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Loadability enhancement with FACTS devices using gravitational search algorithm

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 tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

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OPTIMAL CONTROL APPLICATIONS & METHODS 2017年第6期38卷 993-1003页

作者： Dahiya, Preeti Sharma, Veena Naresh, R. Natl Inst Technol Elect Engn Dept Hamirpur 177005 Himachal Prades India

In this paper, the load frequency regulation problem of 2-area interconnected power system is resolved using the sliding mode control (SMC) methodology. Interconnected 2-area power systems with and without doubly fed induction generator wind turbines are considered for implementing the proposed optimal control methodology. Here, a heuristic gravitational search algorithm (GSA) and its variants such as opposition learning-based GSA (OGSA), disruption-based GSA (DGSA), and disruption based oppositional GSA (DOGSA) are employed to optimize the switching vector and feedback gains of SMC. In order to overcome the inherent chattering problem in SMC, the control signals are considered in the objective function. The robustness of optimized SMC is analyzed by the inclusion of nonlinearities such as generation rate constraint (GRC), governor deadband, and time delay during the signal processing between the control areas, which are present in the real-time power system. The insensitiveness of the optimal controller is shown by variation in system parameters like loading condition, speed governor constant, turbine constant, and tie-line power coefficient. Further, the optimal SMC has been studied with significant load variations and wind power penetration levels in the control areas. The potential of proposed SMC design with chattering reduction feature is shown and validated by comparing the results obtained with the other reported methods in the literature.

关键词： disruption operator gravitational search algorithm load frequency regulation sliding mode control wind turbine

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Error-free and mean value based reversible data hiding using gravitational search algorithm in encrypted images

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第21期81卷 30749-30768页

作者： Raju, K. Upendra Prabha, N. Amutha VIT SENSE Vellore Tamil Nadu India VIT Univ Sch Elect Engn Vellore Tamil Nadu India

In recent years the data hiding on encrypted image is significant topic for data security. Due to its capability for preserving confidentiality, Reversible Data Hiding (RDH) on encrypted domain will helpful on cloud computing as an emerging technology. In this paper, an error free and mean value based RDH process with high capacity on encrypted images is proposed. Here, the image provider (content owner), data hider and data receiver are three parties. Initially, the image provider keeps the averages to obtain the modified image. Encrypt the modified image further through the aid of the encryption key and pass it to data hider. In data hider, gravitational search algorithm (GSA) is utilized to find the best pixel locations in encrypted image for information hiding. Moreover, the similar encrypted image size, an alpha channel is created and then combines the encrypted image and the alpha channel for embedding secret data to make an encrypted image with embedded data. On receiver side, through the aid of encryption key, the receiver recovers the image by decrypting the encrypted image and also the secret bits are removed through the aid of data hiding keys by data extraction. The experimental result demonstrates that proposed system has better performance compared to other state-of- art strategies. The peak signal-to-noise ratio (PSNR) achieved by the proposed system is better than 8.36% and 18.3% compared to other existing works.

关键词： Reversible data hiding Data security Encrypted images Alpha channel Content owner gravitational search algorithm

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A modified gravitational search algorithm for slope stability analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第8期25卷 1589-1597页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan El-Shafie, Ahmed Eslami, Mahdiyeh Univ Kebangsaan Malaysia Dept Civil & Struct Engn Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Dept Elect Engn Bangi 43600 Selangor Malaysia

This paper first proposes an effective modification for the gravitational search algorithm. The new strategy used an adaptive maximum velocity constraint, which aims to control the global exploration ability of the original algorithm, increase its convergence rate and thereby to obtain an acceptable solution with a lower number of iterations. We testify the performance of the modified gravitational search algorithm (MGSA) on a suite of five well-known benchmark functions and provide comparisons with standard gravitational search algorithm (SGSA). The simulated results illustrate that the modified GSA has the potential to converge faster, while improving the quality of solution. Thereafter, the proposed MGSA is employed to search for the minimum factor of safety and minimum reliability index in both deterministic and probabilistic slope stability analysis. The factor of safety is formulated using a concise approach of the Morgenstern and Price method and the advanced first-order second-moment (AFOSM) method is adopted as the reliability assessment model. The numerical experiments demonstrate that the modified algorithm significantly outperforms the original algorithm and some other methods in the literature. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Adaptive maximum velocity Minimum factor of safety Minimum reliability index

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Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle

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ROBOTICA 2024年第7期42卷 2453-2487页

作者： Jiao, Keming Chen, Jie Xin, Bin Li, Li Zheng, Yifan Zhao, Zhixin Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing Peoples R China

Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

关键词： unmanned aerial vehicle path planning gravitational search algorithm chaos

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Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第14期42卷 5839-5847页

作者： Gonzalez, Beatriz Valdez, Fevrier Melin, Patricia Prado-Arechiga, German Tijuana Inst Technol Calzada Tecnol S-N Tijuana Mexico Excel Med Ctr Tijuana Mexico

In this paper the main challenge is to find the optimal architecture of modular neural networks, which means finding out the optimal number of modules, layers and nodes of the neural network, with the fuzzy gravitational search algorithm for a pattern recognition application and in addition provide a comparison with the original gravitational approach. The proposed method is applied to the recognition of medical images. One of the most common methods for detection and analysis of diseases in the human body, by physicians and specialists, is the use of medical images. In this case, we are using a database of echocardiograms, which contains images of disease and healthy patients to test the proposed approach. The optimally designed modular neural networks produce simulation results that are able to show the advantages of the proposed approach. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Modular neural networks gravitational search algorithm Pattern recognition Echocardiograms GSA Fuzzy logic Law of gravity Second motion law

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Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting

