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Optimal Expansion Planning of Distribution System Capacity with Respect to Distributed Generations

INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2016年第3期6卷 817-824页

作者： Nayeripour, Majid Hasanvand, Saeed Fallahzadeh-Abarghouei, Hossein Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Yazd Branch Young Researchers & Elite Club Yazd Iran

Nowadays, distributed generation (DG) is a new option in the power systems to meet the electrical demand growth as well as conventional methods such as substation reinforcement and feeder replacement. This paper presents a new approach to solve single and multi-objective distribution system expansion planning problem including DG and conventional method simultaneously. Since this optimization problem has a nonlinear complex nature, classical mathematical method cannot guarantee to achieve the global optimum solution. So, to overcome this encumbrance, a heuristic evolutionary algorithm based on binary particle swarm optimization (PSO) is proposed. The objective functions of this optimization problem are total expansion cost, voltage deviation, and system losses. The model resolves decision variables as follows: location and size of the DG units, new transformers, and upgraded feeders. This paper proposes several non-dominated solutions, so the decision maker can choose the optimal solution based on the importance of the different objectives. The 9-bus and 30-bus distribution test systems are utilized to show the effectiveness of the proposed method.

关键词： Planning Distribution System Distributed Generation optimization binary particle swarm optimization Weighted Aggregation Method

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Research on the Impact of Distributed Generators on the Distribution Network Reconfiguration

Research on the Impact of Distributed Generators on the Dist...

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2012 International Conference on Power Electronics Engineering and Computer Technology(PEECT 2012)

作者： Wenxia You Lei Cui Pengcheng He Hao Chen Electrical Engineering & Renewable School China Three Gorges University Electric Power Dispatching and communication center Electric Power of Hunan

With the rapid development of distributed generation, all aspects of the distribution network have been affected. This paper proposes the hybrid algorithm of Simulated Annealing(SA) and binary particle swarm optimization(BPSO) in order to study the influence of distributed generation(DG) on the power loss and voltage profile of distribution network system which has been optimized by reconfiguration. The simulation of IEEE33 bus system shows that distributed generation has a beneficial effect on the result of network reconfiguration. The introduction of distributed generation makes the distribution network voltage level improved and power losses reduced.

关键词： Distributed Generations Reconfiguration of Distribution Network binary particle swarm optimization Voltage level Distribution network loss

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Lip Feature Selection based on BPSO and SVM

Lip Feature Selection based on BPSO and SVM

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Measurement Touch the World——IEEE 2011 10th International Conference on Electronic Measurement & Instruments(ICEMI’2011)

作者： Wang Mengjun School of Information Engineering Hebei University of Technology Tianjin 300401 China

In speech synthesis system driven by visual speech,many irrelevant and redundant features will lessen the lipreading recognition *** it is important to select lip features with stronger discriminate *** selection algorithm based on binary particle swarm optimization（BPSO）and support vector machines（SVM）is used to select the "optimal"lip feature *** subset was generated randomly firstly,and then BPSO algorithms searched the feature space guided by the result of SVMs’10-fold crossover *** numbers of iteration,the best fitness feature subset was selected out as the vector of lip *** Markov Model（HMM）with 4 states and 16 Gaussian mixture components is designed as a *** with feature fusion based on concatenating,Experiment results in a small database for speaker-dependent case showed that the recognition rates with feature selection based on BPSO and SVM are improved by as much as 3.89%.

关键词： feature selection binary particle swarm optimization support vector machines normalized geometrical feature normalized DCT coefficients hidden Markov model

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A New Fabric Defect Detection Model based on Summed-up Distance Matching Function and Gabor Filter Bank

A New Fabric Defect Detection Model based on Summed-up Dista...

