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Cable Tension of Long-Span Steel Box Tied Arch Bridges Based on Radial Basis Function-Support Vector Machine Optimized by quantum-Behaved particle swarm optimization

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7163页

作者： Shi, Hongcai Shi, Menglin Xu, Weisheng Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China

To investigate hanger force during the construction phase of large-span steel box tie arch bridges, the challenge of low accuracy in force identification due to multifactor coupling was addressed. An energy method was employed to derive formulas for calculating forces under different boundary conditions. Utilizing the QPSO-RBF-SVM machine learning algorithm model, predictions of bridge formation stage forces were conducted, integrating findings from actual engineering case studies. Error analysis on hanger force was performed, revealing that the quantum particle swarm optimization (QPSO) algorithm optimizes parameters in the radial basis function support vector machine (RBF-SVM). The model was trained on datasets, achieving an average relative error of 0.65% in predicted cable force values compared with measured values in the test set, with a coefficient of determination of 0.97. These results demonstrate superior accuracy compared with calculations derived from the energy method and other machine learning algorithms. This algorithmic model presents a promising approach for accurately assessing cable forces in large-span steel box tie arch bridges.

关键词： tied arch bridges prediction of cable forces energy method quantum particle swarm optimization algorithm radial basis functions support vector machines

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State-of-health estimation of lithium-ion batteries based on QPSO-BPNN

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IONICS 2025年第2期31卷 1437-1449页

作者： Yao, Yongming Li, Fei Li, Haofa Liu, Junchi Wang, Xindi Li, Tianyu Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China

Lithium battery state of health (SOH) estimation is crucial to ensure the safe and reliable operation of the battery. To enhance the accuracy of lithium battery SOH estimation, a model for estimating the state of health of lithium-ion batteries based on quantum particle swarm optimization (QPSO) optimized backpropagation neural network (BPNN) was proposed. Initially, the degradation mechanism of lithium-ion batteries is analyzed. Subsequently, BPNN is utilized to learn from the database training samples, quantum particle swarm optimization is employed to determine the optimal connection threshold and weight, and the key parameters of the model are optimized by QPSO to improve the estimation accuracy of the model. Finally, the SOH estimation result of the lithium battery is obtained. The NASA public dataset was utilized to validate the model. The results indicate that the proposed model exhibits higher accuracy compared to prediction models utilizing standard particle swarm optimization, dung beetle algorithm, and seagull algorithm optimization BPNN on various lithium-ion datasets. The average absolute error, root mean square error, and average relative percentage error are maintained within 0.00805, 0.01140, and 1.30850%, respectively, demonstrating the effective enhancement of the estimation accuracy of lithium-ion battery SOH.

关键词： Lithium-ion batteries Health status estimates quantum particle swarm optimization algorithm BP neural network

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Simultaneous optimal location and sizing of DGs in distribution system considering different types of MGs in an electricity market

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2443-2459页

作者： Dashtaki, Amir Ali Hakimi, Seyed Mehdi Farahani, Elnaz Shahrabi Hassanzadehfard, Hamid Great Tehran Elect Distribut Co Tehran Iran Islamic Azad Univ Dept Elect Engn Damavand Branch Damavand Iran Islamic Azad Univ Renewable Energy Res Ctr Damavand Branch Damavand Iran Islamic Azad Univ Dept Elect Engn Miyaneh Branch Miyaneh Iran

The implementation of distributed generation (DG) units in power systems has been increasing in the last years. Among many of their advantages, voltage improvement and loss reduction can be obtained by the optimum inserting DGs into distribution networks (DNs). The present study suggests a methodology for optimization of DGs in the DN. Various types of microgrids are also considered to be connected to the DN and can inject electricity into the system in a deregulated electricity market. The proposed methodology provides the possibility of comparing, purchasing electricity from the microgrids, installing any distributed generation units, injecting electrical power from the main bus, as well as the penalty for environmental emissions, power losses, fuel consumption, and etc. The simulation results depict that as the microgrids are considered in a grid-connected mode in the distribution system, the power losses reduce from 1100 to 850 kW. In addition, if the DN is responsible for providing the loads of the MGs, the amount of fuel consumption is equal to 42.167 M-Liters during a year. In addition, there is about 27% reduction in the fuel consumption when the optimum interaction between the microgrids and the system are considered.

