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47th IEEE Photovoltaic Specialists Conference (PVSC)

作者： Ding, Kun Chen, Xiang Zhang, Jingwei Li Feng Hamelmann, Frank U. Weng, Shuai Hohai Univ Coll Mech & Elect Engn Changzhou Peoples R China Univ Appl Sci Bielefeld Solar Comp Lab Minden Germany Changzhou Key Lab Photovolta Syst Integrat & Prod Changzhou Peoples R China

ISBN: (纸本)9781728161150

In order to improve the accuracy of short-term photovoltaic (PV) power prediction, the PV array power prediction model based on ensemble empirical mode decomposition (EEMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization algorithm (PSO-KELM) is proposed in this paper. The EEMD algorithm is used to decompose the power sequence of the PV array into a series of intrinsic mode functions (IMFs) with different features, the IMFs with similar features are divided into new components by sample entropy (SE), and establishing the KELM power prediction model corresponding to the new component. The PSO is used to optimize the output weight of KELM, seeking the optimal solution of upper and lower limits, and improving the performance of KELM. The actual data is used to compare the proposed prediction model with the traditional back-propagation (BP) and extreme learning machine (ELM) prediction models. The results validate that the proposed model can predict power of PV array with good accuracy and calculation speed.

关键词： short-term PV power prediction ensemble empirical mode decomposition sample entropy particle swarm optimization algorithm kernel extreme learning machine

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Optimized Design of Steam Turbine Control System

Optimized Design of Steam Turbine Control System

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Chinese Automation Congress (CAC)

作者： Yao, Yuan Gong, Xin Xin, Huang Zhao, Luping Song, Shengjun Liaoning Elect Power Co Ltd Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728176871

The steam turbine regulating system is an important link of a steam turbine generator unit The operating effect of the steam turbine is determined by the quality of control of the regulating system. In this paper, a particle swarm algorithm and fuzzy PID control are combined according to the regulation principle and characteristics of a steam turbine digital electro-hydranlic regulation system(DEM. Respective advantages of two control methods are taken to control the parameters in a loop and regulate the control quality of the system in real time according to the operation deviation change of the DEH control system. Finally, the system is better controlled.

关键词： DEH PID control particle swarm optimization algorithm fuzzy control

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A Study on Fault Diagnosis of Rotating Machinery Combined Wavelet Transform with VMD 4

A Study on Fault Diagnosis of Rotating Machinery Combined Wa...

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4th International Conference on Electrical, Automation and Mechanical Engineering (EAME)

作者： Zhou, Huan Wang, Hao Shanghai Univ Elect Power Machinery & Energy Engn Coll Shanghai 200090 Peoples R China

Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault diagnosis method is proposed based on particle swarm optimization (PSO) and variational modal decomposition (VMD). Firstly, wavelet packet threshold is denoised on the signal, VMD is decomposed on the reconstructed signal, and PSO is optimized on the inherent mode function (IMF) obtained from decomposition so as to determine the best IMF function. Then Hilbert transform and envelope spectrum analysis are carried out on the IMF function, and the envelope spectrum analysis result is compared with theoretical calculation frequency to finally determine the fault type. The results indicate that this method can effectively reduce noise components in signals, extract weak fault information and realize fault diagnosis.

关键词： Wavelet packet denoising VMD IMF particle swarm optimization algorithm Rotating machinery

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Numerical Analyses of Three Inertia-weight-improvement-based particle swarm optimization algorithms 2

Numerical Analyses of Three Inertia-weight-improvement-based...

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2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Chen, Jie Ye, Fang Jiang, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538620304

As one widely applied swarm intelligent algorithm, particle swarm optimization (PSO) algorithm has obtained the attention of various scholars with its advantages of easy implementation, high precision and fast convergence. Firstly, aiming to solve the problems that PSO has low searching speed and PSO is easy to fall into local optimal solution especially when dealing with high-dimension model, this paper modifies the PSO from the point of inertia weight improvements. The three improved inertia weights separately update the searching speed of particles with different principles, which are called linear-decline inertial weight, stochastic inertial weight and adaptive inertia weight. Then, three problems of evaluation non-linear function extremum are adopted in the simulation part to verify the convergence speed and optimization ability of three modified PSO algorithms. The numerical analyses demonstrate that these three improved inertia weights can guide particles to find the optimal solution more precisely and quickly. Thus, three proposed inertia-weight-improvement-based PSO algorithms have certain significance.

关键词： particle swarm optimization algorithm inertia weight convergence analyses

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Integrated energy systems planning with electricity, heat and gas using particle swarm optimization

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ENERGY 2019年 188卷

作者： Qin, Chao Yan, Qingyou He, Gang North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China SUNY Stony Brook Coll Engn & Appl Sci Dept Technol & Soc Stony Brook NY 11794 USA

An integrated energy system combines the power grid, natural gas pipeline, district heating network, and renewable energy generation to enhance the integration of renewable energy and smooth the load demand profile. However, the system faces great uncertainty derived from flexible renewable generation and demand load, etc. This paper brought in the robust optimization theory, considered the wind power integration on the supply side and the load fluctuation on the demand side. It also combined the constraints coming from the power grid, natural gas pipeline and heating network. We constructed a multi objective robust optimization model for integrated energy system, based on minimizing the fuel cost, the wind power curtailment and the variance of peak-valley electrical load on the end-user side, as the objection functions. To solve the global optimal solution of the model, particle swarm optimization algorithm is utilized because of its fast convergence speed. Tianjin was selected as an example to demonstrate the model. Results indicated that, in the scenario of government promoting electricity substitution, the ratios of energy conversion have been optimized. For instance, in recent years, the shares of outsourced electricity, power to heat, and gas to heat are gradually improved toward the optimization results (31.29%, 16.49%, 13.56%). However, the results also implied that the thermal power generation input-output in thermal power plants (heat to power) should be increased, and the ratio of generation from gas-fired units (gas to power) need to be steadily adjusted. The optimization results provide a good reference for the energy investment strategy. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Integrated energy system Uncertainty analysis Energy conversion particle swarm optimization algorithm Robust optimization