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2018第3届联合国际信息技术，机械与电子工程学术会议(JIMEC 2018)

作者： Yihong Zhang Xin Ruan Shijie Pan Lifeng Shi Bao Zong College of Information Science &Technology DongHua University Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education

Focusing on the problem of low efficiency and high mistake rate for manual detection of fabric with periodic textures, a new fabric defect detection model based on summed-up distance matching function and optimal Gabor filter bank is proposed. Firstly, the frequency and scale parameters of the filter bank are accurately calculated by the summed-up distance matching function;for the direction parameter, the objective function is constructed by the mean and variance of the energy of the normal fabric images convolved by the Gabor filter bank, then the optimal direction is determined by particle swarm algorithm. The optimal filter bank is used to convolute the fabric image to be measured, and then the Otsu algorithm is used to accurately segment the convolution image. The experimental results show the method has a short learning time and good robustness, and can detect fabric flaws quickly and accurately.

关键词： periodic texture Gabor binary particle swarm optimization defect detection

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A New Approach to Determine the Optimal Number and Arrangement of Power Quality Monitors for Voltage Sag Detection

A New Approach to Determine the Optimal Number and Arrangeme...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Raif Aličić Senad Smaka Electricity Distribution Company ED Sarajevo Public Enterprise Electric Utility of Bosnia and Herzegovina Sarajevo Bosnia and Herzegovina Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781728138169

This paper presents a new approach to determine the optimal number and arrangement of power quality monitors (PQMs) for voltage sag detection. It is necessary to determine the optimal number and arrangement of PQMs since their installation at all buses in a network is an uneconomical option due to relatively high price of PQMs. The appropriate mathematical model, that describes the considered optimization problem, is created by using the concept of topological monitor reach area. A new definition of the cost function is presented in the paper in order to simultaneously determine the required number of PQMs and their best arrangement. Also, the effect of setting different values of monitor's coverage control parameter on the obtained results is analyzed. Four optimization methods are implemented to solve the considered problem: binary Bat Algorithm, binary Dragonfly Algorithm, binary particle swarm optimization and Genetic Algorithm. The presented approach is tested on the IEEE 34-node test system. Simulations proved that the binary Bat Algorithm has the best performance in terms of computational time, convergence and the probability rate of finding the global optimum.

关键词： binary optimization binary bat algorithm binary dragonfly algorithm binary particle swarm optimization Genetic algorithm Power quality monitors Topological monitor reach area Voltage sags voltage sags particle swarm optimization Genetic Algorithm (GA) Lexical permutation sorting MONITOR optimum number Power quality optimum performance Cost functions binary global optimum

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An Effective Algorithm of Sequential ATPG Based on Improved PSO

An Effective Algorithm of Sequential ATPG Based on Improved ...

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第二届国际测量仪器与测试自动化学术会议

作者： Wang Fei Zhang Ming Cao Ning (College of Electric Engineering,Hohai University,Nanjing 210098,China)

In this paper,the improved binary particle swarm optimization algorithm is introduced into test generation of sequential circuit to generate and optimize the test sequence set. Meanwhile,tree structure model of sequential circuit is proposed as the test *** parallel fault simulator is used to obtain the test sequences of sequential circuit in the ATPG *** the experiment results of the ITC'99 benchmark circuits,we can conclude that our ATPG algorithm of sequential circuit can get high fault coverage rate and short test sequence in short time,the performance is beyond ATPG algorithms based on other bionic optimal methods.

关键词： binary particle swarm optimization tree structure model test generation parallel fault simulation

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Hybrid multi-selection based on swarm intelligence and decision tree

Hybrid multi-selection based on swarm intelligence and decis...

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第六届中国传感器网络学术会议(CWSN 2012)

作者： XIONG Wen JIN Yao-hong Beijing Normal University Institute of Chinese Information ProcessingBeijing 100875China

To study the use of swarm intelligence (SI) to integrate feature selection and instance selection for pre-process data,and to boost the prediction accuracy of classifier on the reduced data in data mining (DM),this article puts forward a novel hybrid data pre-processing method based on SI and decision *** method uses the binary particle swarm optimization (PSO) as a subset generator to control the particles searching the optimal feature subset and instance subset,and employs a decision tree as a wrapper classifier to evaluate feature subsets and instance subsets at the same *** the method,the PSO algorithm takes the multi-selection problem as a combinational optimization problem with a reasonable computational cost,and makes full use of the advantages of the two kinds of data pre-processing methods to reduce the data from feature and instance dimensions,and to generate a more optimized classifier on the reduced *** show the method has attained lower prediction error ***,an average of feature reduction ratio 50.8% and an average of instance reduction ratio 36.8% are obtained at the same time on seven data sets from the University of California,Irvine (UCI),indicting the availability of the method.