关键词： Electricity market Distributed generation Distribution system quantum particle swarm optimization algorithm

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Optimal Recursive Terminal Sliding-Mode Control Using Super-Twisting algorithm for Improving High Efficiency and Reliability of Pump Systems

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3385-3390页

作者： Nassiri, Samir Labbadi, Moussa Chatri, Chakib Cherkaoui, Mohamed Mohammed V Univ Rabat Mohammadia Sch Engineers Engn Smart & Sustainable Syst Res Ctr Rabat 10090 Morocco Aix Marseille Univ CNRS UMR 7020 LIS F-13000 Marseille France

In this letter, we propose an optimal recursive terminal sliding-mode control (ORTSMC) combined with super-twisting algorithm (STA) for a pump system under uncertainties. The main objective of the approach developed is to ensure rapid convergence of the pumping system with minimal power losses. To calculate the optimal input parameters of the pump system, a quantum particle swarm optimization algorithm (QPSO) is used. Next, we introduce a non-linear sliding variable into the cost function of the linear quadratic regulator (LQR). This proposal, along with the ORTSM manifold, aims to achieve fast convergence, dynamic stability, and minimize energy consumption. Additionally, the STA is employed to enhance performance during the reaching phase and reduce the chattering problem. The stability of the closed-loop control system is guaranteed using the Lyapunov theory. Finally, we conduct a comparative simulation analysis with two existing control schemes to demonstrate the superiority and effectiveness of our proposed control strategy.

关键词： Convergence Sliding mode control Robustness Mathematical models Uncertainty Steady-state Pumps quantum particle swarm optimization algorithm linear quadratic regulator

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Automatic Guided Vehicle Global Path Planning Considering Multi-objective optimization and Speed Control

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SENSORS AND MATERIALS 2021年第6期33卷 1999-2011页

作者： Song, Jing Guangzhou Maritime Univ Coll Port & Shipping Management Guangzhou 510725 Peoples R China

Over the past few decades, as the main tool of intelligent material transportation, automatic guided vehicles (AGVs) have been widely used in modern production systems, logistics, transportation, industry, and commerce to further improve productivity, reduce labor costs, raise energy efficiency, and enhance safety. Path planning is a key issue in the field of AGVs to ensure that they do not collide with obstacles during movement and reach the destination as fast as possible to complete the assigned task. We propose two different and crucial operating environments in this paper. More specifically, in a static environment, a multi-objective mathematical model is established with the shortest path and the maximum smoothness, and the improved Levy random quantum particle swarm optimization (LRQPSO) algorithm is used to solve the proposed model and screen the AGV's driving path. In a dynamic environment, an inductive steering algorithm (ISA) that considers the movement of obstacles is proposed to make the AGV avoid obstacles rationally. By combining the steering characteristics of the two environments, AGV speed control rules are set and applied to the steering process in complex environments to ensure that the AGV can travel more smoothly and quickly. Simulation results show that the proposed method can ensure the obstacle avoidance and flexible steering of an AGV, and improve the driving speed and work efficiency in the two environments. In addition, compared with the conventional algorithm, the smoothness, operation speed, and work efficiency of the AGV are significantly increased using the improved LRQPSO algorithm and ISA.

关键词： multi-objective optimization global path planning inductive steering algorithm quantum particle swarm optimization algorithm automatic guided vehicle

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Research on static fault-tolerant control method of thruster based on MPC

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2021年第3期26卷 861-871页

作者： Ding, Xuelian Zhu, Daqi Yan, Mingzhong Shanghai Maritime Univ Shanghai Engn Res Ctr Intelligent Maritime Search Haigang Ave 1550 Shanghai 201306 Peoples R China

To ensure that the unmanned underwater vehicles (UUVs) can still successfully complete the corresponding tasks in the case of thrusters having faults, a novel fault-tolerant control strategy which combines the model predictive control (MPC) algorithm and the fault-tolerant reconstruction algorithm is presented in this paper. Firstly, a cascade control method based on model predictive control algorithm is introduced, and then the problem when the thrusters in the faulty condition are discussed. For different degrees of thruster fault, the method of weighted pseudo inverse and quantum particle swarm optimization (QPSO) is used for hybrid fault-tolerant control (FTC). At the same time, to overcome the limitations brought by the pseudo inverse reconstruction algorithm, an optimization criterion with the infinite norm as the cost function is introduced into the QPSO algorithm to accelerate the search for the optimal solution in the feasibility space so as to ensure the feasibility of the solution. The simulation results show that the fault-tolerant control method proposed based on MPC (FTC-MPC) in this paper can provide ideal control effects for the unmanned underwater vehicles.

