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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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The Influence of the Solar Flywheel Energy Storage Control System Based on the Improved Disturbance Observation Method on the Improvement of New Energy Efficiency 5

The Influence of the Solar Flywheel Energy Storage Control S...

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5th International Conference on Environmental Science and Material Application (ESMA)

作者： Zhu, Leyu North China Elect Power Univ Baoding Baoding Hebei Peoples R China

The purpose of this study was to improve the utilization efficiency of solar energy, and accelerate the innovation and development of solar flywheel energy storage control system, so as to enhance the utilization value of new energy. Firstly, the definition and working method of flywheel energy storage control system were described, and the characteristics of flywheel energy storage control system were introduced in detail. Secondly, the concept and working principle of solar flywheel energy storage control system were introduced, and the principle and calculation method of perturbation and observation method (P&O) were described in detail. Finally, the improved P&O based on particle swarm optimization (PSO) algorithm was described in detail, and its principle and operation methods were explained. The research results show that the biggest characteristic of flywheel energy storage device is that it has no pollution to the environment, the energy stored per unit area is large, and the efficiency of converting it into electric energy is very high, which can be used for a long time. The working property of solar energy storage system is the introduction, maintenance, and output of electric energy. In order to obtain the best tracking data, the flywheel energy storage control system must control the disturbance amplitude and time accurately and make a comprehensive choice. The P&O improved by PSO algorithm is applied to the flywheel energy storage control system, and the experience time to reach the maximum efficiency position is relatively shortened. When the system's derived power reaches the balance, the variation of wave shape in the improved dynamic observation method is also relatively reduced.

关键词： Solar flywheel energy storage control system particle swarm optimization algorithm disturbance observation method new energy

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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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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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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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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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Multi-objective optimization model and algorithm implementation of the distributed power generation system for renewable energy in China and Russia

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Unconventional Resources 2025年 7卷

作者： Ma, Yingkai Peoples' Friendship University of Russia Russian Federation

This study focuses on solving multi-objective optimization problems in distributed power generation systems (DPGS) for renewable energy in China and Russia, including low economic efficiency, poor environmental benefits, and insufficient system reliability. It proposes a hybrid optimization model that integrates deep learning with an improved particle swarm optimization algorithm, namely Adaptive Linear Decreasing Inertia Weight particle swarm optimization with Mutation Strategy (ALD-MPSO). By introducing a Dense Bidirectional Long Short-Term Memory with Attention Mechanism (DBI-LSTM-AM) model, which combines a Bidirectional Long Short-Term Memory (Bi-LSTM) network, Dense layers, and an Attention Mechanism (AM), the model performs time-series forecasting of energy demand. Coupled with the ALD-MPSO algorithm, the model simultaneously optimizes economic efficiency, environmental benefits, and system reliability. The study designs a renewable energy prediction and optimization model for DPGS, based on the fusion of the DBI-LSTM-AM and ALD-MPSO algorithms (DBI-LSTM-2AM-PSO). Finally, the model's performance is evaluated. Experimental results show that the proposed model achieves superior prediction accuracy (95.53 %), with an F1 score of 91.41 %, and a mean squared error (MSE) of 0.049, outperforming the benchmark algorithms. Additionally, the fitness value in MOO is reduced to 0.47, with a training time of only 25.7 s and low computational resource consumption (Center Processing Unit usage at 10.55 %). This study provides effective technical support for the intelligent management of DPGS in the renewable energy sectors of China and Russia. © 2025 The Authors

关键词： Deep learning Distributed power generation system Multi-objective optimization particle swarm optimization algorithm Renewable energy

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Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm

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FUEL 2019年 253卷 189-198页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Nadian, Mohammad Hossein Davoodnia, Vandad Soltanian, Salman Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran Univ Teknol MARA Fac Plantat & Agrotechnol Shah Alam 40450 Selangor Malaysia Biofuel Res Team BRTeam Karaj Iran AREEO ABRII Microbial Biotechnol Dept Karaj Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran

In-depth knowledge on pyrolysis behavior of lignocellulosic biomass is pivotal for efficient design, optimization, and control of thermochemical biofuel production processes. Experimental thermogravimetric analysis (TGA) is usually employed to peruse the pyrolysis kinetics of biomass samples. In addition to that, the main constituents of biomass (i.e., cellulose, hemicellulose, lignin) as well as the process heating rate can excellently reflect its pyrolysis characteristics through modeling techniques. However, the application of statistical and phenomenological models for extremely complex and highly nonlinear phenomena like lignocellulose pyrolysis is challenging. To address this challenge, adaptive network-based fuzzy inference system (ANFIS) was consolidated with particle swarm optimization (PSO) algorithm to prognosticate the kinetic constants of lignocellulose pyrolysis. More specifically, the PSO algorithm was applied to tune membership function parameters of the ANFIS model. Three ANFIS-PSO topologies were designed and trained to estimate the kinetic constants of lignocellulose pyrolysis, i.e., energy of activation, pre-exponential coefficient, and order of reaction. The input variables of the developed models were biomass main constituents and the process heating rate. The developed models could predict the kinetic constants of lignocellulosic biomass pyrolysis with an R-2 > 0.970, an MAPE < 3.270%, and an RMSE < 5.006. The pyrolysis behaviors of three different biomass feedstocks (unseen data to the developed models) were adequately prognosticated with an R-2 > 0.91 using the developed models, further confirming their fidelity. Overall, the lignocellulose pyrolysis behavior could be reliably and accurately estimated using the trained ANFIS-PSO approaches as an alternative to the TGA measurements. In order to make practical use of the trained models, a handy freely-accessible software platform was designed using the selected ANFIS-PSO models for approximati

关键词： Adaptive network-based fuzzy inference system Lignocellulosic biomass particle swarm optimization algorithm Pyrolysis kinetics Thermogravimetric analysis

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A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2063-2071页

作者： Hajizadeh, Ehsan Mahootchi, Masoud Esfahanipour, Akbar Massahi, Mandi Kh Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

An accurate estimation of exchange rate return volatility is an important step in financial decision making problems. The main goal of this study is to enhance the ability of GARCH-type family models in forecasting the Euro/Dollar exchange rate volatility. For this purpose, a new neural-network-based hybrid model is developed in which a predefined number of simulated data series generated by the calibrated GARCH-type model along with other explanatory variables is used as input variables. The optimum number of these data series and other parameters of the network are tuned by an efficient particle swarm optimization algorithm. Using two datasets of real Euro/Dollar rates, how the proposed hybrid model could reasonably enhance the results of GARCH-type models and the traditional neural network in terms of different performance measures is demonstrated. We also illustrate how the respective simulated data series as the input variable to the network could contribute to improve the accuracy of volatility forecasting.

关键词： Exchange rate volatility GARCH models Artificial neural networks particle swarm optimization algorithm Simulated data series

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