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Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第16期43卷 3647-3662页

作者： Tian, Zhongda Shenyang Univ Technol Sch Artificial Intelligence Room 511111Shenliao West Rd Shenyang 110870 Peoples R China

In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

关键词： Short-term wind power prediction kernel principal component analysis echo state network improved particle swarm optimization algorithm

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Evaluation of the improved particle swarm optimization algorithm efficiency inward peer to peer video streaming

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COMPUTER NETWORKS 2018年第Sep.4期142卷 64-75页

作者： Golchi, Mahya Mohammadi Motameni, Homayun Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Streaming video in peer-to-peer(P2P) networks is always faced with many challenges. In most of video streaming methods, service quality and observing the video integration are less concentrated;which is resulted from the loss of suitable algorithms to identify optimized route for sending information between a pair of nodes. The dynamic structure of such networks needs an algorithm which beside its high speed and accuracy can find suitable path for sending data pieces that provide service quality parameters. Using the algorithm of improved particle swarm optimization(IPSO), the present paper provides a method for video streaming. The suggested method is based on the principle that in every moment, every particle adjusts its location in the search space according to the best location that has been placed in it so far as well as the best place in its whole neighborhood. For validity check of the suggested method, a simulation with MATLAB has been done. The results show that the suggested method is more efficient comparing to other methods as well as other meta-heuristic methods such as Genetic algorithm(GA). (C) 2018 Elsevier B.V. All rights reserved.

关键词： Peer-to-peer network The shortest path Video streaming Service quality improved particle swarm optimization algorithm

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Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches

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EVOLVING SYSTEMS 2017年第4期8卷 287-301页

作者： Moeini, R. Babaei, M. Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

In this paper, the improved particle swarm optimization (IPSO) algorithm is used to solve large scale reservoir operation optimization problem proposing unconstrained and two constrained versions of this algorithm. In the two constrained versions proposed for the IPSO algorithm, named PCIPSO and FCIPSO, each particle may be forced to satisfy problem constraints during solution building. By considering water releases or storage volumes at each operation time period as decision variable of the problem, here, two formulations are proposed for each version. In the second proposed constrained version algorithm (FCIPSO), at first, the water storage volume bounds are modified in order to recognize the infeasible components of the search space and exclude from the search process before the main search starts. This mechanism leads to smaller search space size for the problem and finally better results. The simple and hydropower operation problems of "Dez" reservoir in the southern Iran over 60, 240 and 480 monthly operations time periods are solved here using both proposed formulations of theses algorithms and the results are presented and compared with other available results. The results show the capability of the proposed algorithms and especially the second constrained version of the IPSO algorithm, FCIPSO, to optimally solve the reservoir operation optimization problem. In other words, the results of both formulations of constrained IPSO and especially FCIPSO algorithm are improved significantly in comparison with unconstrained IPSO algorithm over all operations time periods of simple and hydropower operation of the reservoir.

关键词： Optimal operation of reservoir Large scale problem Explicitly constraints handling improved particle swarm optimization algorithm Dez reservoir

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Scheduling multi-mode resource-constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2021年第2期3卷 93-104页

作者： Xiao, Xiangjie Pan, Yaohui Lv, Lingling Shi, Yanjun Dalian Univ Technol Mech Engn Dept Dalian Peoples R China

A modified particle swarm optimization (PSO) approach is presented for the multi-mode resource-constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple-AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi-mode resource-constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.

关键词： automatic guided vehicles scheduling particle swarm optimisation distribution tasks resource allocation allocable resources multimode resource-constrained task scheduling model improved PSO algorithm AGV system minimisation automated guided vehicle tasks material delivery time minimisation multiple-AGV combined distribution mode discrete particle swarm optimization algorithm goods distribution improved particle swarm optimization algorithm

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Study on Stochastic Assembly Line Balancing based on improved particle swarm optimization algorithm

Study on Stochastic Assembly Line Balancing based on improve...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Zhu Huabing Yu Feng Xi Yun Wang Long Zhang Juan Hefei Univ Technol Sch Mech & Automot Engn Hefei Peoples R China

ISBN: (纸本)9783037852866

Focusing on a particular assembly line balancing problem of which the task time is a stochastic variable, a stochastic model is established, which aimed at maximization of assembly line balancing rate, completed probability and smoothness index. Simultaneously, an improved particle swarm optimization algorithm is proposed to solve this problem and a reasonable chromosome coding method which effectively prevent to generate infeasible solution is designed. For this reason, the algorithm convergence rate could be improved. At last, rear axle assembly line balancing designs of an automotive part company is taken to test validity of algorithm. Availability of the algorithm is verified by this example.

关键词： Assembly line balancing improved particle swarm optimization algorithm Stochastic variable Chromosome coding

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Research on regional coordination control of urban road traffic based on improved particle swarm optimization algorithm 3

Research on regional coordination control of urban road traf...

