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Multi-Objective Optimal Control Method for the 6-DOF Robotic Crusher

APPLIED SCIENCES-BASEL 2024年第20期14卷 9397页

作者： Duan, Guochen Yao, Lele Zhan, Zhanyu Kang, Tao Guo, Chaoyue China North Vehicle Res Inst Beijing 100072 Peoples R China

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher.

关键词： 6-DOF robotic crusher multi-objective optimization fuzzy multiple attributes decision making (FMADM) grey wolf optimization algorithm

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Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

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ENERGY 2024年 304卷

作者： Sun, Xiaodong Chen, Zongzhe Han, Shouyi Tian, Xiang Jin, Zhijia Cao, Yunfei Xue, Mingzhou Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

关键词： Energy management strategy Equivalent consumption minimization strategy (ECMS) Plug-in hybrid electric bus (PHEB) grey wolf optimization algorithm

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Optimal Utilization of Reactive Power Capabilities of Distributed Solar PV Inverter 22

Optimal Utilization of Reactive Power Capabilities of Distri...

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22nd National Power Systems Conference (NPSC)

作者： Tyagi, Arjun Nazir, Mohsina Kumar, Krishan Tyagi, Vineet Veer Kumar, Bhavnesh Netaji Subhas Univ Technol Dept Elect Engn New Delhi India Shri Mata Vaishno Devi Univ Sch Energy Management Katra Jammu & Kashmir India Trinity Touch Pvt Ltd New Delhi India Netaji Subhas Univ Technol Dept Instrumentat & Control Engn New Delhi India

ISBN: (纸本)9781665462020

To improve energy security and mitigate climate changes, renewable energy particularly solar energy is being significantly integrated with the power grid. Solar PV inverter, used for conversion of generated DC power into AC power has the ability to supply active and reactive power. Traditionally, only active power ability of solar inverter is being used. Although, the reactive power capability of the solar inverter can also be utilized for the maximization of benefits. In this study, the reactive power ability of the solar inverter has been used for minimizing power loss and enhancing the loadability limit of the distribution system. The loadability limit has been maximized by utilizing a new voltage-based stability indicator. New voltage-based stability indicator overcomes the computational complexity problem of unbalanced continuous power flow. Moreover, in the optimization, a grey wolf optimization algorithm has been used and the proposed methodology is applied and verified with IEEE-33 bus test system.

关键词： grey wolf optimization algorithm Loadability limit Power loss Solar PV inverter

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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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Effective virtual inertia control using inverter optimization method in renewable energy generation

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ENERGY EXPLORATION & EXPLOITATION 2021年第6期39卷 2103-2125页

作者： Niu, Shuanbao Qu, Linan Lin, Hsiung-Cheng Fang, Wanliang Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Northwest Branch State Grid Corp China Xian Peoples R China China Elect Power Res Inst Nanjing Nanjing Peoples R China Hebei Univ Technol Sch Elect Engn Tianjin Peoples R China Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

关键词： Virtual synchronous generator renewable energy generation grey wolf optimization algorithm frequency regulation parameter optimization

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Robust multi-user detection based on hybrid grey wolf optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第15期33卷 e5273-e5273页

作者： Sun, Xiyan Fan, Zhuo Ji, Yuanfa Wang, Shouhua Yan, Suqing Wu, Sunyong Fu, Qiang Ghazali, Kamarul Hawari Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Malaysia

The search for an effective nature-inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi-user detection algorithm based on grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi-user detection under an impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.

关键词： differential evolution algorithm grey wolf optimization algorithm hybrid optimization multi-user detection impulse noise

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Intelligent Temperature Control Method of Instrument Based on Fuzzy PID Control Technology

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 927-936页

作者： Li, Wenfang Wang, Yuqiao Huanghe Sci & Technol Univ Fac Engn Zhengzhou 450063 Peoples R China

