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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Shen, Yizhuang Wang, Xihuai Logistic Engineering College Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350382426

Distributed power sources can lead to problems such as increased active network loss and voltage quality degradation of the system when they are integrated into the grid. In this paper, we propose a reactive power optimization method for distributed power sources by time period. The objectives of this method are to reduce the system active network loss and voltage offset, to establish a power system reactive power optimization model containing distributed power sources in time slots, and to improve the standard gravitational search algorithm which is easy to fall into the local optimum. Simulation verification is carried out with the improved IEEE33 node arithmetic example, and the results show that this method can effectively reduce the system active network loss and improve the voltage quality. © 2023 IEEE.

关键词： component Distributed power gravitational search algorithm inverse learning LSTM quadratic interpolation reactive power optimization Tent chaotic mapping

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Optimum Design of an Adaptive Fuzzy Controller as Active Suspension for a Quarter-Car Model

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IETE JOURNAL OF REsearch 2022年第5期68卷 3638-3646页

作者： Mahmoodabadi, M. J. Javanbakht, M. Sirjan Univ Technol Dept Mech Engn Sirjan Iran

This research introduces an optimal adaptive fuzzy controller consists of two fuzzy systems which each of them includes 1 output, 2 inputs, and 25 fuzzy rules. The designed fuzzy sytems, in turn, comprises Gaussian membership functions and applies the singleton fuzzifier, center average defuzzifier, and product inference engine. Moreover, the gravitational search algorithm (GSA) is utilized to ascertain the optimum parameters of the controller. The body acceleration and the relative displacement between the tire and the sprung mass are utilized to form a proper objective function in the optimization procedure. Results show the dominance of the proposed approach over the conventional controllers.

关键词： Active suspension system Adaptive control Fuzzy control gravitational search algorithm Optimal control Quarter-car model

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Sinusoidal Map Jumping Gravity search algorithm Based on Asynchronous Learning

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第3期18卷 332-343页

作者： Zhou, Xinxin Zhu, Guangwei Northeast Elect Power Univ Sch Comp Sci Jilin Jilin Peoples R China Guangdong Yudean Jinghai Power Generat Co Ltd Jieyang Peoples R China

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

关键词： Asynchronous Learning gravitational search algorithm Levy Flight Sinusoidal Map

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Solving Dirichlet boundary problems for ODEs via swarm intelligence

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MATHEMATICAL SCIENCES 2022年第4期16卷 325-341页

作者： Gunel, Korhan Gor, Iclal Adnan Menderes Univ Dept Math Aydin Turkey

In this paper, we examine the effects of swarm intelligence techniques on the obtaining of numerical solutions of ordinary differential equations (ODEs) with Dirichlet boundary conditions (DBCs) via feed-forward neural networks. Population-based global optimization methods such as artificial bee colony (ABC), ant colony optimization (ACO), gravitational search algorithm (GSA) and particle swarm optimization (PSO) are utilized in order to solve ODEs with DBCs numerically. Furthermore, we hybridize ACO, ABC and GSA with PSO to improve the numerical solution of optimization algorithms. Unlike the approaches in the literature, we use the personal best and global best solutions of PSO for the updating position of employed and onlooker bees in ABC, in the hybrid method that combines PSO and ABC. We also prefer the adaptive number of swarm scheme on the hybrid system of PSO and ACO. In the experiments, we give some different Dirichlet Boundary Problems to indicate the efficiency of optimization methods. Also the results are compared with the well-known traditional methods as shooting method, finite difference method and Lobatto IIIa.

关键词： Differential equations Artificial bee colony algorithm Ant colony algorithm gravitational search algorithm Particle swarm optimization Feed-forward neural network

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gravitational search algorithm for coordinated design of PSS and TCSC as damping controller

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Journal of Central South University 2012年第4期19卷 923-932页

作者： M.Eslami H.Shareef A.Mohamed M.Khajehzadeh Department of Electrical Electronic and Systems EngineeringUniversity Kebangsaan MalaysiaBangi 43600SelangorMalaysia

A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.