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APPLIED SOFT COMPUTING 2017年 52卷 290-305页

作者： Li, Chaoshun Mao, Yifeng Zhou, Jianzhong Zhang, Nan An, Xueli Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China China Inst Water Resources & Hydropower Res Beijing 100044 Peoples R China

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system, which regulates frequency and power of hydropower generating unit. In previous study, control model of HTGS is always overly simplified and the elastic water hammer model is seldom considered. In this paper, a nonlinear HTGS model with elastic water hammer effect has been studied and a fuzzy-PID controller is designed to improve control quality of this system. In order to optimize the fuzzy-PID controller, a novel gravitational search algorithm based on Cauchy mutation and mass weighing (GSA-CW) has been proposed with two improvements: a weighting strategy is designed to accelerate the convergence by assigning weights to agents in mass calculation;a combined mutation strategy based on Cauchy and Gaussian distribution is proposed to balance the exploration and exploitation ability of the proposed algorithm. At first, the searching ability of the GSA-CW has been verified on a set of 13 complex benchmark functions by statistical analysis. And then, the GSA-CW is applied to optimize the fuzzy-PID controller, while different meta-heuristics and different PID controllers are employed for comparison. Experimental results indicate that the fuzzy-PID controller optimized by the GSA-CW is more effective to improve the control quality of the nonlinear HTGS. (C) 2016 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Cauchy mutation Mass weighting Hydraulic turbine governing system Fuzzy-PID controller

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A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction

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WATER SUPPLY 2022年第8期22卷 6959-6977页

作者： Zhang, Xianqi Wu, Xilong Zhu, Guoyu Lu, Xiaobin Wang, Kai North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China Prov & Minist Cobuilt Collaborat Innovat Ctr Yellow River Basin Water Resources Efficient Util Zhengzhou 450046 Peoples R China Henan Water Conservancy & Water Transportat Engn Zhengzhou 450046 Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu Sichuan Peoples R China

The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. Coupled runoff prediction model based on the gravitational search algorithm (GSA) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed to address the non-linear and seasonal features of runoff data. The GSA has a significant local optimisation capability, while the SARIMA model allows for real-time adjustment of the model using historical data and is suitable for analysing time series with seasonal variations. Consequently, the GSA-SARIMA model was developed and applied to the runoff prediction of the Xianyang section of the Wei River. The results suggest that the GSA-SARIMA model achieves a linear correlation coefficient of 0.9351, a Nash efficiency coefficient of 0.91, a mean relative error of 6.57 and a root mean square error of 0.21. All of the evaluation indicators of this model outperform the other models developed, and its application to actual runoff prediction is feasible, which creates a new path for runoff prediction.

关键词： ARIMA model gravitational search algorithm runoff prediction seasonal differencing Wei River

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A comparative evaluation of gravitational search algorithm (GSA) against Artificial Bee Colony (ABC) for thermodynamic performance a geothermal power plant

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ENERGY 2018年第PartA期165卷 1061-1077页

作者： Ozkaraca, Osman Mugla Sitki Kocman Univ Technol Fac Dept Informat Syst Engn TR-48000 Mugla Turkey

Optimizing a complex system/problem under real working conditions with optimization methods means ensuring that they operate more efficiently, economical, and eco-friendly. For this purpose, in order to maximize the exergy efficiency of a thermodynamic model of a real operated geothermal power plant (GPP), two optimization methods, namely gravitational search algorithm (GSA) and Artificial Bee Colony (ABC), have been comparatively evaluated in this study. The selected thermodynamic model is a problem that is highly complex, non-linear and unsolvable through mathematical methods. In order to solve the problem, 17 optimization parameters have been selected on the model. In addition, the selected parameters have been divided into 11 groups according to the system equipment specifications to reduce time loss. The results of the study reported that GSA and ABC maximized the exergy efficiency of the real system from 14.52% to 26.31% and 23.92% respectively. The effects of the optimized parameters on the model are observed, and it has been verified by GPP operators, engineers and researchers that no contrariety to logic and engineering discipline existed. Hence, the results of GSA method for the engineering problem addressed in this study are better than those of ABC method and they responded in a much shorter time. The most effective group in both methods is the G3 group related to the turbines. Besides, the most effective optimization parameters on the system performance are the pressure differences in evaporators and mass flow of the geothermal fluid. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Optimization gravitational search algorithm Artificial Bee Colony Geothermal power plant Exergy efficiency Performance comparison

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Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market

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APPLIED SOFT COMPUTING 2013年第5期13卷 2445-2455页

作者： Kumar, J. Vijaya Kumar, D. M. Vinod Edukondalu, K. Natl Inst Technol Dept Elect Engn Warangal 506004 Andhra Pradesh India

A novel stochastic optimization approach to solve optimal bidding strategy problem in a pool based electricity market using fuzzy adaptive gravitational search algorithm (FAGSA) is presented. Generating companies (suppliers) participate in the bidding process in order to maximize their profits in an electricity market. Each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. The gravitational search algorithm (GSA) is tedious to solve the optimal bidding strategy problem because, the optimum selection of gravitational constant (G). To overcome this problem, FAGSA is applied for the first time to tune the gravitational constant using fuzzy "IF/THEN" rules. The fuzzy rule-based systems are natural candidates to design gravitational constant, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested on IEEE 30-bus system and 75-bus Indian practical system and compared with GSA, particle swarm optimization (PSO) and genetic algorithm (GA). The results show that, fuzzification of the gravitational constant, improve search behavior, solution quality and reduced computational time compared against standard constant parameter algorithms. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Electricity market Bidding strategies Market clearing price (MCP) Fuzzy inference gravitational search algorithm

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