The current instrumentation intelligent temperature control is generally realized based on PID control technology, whose efficiency and precision are low and cannot meet the actual production requirements. A fuzzy PID (FPID) control technique is suggested as a solution to this issue with the goal to increase the control precision by adjusting the PID parameters in real-time using a fuzzy algorithm. In addition, a multi -strategy -fused Improved grey wolf optimization (MGWO) algorithm is used to obtain the optimal fuzzy rule parameters for the fuzzy controller to achieve the optimization of FPID. In addition to the aforementioned, the MGWO-FPID-based instrumentation intelligent temperature control model is created to enhance the instrumentation's ability to regulate temperature. The testing results demonstrated that the MGWO-FPID model outperformed the other two models with values for the objective function of 5 10-8, adaptation degree of 13.1, control regulation time of 2.08 s, F1 value of 96.14%, MAE value of 8.53, Recall value of 95.37%, and AUC value of 0.995. The above results prove that the MGWO-FPID-based instrumentation intelligent temperature control model proposed in the study has high accuracy and efficiency, which can effectively realize the instrumentation intelligent temperature control in industrial production, and then improve the accuracy and efficiency of instrumentation temperature control, ensure the safe production of industry, and promote the industrial development to a certain extent. This model can monitor and regulate the temperature in the industrial production process in real time, avoiding safety accidents caused by temperature anomalies, and ensuring the safety of industrial production. And the application of this model can improve the efficiency and product quality of industrial production, help reduce production costs and improve economic benefits. This can not only promote the development of related industries, but also drive the econo

关键词： Fuzzy PID control instrumentation intelligent temperature control differential negative feedback grey wolf optimization algorithm

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Energy-saving optimal scheduling under multi-mode "source-network-load-storage" combined system in metro station based on modified Gray wolf algorithm

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ARCHIVES OF ELECTRICAL ENGINEERING 2024年第1期73卷 121-143页

作者： Tian, Jingjing Qian, Yu Zhao, Feng Mo, Shenglin Xiao, Huaxuan Zhu, Xiaotong Liu, Guangdi Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Minist Educ Key Lab Optotechnol & Intelligent Control Lanzhou Peoples R China

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi -modal "source-network-load-storage" system in metro stations. The proposed method, called the Improved Gray wolf optimization algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the "sourcenetwork-load-storage" joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray wolf optimization algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

关键词： bi-level optimization grey wolf optimization algorithm multi-mode peak shaving and valley filling source-network-load-storage

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CONSTRUCTION AND APPLICATION OF A BEARING FAULT DIAGNOSIS MODEL BASED ON IMPROVED GWO algorithm

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Diagnostyka 2024年第3期25卷

作者： Jiang, Lingbo School of Mechanical and Electronic Engineering Shandong Vocational College of Industry Zibo256414 China

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification. At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved, making the proposed method feasible in bearing fault diagnosis, and providing a more effective scheme for fault diagnosis. © 2024 by the Authors. Licensee Polish Society of Technical Diagnostics (Warsaw. Poland).

关键词： bearings fault grey wolf optimization algorithm permutation entropy support vector machine

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Access Strategy and Path Planning based on Planar Mobile Stereo Garage

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International Journal of Vehicle Structures and Systems 2024年第6期16卷 823-830页

作者： Yuan, Yuan Cheng, Qingli Zhou, Lidong Li, Yongchao Zhao, Pengju Wang, Hang School of Vehicle and Transportation Engineering Taiyuan University of Sci. & Tech Taiyuan China Taiyuan Heavy Industry Co. Ltd Taiyuan China

Taking the planar mobile stereo garage as the research object, in order to improve its overall access efficiency, combined with the operation characteristics of the garage, this paper studies the access strategy and Automated Guided Vehicle (AGV) path planning. Firstly, considering the actual access process of the garage and the different access conditions in the flat peak, storage peak and pickup peak periods, the mathematical model of access strategy is established, and the access strategy suitable for the garage is selected through calculation and analysis. Secondly, based on the traditional grey wolf optimization (GWO) algorithm, an improved GWO algorithm based on initial population opposition-based learning and two-stage nonlinear convergence factor is proposed and applied to the static obstacle avoidance path planning problem of AGV in the stereo garage. Compared with the genetic algorithm and the traditional GWO algorithm, the results show that the improved GWO algorithm has outstanding performance in terms of convergence speed, path length. © 2024. Carbon Magics Ltd.

关键词： Access strategy grey wolf optimization algorithm Path planning Planar mobile stereo garage

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