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multi-parameter optimization and design of self-triggered low voltage hybrid DC circuit breaker based on machine learning

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Zhang, Zhaozi Gao, Caizhi Cao, Weidong Wang, Qian Chen, Silei Li, Xingwen Huang, Yinfang Gong, Zuoyong Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xian Univ Technol Sch Sci Xian 710048 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Shanghai Liangxin Elect Co Ltd Nader Shanghai 201206 Peoples R China

The low-voltage direct current (LVDC) hybrid circuit breaker (HCB), with advantages of low conduction loss and high breaking performance is better to meet the application needs of photovoltaic system. Among these, selftriggered hybrid circuit breakers (STHCBs) based on natural commutation have become an important development direction for LVDC HCBs due to their simple structure and selective protection. However, existing design methods do not adequately consider the influence of multiple parameters, resulting in unreliable current commutation during interruption. To address these issues, this paper proposes a machine learning-based multiparameter optimization method. The method employed Long Short-Term Memory (LSTM) algorithm to predict the interruption waveforms of mechanical switches under different short-circuit fault conditions as an input to the optimization. In addition, an interruption model of HCBs was developed, which comprehensively considered power electronic device characteristics, arc behavior and drive circuit. Based on the theoretical analysis and predicted waveforms, we used Genetic Algorithm (GA) to determine the optimal design parameters for the corresponding interruption waveforms. Experiment results confirm that this method can effectively enhance the interruption reliability of STHCBs while also demonstrates adaptability. The proposed method provides technical reference for the design of LVDC HCBs.

关键词： LVDC Hybrid circuit breaker Arc prediction multi-parameter optimization Short-circuit interruption Machine learning algorithms

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multi-parameter optimization design of parabolic trough solar receiver

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APPLIED THERMAL ENGINEERING 2016年 98卷 73-79页

作者： Guo, Jiangfeng Huai, Xiulan Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China

The design parameters of parabolic trough solar receiver are interrelated and interact with one another, so the optimal performance of solar receiver cannot be obtained by the convectional single-parameter optimization. To overcome the shortcoming of single-parameter optimization, a multi-parameter optimization of parabolic trough solar receiver is employed based on genetic algorithm in the present work. When the thermal efficiency is taken as the objective function, the heat obtained by working fluid increases while the average temperature of working fluid and wall temperatures of solar receiver decrease. The average temperature of working fluid and the wall temperatures of solar receiver increase while the heat obtained by working fluid decreases generally by taking the exergy efficiency as an objective function. Assuming that the solar radiation intensity remains constant, the exergy obtained by working fluid increases by taking exergy efficiency as the objective function, which comes at the expense of heat losses of solar receiver. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Parabolic trough solar receiver multi-parameter optimization Heat transfer Exergy efficiency Thermal efficiency Genetic algorithm

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multi-parameter optimization and control of the cylindrical grinding process

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2002年第1-3期129卷 232-236页

作者： Li, GF Wang, LS Yang, LB Jilin Univ Coll Mech Sci & Engn Changchun 130025 Peoples R China

This paper proceeds beyond the limits of conventional no-burn thought, presenting an optimum strategy permitting burn to appear in the rough grinding stage, but where the burning layer can be accumulated in the following finishing stage. On the basis of the basic grinding models, the objective function and constraint functions for the multi-parameter optimum grinding process have been built in this paper. The non-linear optimum grinding control parameters have been obtained through computer simulation, and the actual grinding process had been controlled by these parameters. The results of the experiments confirm the exactitude of the optimum models and the feasibility of the optimum strategy. This paper had created the pre-condition for grinding automation, virtual grinding and intelligent grinding systems. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： grinding process multi-parameter optimization computer simulation depth of burning layer optimum control

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multi-parameter optimization Algorithm of Frequency-Dependent model for Viscoelastic Damping Material

Multi-Parameter Optimization Algorithm of Frequency-Dependen...

