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Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第3期12卷 1027-1037页

作者： Anbarasi, S. Muralidharan, S. Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named particle swarm optimization (PSO), Bacterial Foraging optimization algorithm (BFOA) and hybrid Bacterial Foraging particle swarm optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

关键词： Bacterial foraging optimization algorithm Hybrid bacterial foraging particle swarm optimization algorithm Load frequency control particle swarm optimization algorithm PID controller

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A social spider optimized image fusion approach for contrast enhancement and brightness preservation

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APPLIED SOFT COMPUTING 2017年 52卷 575-592页

作者： Maurya, Lalit Mahapatra, Prasant Kumar Kumar, Amod CSIR Cent Sci Instruments Org Biomed Instrumentat Div V2 Sect 30C Chandigarh 160030 India Natl Inst Tech Teachers Training & Res Dept Elect & Commun Engn Sect 26 Chandigarh 160019 India

Image enhancement means to process the image in order to obtain a visually pleasing effect on image with more perception details and less noise output. In this work, a social spider optimization based algorithm is used to produce two enhanced images, one having high sharpness or contrast with maximum entropy and the other having high peak signal-to-noise ratio (PSNR) sharp image. The two enhanced images are fused to obtain an output image which has an optimal trade-off between sharpness and noise. The proposed algorithms are applied to lathe tool images as well as to some other standard images to verify their effectiveness. The results are compared with conventional image enhancement techniques such as Histogram Equalization (HE), Linear Contrast Stretching (LCS), and Standard particle swarm optimization (PSO) algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Social spider optimization particle swarm optimization algorithm Image enhancement Image fusion Histogram equalization Linear contrast stretching

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Clustering performance comparison of new generation meta-heuristic algorithms

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KNOWLEDGE-BASED SYSTEMS 2017年第Aug.15期130卷 1-16页

作者： Ozbakir, Lale Turna, Fatma Erciyes Univ Ind Engn Dept TR-38039 Kayseri Turkey

This article addressed two new generation meta-heuristic algorithms that are introduced to the literature recently. These algorithms, proved their performance by benchmark standard test functions, are implemented to solve clustering problems. One of these algorithms called Ions Motion optimization and it is established from the motions of ions in nature. The other algorithm is Weighted Superposition Attraction and it is predicated on two fundamental principles, which are "attracted movements of agents" and "superposition". Both of the algorithms are applied to different benchmark data sets consisted of continuous, categorical and mixed variables, and their performances are compared to particle swarm optimization and Artificial Bee Colony algorithms. To eliminate the infeasible solutions, Deb's rule is integrated into the algorithms. The comparison results indicated that both of the algorithms, Ions Motion optimization and Weighted Superposition Attraction, are competitive solution approaches for clustering problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Ions motion algorithm Weighted superposition attraction algorithm particle swarm optimization algorithm Artificial bee colony algorithm

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Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

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JOURNAL OF ELECTRICAL SYSTEMS 2017年第2期13卷 285-294页

作者： Abdolrasol, Maher G. M. Mohamed, Azah Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Bandar Baru Bangi 43650 Selangor Malaysia

This paper discuss virtual power plant (VPP) and Microgrid controller for energy management system (EMS) based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA) and particle swarm optimization algorithm (PSO). The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

关键词： Virtual Power Plant Microgrid Energy Management System Backtracking search algorithm particle swarm optimization algorithm

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The Berth-Quay Cranes and Trucks Scheduling optimization Problem by Hybrid Intelligence Swam algorithm

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第2期11卷 74-89页

作者： Liu, Yi Shahbazzade, Sabina Hangzhou Dianzi Univ Sch Management Hangzhou Zhejiang Peoples R China Univ Calif Berkeley Elect Engn & Comp Sci Berkeley CA 94720 USA

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.

关键词： Artificial Fish swarm algorithm Bioinformatics Computational Intelligence Container Truck Scheduling Problem particle swarm optimization algorithm Quay Crane Scheduling Problem

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Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

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DECISION SUPPORT SYSTEMS 2010年第4期49卷 451-462页

作者： Kuo, R. J. Lin, L. M. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan AU Optron Corp Hsinchu 300 Taiwan

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Clustering analysis ART2 neural network particle swarm optimization algorithm Genetic algorithm

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Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

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INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING 2017年第4期8卷 331-341页

作者： Bechouat, Mohcene Younsi, Abdelaziz Sedraoui, Moussa Soufi, Youcef Yousfi, Laatra Tabet, Ismail Touafek, Khaled Univ 8 Mai 1945 Guelma Lab Telecommun Guelma Algeria Univ Tebessa Labget Lab Dept Elect Engn Tebessa Algeria Univ Tebessa Lab Inverses Problems Modeling Informat & Syst PI Tebessa Algeria CDER URAER Ghardaia 47133 Algeria

Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms;genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

关键词： Photovoltaic system Photovoltaic thermal system Modelization Identification Genetic algorithm particle swarm optimization algorithm

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Automatic Discovery and Recommendation for Telecommunication Package Using particle swarm optimization 7th

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7th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Liu, Shanshan Yang, Bo Wang, Lin Abraham, Ajith Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Sci Network Innovat & Res Excellence MIR Labs Auburn WA USA

ISBN: (纸本)9783319274003;9783319273990

Telecommunication package is a product produced by telecom operator to satisfy different consumer groups. Telecom operator should not only consider the users' acceptability, but also enable to maximize profits. However, how to balance the relationship of both and designing a package are very important tasks and complicated problems. At present, design of telecom package is affected greatly by designer's subjective experience which is blind. In this paper, a new idea of automatic discovery and recommendation for telecom package is proposed. This idea is combined user's acceptance with operator's profit. The package model and customer model are set up based on consumption of customers. particle swarm optimization is used for discovering an inverse package. Meanwhile, the potential customers of the targets are selected by calculating proportion of package attribute usage. Experimental results show that the proposed method has favorable performance.

关键词： Telecom packages particle swarm optimization algorithm Automatic discovery Recommendation

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Kalman Filter and particle swarm optimization on Real Time Satellite Tracking

Kalman Filter and Particle Swarm Optimization on Real Time S...

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CIE International Conference on Radar (RADAR)

作者： Kocadag, Fatih Cinar, Ridvan Firat Demirkol, Askin Abant Izzet Baysal Univ IT Ctr Bolu Turkey Sakarya Univ Elect & Elect Engn Fac Sakarya Turkey

ISBN: (纸本)9781509048281

In this study, it is aimed to tracking a satellite on mobile vehicles for receiving the broadcasting signal of the satellite. Satellite tracking is used in many areas on mobile platforms. Too many problems come with the mobility. A robust tracking algorithm is need for overcome such problems. The robust satellite tracking case is researched and test software is developed for Android based platforms. Satellite tracking can be done with a receiver antenna by adjusting the positions according to the movements of the vehicle. Therefore Kalman Filter and particle swarm optimization algorithm are compared in the use of satellite tracking process. An Android based application was developed to accomplish this and tests were performed using an android - based device on a mobile vehicle. Test data were collected through application from the sensors of the android device. Orientation and location data of the mobile vehicle are collected from the device sensors and they are used in computation of the azimuth and elevation angles of receiver antenna. Two well-known algorithms were implemented and performed on the application for tracking to keep satellite link online. It was systemically investigated the estimation and computational performance of the two algorithm on this subject. The results and test cases were analyzed and discussed. Test cases cover the computation of the azimuth and elevation angles.

关键词： Kalman Filter algorithm particle swarm optimization algorithm target tracking azimuth elevation and skew angle formula accelerometer magnetometer GPS data reading

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QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2010年第2期29卷 188-196页

作者： Zhou, Xuan Li, Zhanchao Dai, Zong Zou, Xiaoyong Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China Guangdong Pharmaceut Univ Sch Pharm Guangzhou 510006 Guangdong Peoples R China

A novel method coupling particle swarm optimization algorithm (PSO) and genetic algorithm (GA) was proposed to optimize simultaneously the kernel parameters of support vector machine (SVM) and determine the optimized features subset. By coupling GA with PSO, the particles produced in each generation in PSO algorithm were processed by crossover and mutation of GA, and then the particles could keep diversity to escape from local optima and find the global optima quickly and accurately. In order to evaluate the proposed method, four peptide datasets were employed for the investigation of quantitative structure-activity relationship (QSAR). The structural and physicochemical features of peptides from amino acid sequences were used to represent peptides for QSAR. The correlation coefficients (R) of training set of the four datasets were 1.0000, 0.9508, 1.0000, 0.9995, the R of test set of the four datasets were 0.9922, 0.9687, 0.9022, 0.7404, respectively. The root-mean-square errors (RMSEs) of training set of the four datasets were 0.0000, 0.0986, 0.0000, 0.0203, the RMSEs of test set of the four datasets were 0.2522, 0.2782, 0.9625, 0.2928, respectively. A protein dataset, which consists of 277 proteins, was also employed to evaluate the current method for predicting protein structural class, and the good results of overall success rate were obtained. The results indicated that the proposed method might hold a high potential to become a useful tool in peptide QSAR and protein prediction research. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Quantitative structure-activity relationship particle swarm optimization algorithm Genetic algorithm Support vector machine Peptide

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