关键词： swarm intelligence decision tree feature selection instance selection binary particle swarm optimization data pre-processing

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Hyperspectral Image Classification Based on Novel binary particle swarm with Mutation Mechanism for Band Selection

Hyperspectral Image Classification Based on Novel Binary Par...

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2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Lishuan Hu Qun Wang Tingyan Xing School of Information Engineering China University of Geosciences (Beijing) College of Urban Rail Transit and Logistics Beijing Union University

ISBN: (纸本)9781510863064

Hyperspectral remote sensing sensors can capture hundreds of narrow contiguous bands and provide plenty of valuable information. Duo to the high-dimension characteristics of hyperspectral data, band selection plays an important role in the field of Hyperspectral Image(HSI) classification. In this paper, a HSI classification method based on Novel binary particle swarm optimization with mutation mechanism(MNBPSO) for band selection is proposed. First, we present a thorough experimental study to show the superiority of the MNBPSO method. Then we introduce a pre-processing method for feature selection and parameters determination simultaneously based on MNBPSO. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach can select those bands with more discriminative information and improve the classification accuracy effectively.

关键词： binary particle swarm optimization band selection parameters determination mutation mechanism support vector machines

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RADAR ASSIGNMENT FOR STEALTH TARGETS DETECTION AND TRACKING BASED ON BPSO IN AIR-DEFENSE RADAR NETWORK

RADAR ASSIGNMENT FOR STEALTH TARGETS DETECTION AND TRACKING ...

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IET International Radar Conference

作者： Q.Liu Z. Liu R.Xie S. H. Zhou Y.F.Liu National Lab. of Radar Signal Processing Xidian Univ.

ISBN: (纸本)9781629931340

In this paper we consider the radar assignment problem for air-defense radar network to detect and track multiple stealth targets. For the purpose of selecting appropriate radar at appropriate time to optimize the detection and tracking performance under the given constraints, we propose a novel radar assignment algorithm based on posterior Cramér-Rao lower bound (PCRLB), binary particle swarm optimization (BPSO) and particle filtering (PF). A group of randomly generated particles are used to obtain the detection probability of newborn targets, while the tracking accuracy is measured by PCRLB. Moreover, the BPSO is adopted to search the surveillance region for the optimal activated radar subset. Finally, the PF outputs of activated radars are fused. Simulation results show that the proposed method can not only quickly identify newborn targets, but also can optimize the tracking performance of existing targets. Compared with traditional methods, the tracking accuracy of the radar network is improved.

关键词： air-defense radar network radar assignment posterior Cramér-Rao lower bound particle filter binary particle swarm optimization

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Optimal PMU Placement with binary PSO

Optimal PMU Placement with Binary PSO

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IEEE Energy Conversion Congress and Exposition

作者： J. Peppanen T. Alquthami D. Molina R. Harley Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

ISBN: (纸本)9781467308021

A growing number of Phasor Measurement Units (PMUs) are being installed throughout the power grid. Among other objectives, PMUs can be used in state estimation. Due to the high purchase price of a PMU and its high installation cost, a minimum number of PMUs is desired to make a given power network fully observable. This paper proposes a particle swarm optimization based method to find the optimal PMU locations in a given grid topology. This method was tested successfully with the IEEE 14-bus, 30-bus, and 68-bus systems as well as with a large portion of the Brazilian power system.

关键词： Phasor measurement unit binary particle swarm optimization system observability

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