg-Haddad, Omid Janbaz, Mahdieh Loaiciga, Hugo A. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran Iran Univ Tehran Dept Irrigat & Reclamat Engn Fac Agr Engn & Technol Coll Agr & Nat Resources Tehran Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA

Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： gravity search algorithm Optimization Multi-reservoir operation

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Optimization of Database Resources Assignment in Cloud Computing Using gravity search algorithm 1

Optimization of Database Resources Assignment in Cloud Compu...

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1st International Conference on Advanced Research in Engineering Sciences (ARES)

作者： Tahmassebpour, Mahmoudreza Tehran Iran

ISBN: (纸本)9781538648445

Cloud computing of the computational model is based on the great computer networks such as the internet;these networks propose a new pattern for providing, consuming, and delivering the Information Technology (including hardware, software, information, and the other common computational resources) by using internet. Cloud computing suggests guidelines to provide Information Technology services in similar ways such as public industries (water, electricity, telephone, etc.). This means that access to Information Technology at the time of demand and based on the user's demand level is provided for the users in a flexible and scalable way. On the other hand, the optimal resources in the cloud computing systems are considered from the fundamental challenges. Due to its large search space, this issue is from the difficult issues and the random search methods are more used for solving it. So far, different algorithms have been proposed for solving this problem. In this paper, for solving the resource assignment problem in cloud computing, we have used a meta-heuristic algorithm called GSA-RACloud. To show the performance of the proposed algorithm (GSA-RACloud), this algorithm has been compared with the ant colony algorithm (ACO). The empirical results show that the proposed algorithm has a higher performance than the compared algorithm.

关键词： cloud computing resource assignment gravity search algorithm

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Optimization configuration of energy storage capacity in wind solar storage combined power supply network based on gravity search algorithm 3

Optimization configuration of energy storage capacity in win...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Ye, Jianfeng Sha, Lei He, Ping Jiaxing Hengchuang Elect Power Design & Res Inst Jiaxing Zhejiang Peoples R China

ISBN: (纸本)9798350375145;9798350375138

In order to further improve the configuration effect, a method based on gravity search algorithm for optimizing the energy storage capacity of wind solar storage combined power supply network is proposed. Analyze the wind power model, photovoltaic model, and energy storage model group of the wind solar storage combined power supply network;Construct the objective function and constraint conditions for optimizing the configuration of energy storage capacity in the wind solar energy storage combined power supply network, and construct a configuration model;Introducing chaos thinking to improve the gravity search algorithm, using an improved gravity search algorithm to optimize the configuration model, and achieving optimal configuration of energy storage capacity in the wind solar storage combined power supply network. The experimental results show that the improved gravity search algorithm of this method has better performance in solving models. After the application of this method, the voltage stability of the wind solar storage combined power supply network is stronger, and the maximum and fluctuation values of the load are significantly reduced.

关键词： gravity search algorithm Wind and solar energy storage Energy storage capacity

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A multi-subgroup hierarchical hybrid of central force optimization and gravity search algorithm 12

A multi-subgroup hierarchical hybrid of central force optimi...

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Fei, Qin Jie, Liu Jing, Li Bin, Zhou Xian Univ Sci & Technol Deans Off Xian Peoples R China Xian Univ Sci & Technol Coll Sci Xian Peoples R China

ISBN: (纸本)9781509048403

To balance the explore and exploit, we combine two algorithms, Central Force Optimization (CFO) and Gravitational search algorithm (GSA), and propose a multi-subgroup hierarchical hybrid of CFO and GSA (HCFO-GSA). This hybrid algorithm has a hierarchical structure: base level is a series of subgroups of CFO, and the top level comprises all elite individuals with entire subgroups. The performances of HCFO-GSA have been tested by several Benchmarks. The numerical experiment results show that the new algorithm is superior to other related methods.

关键词： Central Force Optimization gravity search algorithm subgroup hierarchical

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Agricultural UAV Path Planning Based on Improved A* and gravity search Mixed algorithm

Agricultural UAV Path Planning Based on Improved A* and Grav...

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作者： Ru Tian Maoyong Cao Fengying Ma Peng Ji Shandong University of Science and Technology Qilu University of Technology (Shandong Academy of Sciences)

In order to ensure the accuracy, efficiency and energy saving of Unmanned Aerial Vehicles(UAVs) in irregular working areas, this paper has carried out research on its path planning methods. Taking the heading angle and return point of agricultural UAVs as the optimization objectives, the plan was made by improving the A* and gravitational search algorithm to plan the optimal path. First, the execution process of the algorithm is analysed, the application model is solved, and then the simulation experiment is performed through MATLAB. The path calculated by the proposed method was also compared with the unplanned and gravitational search algorithm planned path respectively, which showed the non-spraying distance of the proposed method was reduced by 23.18% and 5.83%, while the excess coverage of pesticide spraying was reduced by 64.47% and 64.47%. The study indicated that the proposed method which could produce paths with less working time was a reasonable, feasible and useful solution for the path planning problem of the plant protection UAV.

