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PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

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CSEE Journal of Power and Energy Systems 2018年第2期4卷 210-218页

作者： Yordanos Kassa Semero Jianhua Zhang Dehua Zheng School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China Goldwind Science and Technology Co. Ltd.Beijing 100176China

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)*** accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this *** GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the *** proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in *** results demonstrate the superior performance of the proposed method as compared with commonly used forecasting *** proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.

关键词： ANFIS binary genetic algorithm feature selection hybrid method particle swarm optimization PV power forecasting

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EDA-based Decomposition Approach for binary LSGO Problems 8

EDA-based Decomposition Approach for Binary LSGO Problems

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8th International Joint Conference on Computational Intelligence

作者： Sopov, Evgenii Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9789897582011

In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LSGO) problems. Many well-known real-world LSGO problems are not separable and are complex for detailed analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution schemes using the problem decomposition. These algorithms are mainly proposed for the real-valued search space and cannot be applied for problems with discrete or mixed variables. In this paper a novel technique is proposed, that uses a binary genetic algorithm as the core technique. The estimation of distribution algorithm (EDA) is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The EDA-based decomposition GA using the island model is also discussed. The results of numerical experiments for benchmark problems from the CEC competition are presented and discussed. The experiments show that the approach demonstrates efficiency comparable to other advanced techniques.

关键词： Large-Scale Global Optimization Problem Decomposition Estimation of Distribution algorithm binary genetic algorithm

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Two-level Topology Optimization based on a Metaheuristic Method and Morphological Reconstruction 47

Two-level Topology Optimization based on a Metaheuristic Met...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Ruzbehi, Shabnam Hahn, Ingo Univ Erlangen Nurnberg Inst Elect Drives & Machines Erlangen Germany

ISBN: (纸本)9781665435543

This paper offers a Topology Optimization (TO) method to replace conventional parameter optimization, which is a common method for electrical machine's or electromagnetic device's design optimization. In most of the final designs gained with TO, there are challenges in the manufacturing process using the nowadays available manufacturing technologies. Therefore, this work investigated the approach on two tasks, in the first level a global optimization method, called Enhanced binary genetic algorithm is applied to the design domain. After optimization with this meta-heuristic method, some holes inside the structure or some spread iron cells appear. This drawback will be improved in the second level by one of the image processing methods, called Morphological Reconstruction, to gain a smoother shape to reduce the manufacturing complexities while reaching reasonable technical characteristics. The goal is to find a lighter weight of the structure while preserving the force in a suited amount. The proposed method is applied to an E-core actuator as a case study. After using the proposed hybrid optimization method, air cells appear around some parts of the actuator's coil, which are used to extend the copper areas, so that the magnetic force considerably increases. In general, it is possible to extend the method to different active parts of the electrical machines.

关键词： Morphological Reconstruction binary genetic algorithm topology optimization Actuators

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Cybersecurity in New Communications Systems through 4D Array Architectures 13

Cybersecurity in New Communications Systems through 4D Array...

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13th European Conference on Antennas and Propagation (EuCAP)

作者： Rocca, Paolo Poli, Lorenzo Hannan, Mohammad Abdul Yang, Shiwen Massa, Andrea Univ Trento UniTN ELEDIA Res Ctr ELEDIA Trento Italy UESTC ELEDIA ELEDIA Res Ctr Chengdu Sichuan Peoples R China Univ Carlos III Madrid UC3M ELEDIA ELEDIA Res Ctr Madrid Spain

ISBN: (纸本)9788890701887

In this paper, an optimization-based synthesis of 4D array is performed in order to provide an additional layer of security to secure the communication signals from eavesdroppers. The security is ensured by transmitting undistorted signals along the directions of interest while maximizing the distortion elsewhere, thanks to the directional modulation (DM) characteristics of 4D array. Towards this goal, the on/off sequence of modulating pulses controlling the RF switches have been optimized by a customized strategy based on a binary genetic algorithm (GA). An innovative objective function based on fidelity factor (FF) which quantifies the degree of distortion as a function of angular directions is defined. Numerical and experimental results have been reported in order to validate behavior and potentialities of the proposed synthesis approach for physical layer secure communications.

关键词： secure communication 4D antenna array time-modulated array directional modulation binary genetic algorithm

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Multipurpose Reconfiguration of Deregulated Distribution Networks Using BGA

Multipurpose Reconfiguration of Deregulated Distribution Net...

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2nd International Power and Energy Conference (PECon 2008)

作者： Jalilzadeh, S. Hosseini, H. Nabaei, V. Govar, G. R. Zareie Zandi, M. Zanjan Univ Dept Elect Engn Zanjan Iran

ISBN: (纸本)9781424424047

A multipurpose optimization of deregulated distribution network is proposed in this paper. Enhancing system reliability and minimizing energy supply cost (ESC) are considered in the proposed objective function. The solution of the propose problem is based on reconfiguration technique, and using binary genetic algorithm (BGA). A typical test system in both traditional and deregulated environments are investigated to compare the obtained results. Finally, the validity of proposed solution method is demonstrated by developing an exhaustive search technique.

关键词： binary genetic algorithm Deregulated Distribution Network Multipurpose Objective Function Reconfiguration

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GA BASED INPUT-OUTPUT FEEDBACK LINEARIZATION CONTROLLER FOR ROTARY INVERTED PENDULUM SYSTEM

GA BASED INPUT-OUTPUT FEEDBACK LINEARIZATION CONTROLLER FOR ...

