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2014 International Conference on Power and Energy(ICPE 2014)

作者： Z.H.LI Y Lin W.H.Chen D.H.You G.Wang M.L.Dong Q.R.Chen Y.Zou J.Hu Guangdong Power Grid Development Research Institute Co. Ltd State Key Laboratory of Advanced Electromagnetic Engineering and Technology of Huazhong University of Science and Technology

In order to remedy the defect that the determination of weight in fuzzy comprehensive evaluation is impacted by subjectivity, the combination weight is introduced. In this paper, the optimization Model combines the subjective weight and objective weight, which is solved by PSO, is built. At the same time, In order to give full consideration to the uncertain and fuzzy factors which involved in the grid planning, this paper combines the combination weight and fuzzy comprehensive evaluation to assess the grid-planning scheme. The calculation results of IEEE Garver-6 show that the proposed fuzzy comprehensive evaluation system is clear in theory and convenient to calculate, the combination weight is objective and reasonable as well as its results are scientific and intuitive.

关键词： grid planning scheme fuzzy comprehensive evaluation combination weight particle swarm optimization algorithm

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Optimal Active Power Loss with Feeder Routing Collaborate Distributed Generation Allocation and Sizing in Smart Grid Distribution

Optimal Active Power Loss with Feeder Routing Collaborate Di...

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2017经济金融与统计国际会议

作者： Phatcharasak Phawanaphinyo Narongdech Keeratipranon Chaiyaporn Khemapatapan College of Innovative Technology and Engineering Dhurakij Pundit University

The Smart Grid(SG) has widely supported the electric power from the Distributed Generation(DG). It becomes a practical standard to generate the electric power from a renewable energy into the distribution system to compensate for the power demand, especially in the peak time. However, the advancement of the SG continuing with the classical problem of active power loss as the traditional grid. This research aimsto solve the active power loss problem by analyzing the elements and study to solve, in the scope of the organization that provides the electrical power. In order to solve the problem, the solutions can be achieved by feeder routing with adjusting cost Dijkstra's algorithm, afterward decided the allocation and sizing of DG by using the Evolutionary Computing(EC) which are Harmony Search(HS), Artificial Bee Colony(ABC), and particle swarm optimization(PSO) algorithms. The experiment evaluates the performance of the algorithm usingpower flow analysis, Backward/Forward Sweep Method, on the IEEE 33-bus system. From the experimental result, the PSO provides the best performance. The overall active power loss in the case 3 DGs was reduced from 202.67 to 52.29 k W, representing 74.20%reduction.

关键词： Minimal active power loss Backward\Forward Sweep Method Harmony Search algorithm Artificial Bee Colony algorithm particle swarm optimization algorithm

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Application of Improved Wavelet Neural Network in MBR Flux Prediction

Application of Improved Wavelet Neural Network in MBR Flux P...

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International Conference on Computer and Information Science

作者： Guoshuai Cai Chunqing Li College of Computer Science and Software Tianjin Polytechnic University No. 399 Bin Shui Xi Road Xi Qing District Tianjin 300387 P.R. of China

ISBN: (纸本)9781509055081

Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning,the main components of the factors affecting the flux of MBR membrane were analyzed .The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the ***, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.

关键词： MBR Membrane flux particle swarm optimization algorithm Wavelet neural network BP neural network

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Design and optimization of Control Parameters Based on Direct-Drive Permanent Magnet Synchronous Generator for Wind Power System

Design and Optimization of Control Parameters Based on Direc...

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IEEE Conference on Industrial Electronics and Applications

作者： Lixia Sun Chengya Gong Fengling Han College of Energy and Electrical Engineering Hohai university Nanjing School of Computer Science and Information Technology

ISBN: (纸本)9781467363204

The direct-drive permanent magnet synchronous generator (DDPMSG) for wind power system uses a back-to-back double PWM converter. PI controller based on decoupling control strategies is used to control generator side converter and grid side converter. But the parameters of the PI controller are difficult to obtain correctly. Though manual tuning method is applied to regulate the parameters, the method would waste a lot of time and greatly depend on the experience. The paper analyses the mathematical model of direct-drive permanent magnet synchronous wind power generation system. It presents a particle swarm optimization (PSO) method for determining the parameters of PI controller for PMSG to improve the control ability. PSO is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. Under the condition of wind speed mutation, the simulation results of PMSG system after PI parameter optimization show that the PI control with PSO algorithm can fit the real value. The PSO controller has fast convergence rate, strong adaptability and good dynamic performance.

关键词： PMSG Manual Tuning Method particle swarm optimization algorithm Parameter optimization

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AN EFFECTIVE METHOD FOR RFID TAG ANTENNA optimization BASED ON ARTIFICAL NEURAL NETWORK

AN EFFECTIVE METHOD FOR RFID TAG ANTENNA OPTIMIZATION BASED ...

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2010 IEEE Youth Conference on Information,Computing and Telecommunications(2010 IEEE青年信息、计算和通信技术大会)

作者： Jiachuan Shang Ning Zhang Xiuping Li Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Millimeter Waves 210096 Nanjing

Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particle swarm optimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as an electromagnetic simulator and can be used for constructing the fitness function of PSO and GA. The PSO and GA optimizers are developed and executed in C++. Finally, this optimization method is turned out to be much more efficient than any electromagnetic simulator optimization. In addition, the PSO optimization results show that it is faster than GA.