关键词： . Agricultural UAV path planning grid method A~* algorithm gravity search algorithm

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Application of BPNN optimized by chaotic adaptive gravity search and particle swarm optimization algorithms for fault diagnosis of electrical machine drive system

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ELECTRICAL ENGINEERING 2022年第2期104卷 819-831页

作者： Zhang, Peng Cui, Zhiwei Wang, Yinjiang Ding, Shichuan Anhui Univ Sch Elect Engn & Automat Hefei Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Tech Hefei Peoples R China Anhui Univ Minist Educ Power Qual Engn Res Ctr Hefei Peoples R China Anhui Univ Ind Power Saving & Safety Lab Hefei Peoples R China

This paper proposes a fault diagnosis method for electrical machine drive system by using backpropagation neural network (BPNN) optimized by chaotic adaptive gravity search algorithm (GSA) and particle swarm optimization (PSO) algorithm. In this method, an adaptive gravitational constant factor based on iteration times and chaotic mapping is introduced to balance the global search ability and local development ability of GSA. Then, it is combined with PSO algorithm to solve the problem of prematurity and local optimum of PSO algorithm. Finally, combined with BPNN, a fault diagnosis model based on chaos adaptive GSA-PSO-BPNN is established. The experimental results show that the introduction of the attenuation factor of adaptive gravitational constant and the chaotic mapping can improve the classification performance of GSA-PSO-BPNN, and the feasibility and effectiveness of the chaotic adaptive GSA-PSO algorithm are also proved.

关键词： Backpropagation neural network gravity search algorithm Chaotic mapping Particle swarm optimization Fault diagnosis

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Output Voltage Control of BESS Inverter in Stand-Alone Micro-Grid Based on Expanded Inverse Model

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IEEE ACCESS 2020年 8卷 3781-3791页

作者： Wang, Xingwu Lin, Yongjun Wang, Bingshu Liu, Weiliang Bai, Kang North China Elect Power Univ Sch Control Sci & Engn Baoding 071003 Peoples R China Baoding Sinosimu Technol Co Ltd Baoding 071051 Peoples R China

This paper investigates robust output voltage control of battery energy storage systems (BESS) inverter in stand-alone micro-grid. The transfer function model between the output voltage and duty cycle of the BESS inverter is established, base on which the main factors affecting the output voltage are analyzed theoretically. Then, the expanded inverse model of the BESS inverter is developed based on BP neural network, and the gravity search algorithm (GSA) is employed to search the initial values of the network's parameters in the training process. Further, a control method for the output voltage of BESS inverter is proposed by putting the expanded inverse model in series with the original system as well as performing the single-loop control with PI controller. Both simulation experiment results and prototype experiment results show that the proposed expanded inverse model control method has a strong capability of disturbance rejection and could ensure the high power quality for the output voltage of the BESS inverter.

关键词： BESS inverter inverse model BP neural network gravity search algorithm load disturbance

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A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第8期45卷 1397-1410页

作者： Chen, Xinjie Wang, Wenhai Zou, Ruping Zhang, Zeyin Chen, Shichao Wang, Jun Liu, Xinggao Zhejiang Univ Control Dept State Key Lab Ind Control Technol NGICS Platform Hangzhou Peoples R China Xian Modern Control Technol Res Inst Xian Peoples R China Zhejiang Univ Math Dept Hangzhou Peoples R China Zhejiang Univ Control Dept State Key Lab Ind Control Technol NGICS Platform Hangzhou 310027 Zhejiang Peoples R China

Radar operating mode identification is one of the top priorities in the electronic countermeasure field for threat prediction. However, the inference speed and accuracy of existing identification methods are still not satisfactory for threat detection, and some of them need extra prior knowledge to extract features. This paper introduces an identification approach for radar operating mode to solve these problems. The variational relevance vector machine (VRVM) is presented as the feature extractor and the basic unit of identification. An improved chaotic gravity search algorithm (CGSA) is developed to increase the search breadth and optimize the hyperparameters of VRVM. Threshold-one-versus-one (TOVO) is further proposed to screen the identification results to form the final ensemble method called CGSA-VRVM-TOVO. Experimental results indicate the CGSA-VRVM-TOVO achieves 99.44% accuracy without prior knowledge. Compared with XGBoost, Random Forest, and LightGBM, our approach is 1.94%, 0.55%, and 1.11% higher in accuracy, respectively, and twice as fast as the fastest method of them all.

关键词： Radar operating mode identification gravity search algorithm variational relevance vector machine ensemble algorithm

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