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5th International Symposium on Mechatronics and its Applications

作者： Hassanzadeh, Iraj Mobayen, Saleh Univ Tabriz Fac Elect & Comp Engn Dept Control Engn Res Lab Robot Tabriz Iran

ISBN: (纸本)9781424420339

This paper presents an optimum approach to design input-output feedback linearization (IOFL) controller for a rotary inverted pendulum (RIP) using the binary genetic algorithm. genetic algorithms (GAs) are stochastic global search methods that emulate the process of natural evolution and because of their simplicity and robustness, they are more popular and applicable. The primary design goal is to minimize the integral absolute error of system angles and velocities and balance the pendulum in the inverted position by minimizing overshoot, settling time and rise time of step response. An objective function using these indexes is established. Then by minimizing the objective function using binary genetic algorithm, the optimal controller parameters can be assigned. Simulation results verified capable and competent characteristics of proposed optimal feedback linearization controller. The proposed method can be considered as a promising way for control of various similar nonlinear systems.

关键词： Rotary inverted pendulum input-output feedback linearization binary genetic algorithm SIMO controller nonlinear model

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Modified Cuckoo Search for Resource Allocation on Social Internet-of-Things 5

Modified Cuckoo Search for Resource Allocation on Social Int...

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5th IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC)

作者： Jindal, Himanshu Singh, Hari Bharti, Monika Jaypee Univ Informat Technol Dept Comp Sci & Engn Wakhnaghat Solan India

ISBN: (纸本)9781728106465

The fundamental requirement for communication and computation across distinct application areas on Social Internet of Things (SIoT) is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities face challenges due to interoperability. In order to eliminate the challenge, the paper focuses on to propose an optimal resource selection technique namely, Modified Cuckoo Search (MCSA). The technique helps in reducing traffic congestion on network by selecting optimal resources in less time. The technique is tested on random dataset. The obtained results show that MCSA outperforms 22 % approximately in comparison to nature-inspired, meta heuristic based machine learning algorithms i.e., Particle Swarm Optimization and binary genetic algorithm.

关键词： Social Internet-of-Things Resource Allocation Cuckoo Search Particle Swarm Optimization binary genetic algorithm

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Optimization Decision algorithm for Water Resource Allocation and Scheduling in Large-scale Water Supply Networks 24

Optimization Decision Algorithm for Water Resource Allocatio...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Su, Yang Guo, Songming Yan, Jun ShenZhen Water Grp Shenzhen 518001 Peoples R China

ISBN: (纸本)9798400718144

With the acceleration of urbanization and population growth, water resource management is facing increasingly severe challenges. Especially in large-scale water supply networks, how to efficiently, fairly, and sustainably allocate and dispatch water resources has become a key issue. Therefore, this article proposes an optimization decision algorithm for water resource allocation and scheduling in large-scale water supply networks. When delving into the problem of water resource allocation and scheduling, it is necessary to first comprehensively consider the key factors of social, economic, and environmental dimensions, and establish a scientific and reasonable objective function. Consider the water supply capacity, transmission capacity, and water demand capacity of each region as constraints. The binary genetic algorithm is used to transform the allocation and scheduling problem of water resources into a binary coding form. Through iterative optimization of the initial population, genetic, mutation, crossover and other mechanisms, the optimal solution is found in the search space. Through experimental verification, this method can reasonably allocate water resources, ensure water supply security in various regions, improve the balance of the water supply network, and reduce water resource waste and loss.

关键词： binary genetic algorithm Water allocation Water scheduling binary coding Optimal decision-making algorithm

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Three-Dimensional Grain Design Optimization of Solid Rocket Motors 7

Three-Dimensional Grain Design Optimization of Solid Rocket ...

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7th International Conference on Recent Advances in Space Technologies (RAST)

作者： Yucel, Osman Acik, Sevda Toker, K. Atilgan Dursunkaya, Zafer Aksel, M. Haluk Roketsan Missiles Ind Inc Prop Syst Design Dept Ankara Turkey Middle East Tech Univ Dept Mech Engn Ankara Turkey

ISBN: (纸本)9781479976973

This study investigates the ballistic design optimization of three-dimensional grains of solid rocket motors (SRMs). The geometric modeling and burnback analysis of grains are performed analytically by using basic geometries like cylinder, cone, prism, sphere, ellipsoid, and torus. For the internal ballistic analysis, a quasi-steady zero-dimensional flow solver is used. Three different optimization methods are considered: real-coded genetic algorithm (GA), binary genetic algorithm and complex method. The optimization methods are applied to the problems of finding the parameters of grain geometry satisfying an objective thrust versus time profile and maximizing the parameter of total impulse satisfying the constraints of chamber pressure and propellant mass.

关键词： Solid rocket motor grain design burnback analysis optimization binary genetic algorithm real coded genetic algorithm complex method

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Adaptive H_∞ Controller Based on GA-Hybrid Wavelet RBF for Robot Arm 2

Adaptive H<sub>∞</sub> Controller Based on GA-Hybrid Wavele...

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2nd RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

作者： Ahmadi, Bahar Nourisola, Hamid Univ Tabriz Fac Elect & Comp Engn Dept Elect Control Engn Tabriz Iran

ISBN: (纸本)9781479967438

this paper proposes an adaptive H-infinity control schemes based on a GA-hybrid wavelet RBF for tracking control of a two link robot arm. In this control scheme, GA-hybrid wavelet RBF is employed to construct the adaptive controller. A binary genetic algorithm (GA) is proposed to identify the wavelet RBF kernel function parameters. The most important characteristic of the proposed control scheme is its inherent robustness and its ability to handle the nonlinear behavior of the system. Finally, some simulation results are presented to show the utility of our proposed method.

关键词： hybrid Wavelet-RBF adaptive H-infinity control Two-Link Robot Arm binary genetic algorithm

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