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3rd International Conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Wen, Ling Dong, Baotian Li, Pengcheng Beijing Jiaotong Univ Transportat Beijing Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

In order to alleviate the pressure of urban road traffic, the coordinated control strategy of regional traffic signals is studied. Taking the maximum capacity of the traffic control sub-area as the optimization objective, the regional signal coordinated control model is constructed, and the particle swarm optimization algorithm is used to solve the problem. The performance of the particle swarm algorithm is improved by adding interference items, decreasing inertia weight and adjusting learning factors. The above model and algorithm are verified by the traffic flow data of the actual road network. The experimental results show that the improved particle swarm algorithm has faster convergence speed and higher solution accuracy, and the proposed signal control model can effectively improve the overall traffic capacity of the region.

关键词： Urban road traffic Regional coordination control strategy improved particle swarm optimization algorithm

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Research on Power Quality Data Placement Strategy Based on improved particle swarm optimization algorithm 39

Research on Power Quality Data Placement Strategy Based on I...

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39th IEEE International Performance Computing and Communications Conference (IPCCC)

作者： Wang, Chengdong Fang, Jun Zhao, Zhuofeng Zhao, Bo North China Univ Technol Sch Informat Beijing Peoples R China

ISBN: (纸本)9781728198293

For the national grid power quality monitoring system, the effective integration of monitoring terminals, the master stations of each network and the province and the state grid data center work together, the reasonable placement of the monitoring data in the system, and the relief of the calculation pressure of the state grid data center are the project research focus. From a global perspective, this paper models and describes the data placement problem of the harmonic monitoring system, and proposes a data placement strategy based on an improved particle swarm optimization algorithm. This paper proposes an initial population generation algorithm based on Markov random walk, which enables individuals in the initial population to have a certain degree of clustering accuracy and strong diversity. The initial population generation algorithm cooperates with the particle swarm optimization algorithm, which effectively enhances the algorithm's optimization ability. Through comparative experiments with traditional data placement strategies, the experimental results show that the data placement strategy based on improved particle swarm optimization algorithm has higher efficiency.

关键词： data placement strategy Markov random walk improved particle swarm optimization algorithm data transmission time

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Design of subsynchronous damping control based on improved particle swarm optimization algorithm by STATCOM

Design of subsynchronous damping control based on improved p...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Gu, Wei Li, Xingyuan Wang, Yuhong Sichuan Univ Dept Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781424448135

Series capacitors compensations may cause subsynchronous resonance (SSR) of steam turbine-generators. Using the principle which is depressed SSR by STATCOM and choosing appropriate measured data to design the subsynchronous damping control (SSDC) based on improved particle swarm optimization algorithm which is presented in this paper. Regard the controller parameter of STATCOM as adaptive parameter. Treating the damping ratio as objective function and basing on improved particle swarm optimization algorithm to damp SSR. Take the subsystem within Mohong and Yanchong series capacitors of China Southern Power Grid as example to verify the validity of SSDC.

关键词： subsynchronous resonance improved particle swarm optimization algorithm STATCOM subsynchronous damping control

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using improved particle swarm optimization algorithm 18

Determine the optimal switching angles symmetrical cascaded ...

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18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Ahmadi, Sajjad Sahnesaraie, Mostafa Andalib Talami, Saeid Hojjati Dini, Fakhteh Zanganeh, Mahnaz Mohebbi Ashgevari, Yazdan Islamic Azad Univ Dept Elect Engn Ardabil Sci & Res Branch Ardebil Iran Alborz Elect Power Distribut Alborz Iran Islamic Azad Univ Dept Elect Engn Mehriz Branch Mehriz Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran

ISBN: (纸本)9781728131498

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multi-level inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm improved particle swarm optimization algorithm

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Battery state-of-charge estimation based on fuzzy neural network and improved particle swarm optimization algorithm

Battery state-of-charge estimation based on fuzzy neural net...

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2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Lv, Jianxun Yuan, Haiwen Lv, Yingming Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

ISBN: (纸本)9780769549354

The battery state-of-charge (BSOC), which is a key reference for battery management, can not be measured directly. An estimation approach for the SOC of the individual lithium-ion battery cell used in applications where the battery load is very unstable is studied. The estimator model based on fuzzy neural network (FNN) is proposed, considering the battery terminal voltage, discharge current and battery surface temperature as inputs. Furthermore, an improved particle swarm optimization (PSO) algorithm borrowing the operation of selection and crossover from genetic algorithm (GA) is developed to train the FNN-based estimator model. This hybrid algorithm can incorporate the superiorities of the two heuristic optimization techniques. The results of simulation and experiment demonstrate that the improved PSO algorithm is more adaptive to the initial value of the parameters than the traditional training method using the back propagation (BP) algorithm. The FNN model trained by the improved PSO algorithm has better generalization capacity. At the same class of the training accuracy, higher accurate estimation value of BSOC can be obtained by employing the improved PSO algorithm. This study proposes an effective approach for estimating the BSOC.

关键词： battery state-of-charge estimation fuzzy neural network improved particle swarm optimization algorithm

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