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Adaptive Weighted Hybrid PSOGSA algorithm and its Application in Power System Economic Load Dispatch Problem 32

Adaptive Weighted Hybrid PSOGSA Algorithm and its Applicatio...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Pu, Xingcheng Zhao, Longlong Xiong, Chaowen Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

ISBN: (纸本)9781728158556

Gravity Search algorithm (GSA) is a swarm intelligence optimization algorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The particle swarm optimization (PSO) algorithm has high convergence speed and search efficiency. Based on the advantages of the above two algorithms, a hybrid algorithm(PSOGSA) is proposed in this paper, and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid algorithm. At the same time, we added variable mutation probability to solve the problem that particles are easily be trapped in local optimum. In order to verify the effectiveness of the two improved hybrid algorithms, the two algorithms are applied to the power system economic load dispatch (ELD) problem. Power generation cost optimization performance tests are computed for three groups of power systems with different unit numbers. The simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.

关键词： Adaptive Weighted Power System Economic Load Dispatch Problem particle swarm optimization algorithm Gravity Search algorithm

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Design of Digital PID Controllers using particle swarm optimization: A Video Based Teaching Experiment

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IFAC-PapersOnLine 2018年第4期51卷 298-303页

作者： Oliveira, P.B. de Moura UTAD-Universidade de Trás-os-Montes e Alto Douro Escola de Ciências e Tecnologia Vila Real5000-801 Portugal

The use of videos is a valuable and powerful tool which may significantly contribute to change and improve teaching and learning methods. Lecturers can made their own videos addressing specific topics suitable to fulfill their student's needs. These videos can address control engineering syllabus as well as complementary topics. This paper proposes using video as a tool to introduce the particle swarm optimization algorithm to students within a digital PID control simulation experiment. The experience preliminary results and feedback received from students are quite positive. © 2018

关键词： Three term control systems Computer graphics E learning particle swarm optimization (PSO) Students Teaching Digital PID controllers Digital PID controls Internet based learning particle swarm optimization algorithm swarm optimization Teaching and learning methods Use of video Videos

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Research on Load and Mutual Inductance Identification Method of WPT System Based on a LCC-S Type Compensation Network

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WORLD ELECTRIC VEHICLE JOURNAL 2021年第4期12卷

作者： Xue, Ming Lu, Keyan Zhu, Lihua Tiangong Univ Tianjin Key Lab Adv Technol Elect Engn & Energy Tianjin 300387 Peoples R China

The identification of load and mutual inductance parameters of a wireless power transfer system can make the mathematical model of the system more accurate, which can effectively avoid system errors due to parameter uncertainties in the implementation of control, and provide theoretical support for system interoperability and high efficiency. This paper uses the two-port theorem and fundamental wave analysis to establish the identification model and obtain the relationship between inverter output current and load and between mutual inductance and load based on the equivalent circuit of a LCC-S magnetically coupled wireless power transfer system. To make the identification results more accurate, a particle swarm optimization algorithm with weights is introduced to transform the parameter identification problem of the system into an optimization problem, which can obtain the identification method of the system load and mutual inductance parameters. Both simulation and experimental results verify the feasibility and effectiveness of the method.

关键词： wireless power transfer (WPT) load identification mutual inductance identification LCC-S type compensation particle swarm optimization algorithm

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Improving performance of open-pit mine production scheduling problem under grade uncertainty by hybrid algorithms

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Journal of Central South University 2020年第9期27卷 2479-2493页

作者： Kamyar TOLOUEI Ehsan MOOSAVI Amir Hossein BANGIAN TABRIZI Peyman AFZAL Abbas AGHAJANI BAZZAZI Department of Petroleum and Mining Engineering South Tehran BranchIslamic Azad UniversityTehranIran Department of Mining Engineering University of KashanKashanIran

One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s *** the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit *** and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this *** the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling *** the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational *** the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade *** is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the ***,the Lagrangian coefficients are updated by using PSO and *** presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian *** results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid ***,it is more effectual than the conventional ***,the time and cost of computation are diminished by the proposed hybrid *** CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.

关键词： open-pit mine long-term production scheduling grade uncertainty augmented Lagrangian relaxation particle swarm optimization algorithm bat algorithm

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Optimal Tuning Of PID Controller For Switched Reluctance Motor Speed Control Using particle swarm optimization

Optimal Tuning Of PID Controller For Switched Reluctance Mot...

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International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT)

作者： Nimisha, K. K. Senthilkumar, R. Vimal Jyothi Engn Coll Dept EEE Power Elect Chemperi Kerala India Vimal Jyothi Engn Coll Dept EEE Chemperi Kerala India

ISBN: (纸本)9781538607961

This paper describes the Design of controllers for Switched Reluctance Motor using technique such as particle swarm optimization. Conventional PID controller is nowadays used in most Engineering being acknowledged its ability to give up a superior control in power electronic system. The purpose of this work is to design a speed controller for the motor to achieve minimum time domain integral squared error. This work concentrates mainly on the design of feedback PID controller to achieve the minimum integral squared error and hence the controller parameters k(p), k(i), and k(d) are identified. It can be done through PSO algorithm. The model of a converter works along with the algorithm which results with a robust feedback PID controller was developed using MATLAB/SIMULINK software.

关键词： Active boost converter Switched Reluctance Motor PID controller particle swarm optimization algorithm

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