关键词： gravitational search algorithm power system stabilizer thyristor controlled series capacitor tuning

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization gravitational search algorithm

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Nonlinear System Identification Using Adaptive Volterra Model Optimized with Sine Cosine algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14411-14422页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttarakhand India

This paper presents the handling of nonlinear system identification problem based on Volterra model. Gradient-based algorithms are generally applied to solve system identification problems. However, these algorithms have the limitation of getting trapped in the local minimum. In the presented work, a novel population-based optimization algorithm popularly known as sine cosine algorithm (SCA) is being utilized for the identification of nonlinear discrete-time system. The SCA uses mathematical sine and cosine functions for the purpose of optimization. SCA is responsible for the creation of multiple random solutions and moving them towards best solution while maintaining proper balance between the exploitation and exploration phases of optimization. The performance evaluation of the applied SCA is carried out in terms of coefficient evaluation, mean square error and convergence profile. Two different examples for nonlinear system are presented in this work so as to demonstrate the validity of the employed algorithm. Performance analysis of the proposed approach with the existing state-of-the-art algorithms proves that the SCA outperforms the other algorithms.

关键词： Nonlinear system Volterra model Sine cosine algorithm gravitational search algorithm Particle swarm optimization algorithm Cuckoo search algorithm

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gravitational search algorithm Based Optimal Reactive Power Dispatch for Voltage Stability Enhancement

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第9期40卷 956-976页

作者： Roy, P. K. Mandal, B. Bhattacharya, K. Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

In this article, an efficient and reliable optimization procedure based on the behaviors of swarm in nature, namely the gravitational search algorithm, is proposed for solving multi-objective optimal reactive power dispatch problems, which minimizes transmission loss while maintaining the quality of voltages. The gravitational search algorithm is based on Newton's law of gravity and interaction of masses. In the proposed algorithm, the searcher agents which are a collection of masses interact with each other using Newton's laws of gravity and motion. In order to investigate the performance of the proposed scheme, multi-objective optimal reactive power dispatch problems are solved. This new gravitational search algorithm method is tested on IEEE 57-bus and IEEE 118-bus power systems. Results obtained by the gravitational search algorithm are compared with two versions of genetic algorithms, three versions of differential evolution algorithms, four versions of particle swarm optimization algorithms and the seeker optimization algorithm It is observed from the test results that the proposed gravitational search algorithm approach converges to better solutions much faster than the earlier reported approaches.

关键词： biogeography-based optimization evolutionary programming gravitational search algorithm law of gravity law of motion optimal reactive power dispatch voltage stability margin

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Research on UFastSLAM algorithm Based on Gravity search algorithm

Research on UFastSLAM Algorithm Based on Gravity Search Algo...

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OCEANS Hampton Roads Conference

作者： Zhang, Di Zhang, Xin Zhai, Ning Chen, Ziqing He, Bo Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Simultaneous Localization and Mapping (SLAM) algorithm is one of the effective strategies to solve the problem of underwater precise localization and navigation. This paper proposes a Gravity search algorithm (GSA) to improve UFastSLAM algorithm which has been successfully used for several years, the proposed GSA improves the state of particles before updating the weight by improving the resampling step, and makes the particle set tend to the region with high a posteriori probability, optimizes the particle state, and can effectively solve the problem of particle degradation and improve the navigation accuracy. The proposed method is verified by sea trial data and simulation experiments. The experimental results show that this method has higher accuracy and efficiency than traditional UFastSLAM in the process of robot positioning.

关键词： UFastSLAM Autonomous Underwater Vehicle gravitational search algorithm

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Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization

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ENERGIES 2024年第15期17卷 3623页

作者： Su, Lei Wu, Wenxiang Feng, Wanli Qin, Junda Ao, Yuqi State Grid Hubei Elect Power Res Inst Wuhan 430074 Peoples R China State Grid Smart Grid Res Inst Co Ltd State Key Lab Adv Power Transmiss Technol Beijing 102209 Peoples R China

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

关键词： data center uncertainty interval optimization collaborative planning particle swarm optimization algorithm gravitational search algorithm

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