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China International Conference on Electricity Distribution (CICED)

作者： Wang Ping Fan Lei Liang Ling Chen Feng Wang Guosong Wan Huijiang Zhou Siming Guizhou Power Grid Co Ltd Guiyang 550002 Guizhou Peoples R China NR Elect Co Ltd Nanjing 211102 Jiangsu Peoples R China

ISBN: (纸本)9781538667750

This paper proposes a multi-objective optimization model for the difference coefficient of generator excitation system to improve grid operation voltage level during multi-period and reduce grid loss. The minimum voltage fluctuation at power grid pilot point during multi-period and the minimized active grid loss are taken as the objective functions, while the power flow equations are used as the constraints. The particle swarm optimization (PSO) algorithm is employed to solve the optimization model. The effects of the difference coefficients of generator excitation system on power grid operation voltage are analyzed in detail. The simulation results indicate that the operating voltage level of power grid can be effectively improved and the grid loss can be significantly reduced.

关键词： difference coefficient generator excitation system particle swarm optimization algorithm power grid reactive power and voltage

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A Optimal Path Planning of Multi-destinations for Mobile Robot in Complex Environment

A Optimal Path Planning of Multi-destinations for Mobile Rob...

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Chinese Automation Congress (CAC)

作者： Liu, Ping Wang, Chunying Qin, Hongzheng Sun, Jinju Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9781728113128

A lot of progress has been made in the research of point-to-point and complete coverage path planning of mobile robots, while the multi-destinations path planning is seldom reported in the literatures. Based on particle swarm optimization and vortex search algorithms, this paper proposes a multi-destinations path planning approach, which is suited to plan a feasible, safe and optimal path between multi-destinations in complex home environment for mobile robot. Firstly, the sequence of the destinations is quickly optimized by using the particle swarm optimization algorithm. Then, the collision-free path between destinations is obtained via vortex search algorithm with its advantages of high efficiency and small computation. Finally, simulation results show that the proposed multi-destinations path planning approach has the good explorative and exploitation ability, while the path planned by proposed approach is smooth, short.

关键词： Multi-destinations optimization particle swarm optimization algorithm Vortex search algorithm Optimal path planning

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particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

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1st order nonstationary element optimization using PSO algorithm 23

1st order nonstationary element optimization using PSO algor...

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23rd International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Wiechetek, Katarzyna Kaszynski, Roman West Pomeranian Univ Technol Fac Elect Engn Szczecin Poland

ISBN: (纸本)9781538643259

The paper presents a new concept of establishing the varying function parameters in order to reduce the duration of the transition processes. The optimization was conducted using particle swarm optimization algorithm (PSO). The main object of the research was 1st order element with two outputs: a low-pass and a high-pass. By selecting the right values of the parameters a better operation and considerably shorter settling times were achieved. After applying multiobjective optimization algorithm the system could adapt to more requirements at once.

关键词： nonstationary filter time-varying parameters transient state multiobjective optimization particle swarm optimization algorithm

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Crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system 20

Crow search algorithm based optimal dynamic performance cont...

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20th National Power Systems Conference (NPSC)

作者： Kumar, Sudeep Kumar, Akshay Shankar, Gauri Indian Inst Technol ISM Elect Engn Dept Dhanbad Jharkhand India

ISBN: (纸本)9781538661598

A study on improvement of dynamic as well as steady state performance of power system model using static VAR compensator (SVC) is carried out in this paper. SVC refers to a shunt device within the family of flexible alternating current transmission system and is widely used for enhancing voltage profile and power transmission capability. SVC along with power system stabilizer (PSS) is employed to damp out the oscillation and take back the system to a stable state following disturbance. Crow search algorithm (CSA) is implemented to optimize conventional parameters of PSS and SVC. Simulation results found by it are compared with others optimization algorithms such as particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO). Also, power system model under the effect of CSA based tuned PSS and SVC is verified using eigenvalue analysis under steady state condition for investigating steady state stability. A comparative time domain simulation under MATLAB/SIMULINK platform employing CSA, PSO and TLBO tuned PSS and SVC with studied single machine infinite bus system is carried for dynamic stability assessment. Results obtained confirm the efficacy of the CSA in better tuning of PSS and SVC than other counterparts.

关键词： Crow search algorithm particle swarm optimization algorithm power system stabilizer static VAR compensator teaching-learning-based optimization algorithm

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Lifetime Enhancement of Wireless Sensor Networks by using MFO algorithm

Lifetime Enhancement of Wireless Sensor Networks by using MF...

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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538644911

Wireless sensor networks (WSNs) are extensively used in numerous applications from sensing and tracking to atmospheric quantity measurement. Sensor nodes used in the network are mostly battery powered and due to odd terrain of deployment it is not always easy to replace them. For managing this battery resource of the WSNs various schemes have been proposed and implemented. In this paper we have used Moth Flame optimization algorithm in the clustering and routing for enhancing the lifetime of the sensor network. Outcome through this algorithm are compared with the previously used various algorithms like particle swarm optimization, Genetic algorithm and Least Distance Clustering algorithm.

关键词： WSN Moth flame optimization particle swarm optimization algorithm PSO Genetic algorithm Clustering Routing

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Research on Hospital Operation Index Prediction Method Based on PSO-Holt-Winters Model 18

Research on Hospital Operation Index Prediction Method Based...

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Zheng, Tao Zhang, Ya Fan, Chenhao Shanghai Jiao Tong Univ Tongren Hosp Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Tongren Hosp Shanghai Peoples R China

ISBN: (纸本)9781450365123

The prediction(1) of hospital operation indicators is of great significance and can provide an important basis for hospital operation and management, so as to assist managers to make decisions such as resource allocation and task planning. In order to solve this problem, a novel Holt-Winters model based on particle swarm optimization (PSO) is proposed, aiming at the accurate prediction of hospital operating indicators. In the process of model construction, according to the characteristics of time series data of hospital operation indicators, a time decay mean square error function is constructed as an optimization function of particle swarm optimization algorithm, which enables particle swarm optimization algorithm to better fit recent historical data and grasp the characteristics of recent time series, so as to improve the prediction accuracy. An example is given to analyze the hospital operation index data of a third-class hospital from 2014 to 2017. By initializing the parameters of the model and optimizing the parameters, the improved PSO-Holt-Winters model of TDMSE-1 is established, which can accurately predict the outpatient, inpatient, emergency, discharged and surgical cases.

关键词： Hospital operation indicators particle swarm optimization algorithm Holt - Winters model

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Improved Optimal Path algorithm Based on Ant Colony algorithm

Improved Optimal Path Algorithm Based on Ant Colony Algorith...

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2019 4th International Conference on Automatic Control and Mechatronic Engineering(ACME 2019)

作者： Zhaohua Long Ruifang Dong Chongqing University of Posts and Telecommunications

Ant colony algorithm is based on Ant System(AS) and it is a very important group intelligence algorithm,which is used in many fields,but there are also some *** classical ant colony algorithm is analyzed and studied,and an improved P-ACS algorithm is proposed based on ACS algorithm in this *** analysis and experiment,it is found that although the performance of ACS algorithm is higher than AS algorithm,there are still some problems,such as:falling into local optimal solution,search stagnation,and slow initial *** important reason for the above problems is that the pheromone update can not accurately reflect the actual situation of the *** at this problem,a P-ACS ant colony algorithm is proposed based on particle swarm optimization algorithm(PSO).The algorithm optimizes the pheromone update strategy from three aspects:pheromone concentration range setting,initial pheromone setting and global update strategy improvement.

关键词： Ant colony algorithm ACS particle swarm optimization algorithm P-ACS

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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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