关键词： optimization Tag Antennas particle swarm optimization algorithm Genetic algorithm Neural Network

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Based on PSO-BP Network algorithm for Fault Diagnosis of Power Transformer

Based on PSO-BP Network Algorithm for Fault Diagnosis of Pow...

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2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Hairu Li DaowuYang ZhuoRen ZhewenLi School of Chemical and Biological Engineering Changsha University of Science and Technology Changsha Ultrahigh Voltage Power Transmission and Transformer Company Changsha 410100 P.R.China

ISBN: (纸本)9781424479573

Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is proposed in this paper. The particle swarm optimization (PSO) technique is used to integrate with Back Propagation (BP) neural networks, and using particle swarm to optimize the network's weights and biases, the fault of transformers is simulated and discussed. The results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio method. So the algorithm based on PSO-BP network model provides a more accurate, safe and reliable result for the fault diagnosis of transformers.

关键词： component transformer fault diagnosis dissolved gas-in-oil analysis particle swarm optimization algorithm

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Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM

Short Term Load Forecasting and Early Warning of Charging St...

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作者： Liao Xiaoqun Kang Xiaofan Ma Li Cao Nanlan Xi’an University of Science & Technology

In this paper,a short-term load forecasting model and a load early warning model for charging station based on PSO-SVM are *** swarm optimization(PSO) is used to optimize the parameters of support vector machine(SVM) model,and the PSO-SVM load forecasting model for the optimal nuclear parameters of charging station is established according to the normalized root mean square error(NRMS).On the basis of it,a load warning model of charging station is established and verified by an *** show that the short-term load forecasting model based on PSO-SVM and the load forecasting model of charging station meet the requirements of forecasting and forecasting accuracy.

关键词： Load Forecasting Optimal Kernel Parameters Support Vector Machine particle swarm optimization algorithm Load Early Warning

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Establishing an emergency communication network and optimal path using multiple autonomous rover robots

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第14期31卷 e4636-e4636页

作者： Sakthitharan, S. Jayashri, S. Adhiparasakthi Engn Coll Fac Informat & Commun Engn Melmaruvathur 603319 India

The natural calamity or disaster may destroy all communication networks especially a cellular network that relies on a tower. Although many solutions to an ad hoc wireless network have been proposed, forming a network covering a respective region with mobile robots toward optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the optimal network coverage and path selection in disaster region with the help of multiple movable/rover robots. This paper consists of load balance distribution algorithm and optimal coverage algorithm applied to find the next optimally possible node location for all robots. Next, the robots maneuvering in an unknown disaster environment to identify the optimal path between the source and destination by using a particle swarm optimization algorithm. Finally, simulated results show that the algorithms can significantly improve the network coverage in the entire region, and the optimal path can effectively identify the optimal solution for all rover robots.

关键词： autonomous maneuvering disaster management load balancing algorithm optimal coverage algorithm particle swarm optimization algorithm

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Study on Comprehensive optimization Method in the Performance of a New Type of High-speed USV and Its Analysis

Study on Comprehensive Optimization Method in the Performanc...

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2016 International Conference on Modeling, Simulation and optimization Technologies and Applications（MSOTA 2016）

作者： Zifan Wei Songlin Yang Shengping Jing Man Liu Jiangsu Modern Shipbuilding Technology Co Ltd Jiangsu University of Science and Technology

ISBN: (纸本)9781510835702

Taking the comprehensive performance of a new type of high-speed unmanned surface vehicle(USV) including its rapidity, maneuverability, seakeeping and overturning resistance into consideration, this paper establishes a mathematical model of the performance comprehensive optimization of high-speed USV. And according to the mathematical model and optimization algorithm, comprehensive optimization software based on a variety of optimization algorithms was designed and compiled to analyze the relevant calculation. The results indicates that the optimization effect of the growth mechanism is more superior to the roulette mechanism;genetic algorithm of growth mechanism and particle swarm optimization algorithm would have the best results in the optimization of the 5000 th generation, and it can get the rapidity optimization system had greater influence on the total optimization system;and what's more, the optimal values of hull length and other key design variables and partial parameters of different algorithms are obtained.

关键词： high-speed USV comprehensive optimization genetic algorithm particle swarm optimization algorithm key variables

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RESEARCH ON INTRUSION DETECTION OF SVM BASED ON PSO

RESEARCH ON INTRUSION DETECTION OF SVM BASED ON PSO

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： TIE-JUN ZHOU JIA LI YANG LI School of Computer and Communication Hunan University Changsha 410082 China Computer Science College Central South University of Forestry and Technology Changsha 410004 Chi

Intrusion detection plays more important role in network security today. This paper introduces a method, particle swarm optimization and support vector machine, to intrusion detection system, and presents a new design of ID Based on particle swarm optimization and Support Vector Machine. This paper presents an optimal selection approach of the SVM parameters(regulation parameter C and the radial basis function width parameter σ ) based on particle swarm optimization algorithm. The experiments show that the optimal parameter selection approach based on PSO is available and the Research of Intrusion Detection Based on particle swarm optimization and Support Vector Machine is effective in reducing the number of alerts, false positive, false negative better.

关键词： Intrusion detection particle swarm optimization algorithm Support vector machine Parameter optimization

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