关键词： Model predictive control algorithm Fault-tolerant reconstruction Weighted pseudo inverse quantum particle swarm optimization algorithm Unmanned underwater vehicles

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Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1113-1124页

作者： Zhang, Lan Xu, Lei Xian Aeronaut Polytech Inst Xian Shaanxi Peoples R China

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

关键词： Multi-objective optimization problem diagonal recursive neural network quantum particle swarm optimization algorithm load forecast

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An Improved quantum particle swarm algorithm Optimized Regularized Extreme Learning Machine for Short-Term Load Forecasting

An Improved Quantum Particle Swarm Algorithm Optimized Regul...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Chen Yonglong Zhu Jinda Yang Dongmei Wang Xinping NARI Grp Corp Lab Integrated Energy Syst Simulat & Operat Contr Nanjing Peoples R China

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152813

In order to screen out the nonlinear relationship in massive load data more quickly and improve the accuracy of short-term load forecasting model, the genetic quantum particle swarm optimization algorithm (GAQPSO) is proposed to optimize the input weight and hidden layer deviation of the regularized extreme learning machine (RELM) based on the shortcomings of quantum particle swarm optimization algorithm (QPSO) in dealing with complex high-dimensional parameter optimization problems, which forms a hybrid short-term load forecasting model called GAQPSO-RELM. Meanwhile, when the input features are selected, the influences of historical load, temperature, time interval and type of date are fully considered, so the accuracy of the short-term load forecasting model is further improved. The experimental result shows that the proposed short-term load forecasting model has a higher accuracy than the QPSO-RELM model and the common RELM model. Besides, it can better reflect the changeable trend of the daily load curve, which verifies the effectiveness of the proposed predictive model.

关键词： genetic algorithm quantum particle swarm optimization algorithm regularized extreme learning machine short-term load forecasting

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An improved quantum particle swarm photovoltaic multi-peak mPPT method combined with Levy flight

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ENERGY SCIENCE & ENGINEERING 2020年第11期8卷 3980-3994页

作者： Chen, Lei Li, Zhijun Zhang, Yinan Zhang, Yi Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300130 Peoples R China North China Univ Sci & Technol Grad Sch Tangshan Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan Peoples R China

Inertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi-peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Levy flight strategy with DCWQPSO algorithm. Levy flight is a non-Gaussian random process. The algorithm introduces Levy flight strategy to change the mutation formula of particles and uses the characteristics of Levy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast-tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness.

关键词： hyperparameter Levy flight MPPT partial shading photovoltaic quantum particle swarm optimization algorithm

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Interference Suppression Control Method for Aircraft Electromechanical Speed Control System

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2022年第1期16卷 19-29页

作者： Zhang, Qian Guangdong Univ Educ Dept Comp Sci Guangzhou 510303 Peoples R China

In order to improve the stability and dynamic performance of the aircraft electromechanical speed control system, the interference suppression control method is proposed. Firstly, the interference factors of the speed regulation system are analyzed in detail, and then the chaotic mutation attractor is introduced into the quantum particle swarm optimization algorithm by using intelligent optimization method. The nonlinear model of the aircraft electromechanical control system is established to realize the tracking and identification of the parameters of the aircraft electromechanical speed regulation system. Accordingly, the PID control method is improved, and the interference suppression of aircraft electromechanical speed regulation system is realized. The simulation results show that the control method has high control accuracy, good dynamic and static characteristics, and also has good performance in resisting disturbance. The purpose of improving the transient stability of power system is realized. (C) 2022 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved

关键词： Aircraft electromechanical Speed control system quantum particle swarm optimization algorithm Interference suppression

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