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International Conference on Material and Manufacturing Technology

作者： Cao, Y. Q. Deng, Z. X. Li, J. Liu, H. J. Chongqing Univ Inst Engn Mech Chongqing Peoples R China

ISBN: (纸本)9780878492435

In order to obtain the parameters of viscoelastic damping material model, namely GHM, which account for frequency dependence and allow time-domain analysis of viscoelastic composite structure, a new optimization algorithm which is composed of particle swarm optimization(PSO) and sequential quadratic programming (SQP) algorithm is presented. The results are compared with those of standard rheological model and fractional derivative model as well as the experiment results. The results show that the hybrid algorithm can obtain the better optimum solution for the model and have a fast convergence, it will correctly describe the constitutive characteristics of viscoelastic damping material.

关键词： viscoelastic damping material GHM model PSO-SQP hybrid algorithm multi-parameter optimization

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multi-parameter optimization and control of the cylindrical grinding process

Multi-parameter optimization and control of the cylindrical ...

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10th International Manufacturing Conference in China (IMCC 2002)

作者： Li, GF Wang, LS Yang, LB Jilin Univ Coll Mech Sci & Engn Changchun 130025 Peoples R China

关键词： grinding process multi-parameter optimization computer simulation depth of burning layer optimum control

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multi-parameter optimization of Vented CMUTs for Airborne Applications

Multi-Parameter Optimization of Vented CMUTs for Airborne Ap...

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IEEE International Ultrasonics Symposium (IUS)

作者： Ma, Bo Chang, Chienliu Oguz, Huseyin kagan Firouzi, Kamyar Khuri-Yakub, Butrus T. Stanford Univ Dept Elect Engn Edward L Ginzton Lab Stanford CA 94305 USA

ISBN: (纸本)9781538633830

This paper reports vented CMUTs with wide bandwidth and high sensitivity, optimized by multi-parameter optimization method. The dominated parameters, including the radius and thickness of the flexible plate, gap height, and the size and distribution of vented holes have been individually optimized. The impedance measurements well-match the simulation results. Then the multi-parameter optimization method in place of the individual parameter optimization method is used to optimize the design parameters all together. Finally, keeping the approximate bandwidth, the multi-parameter optimization makes the sensitivity improved by 11% at the same percentage of pull-in voltage, and driving voltage is lowered by 49% compared with the individual parameter optimization.

关键词： CMUTs individual parameter optimization multi-parameter optimization wide bandwidth high sensitivity

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Application of dimension reduction based multi-parameter optimization for the design of blast-resistant vehicle

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STRUCTURAL AND multiDISCIPLINARY optimization 2017年第4期56卷 903-917页

作者： Wei, Ran Wang, Xianhui Zhang, Ming Zhou, Yunbo Wang, Liangmo Nanjing Univ Sci & Technol Nanjing Dept Mech Engn Nanjing 210094 Jiangsu Peoples R China

The design of blast-resistant vehicle provides an appropriate level of protection for the vehicle and occupants against the serious threat from landmine and improvised explosive devices (IED). Mathematically, the objective of this research is to optimize the configuration of light armored vehicle installed multilayer honeycomb sandwich structures (MHSS), shock-mitigating seat and seat belt, and cope with the challenge of highly computational cost on dealing with the large scale, multi-parameter, nonlinear and fluid-structure interaction simulation models. multi material Arbitrary Lagrangian-Eulerian (MM-ALE) method is used to obtain the high-precision vehicle responses and occupant injuries under blast wave. The baseline model validated by the blast test is built, and the optimization model for blast-resistant vehicle is defined. Then, identifying important design parameters accurately is so difficultly when sufficient samples are not provided due to the expensive computational cost, and it's inappropriate to screen parameters with inconsistent sequence of variable sensitivities for occupant injuries. Factor analysis based multi-parameter optimization (FAMO) is proposed to reduce the computational cost on improving the blast resistant performance of vehicle. The normal-boundary intersection (NBI) and the R (2) metric are used to obtain optimal compromise solution which noticeable reduced the peak value of occupant injuries, and the physical insights which drive the optimal solution to reduce the occupant injuries is analyzed. Additional studies are conducted on comparing between proposed algorithm and the conventional algorithm, including shape of the Pareto front, optimal compromise solution, design variables and responses.

关键词： Light armored vehicle Blast resistance multi-parameter optimization Factor analysis Occupant injuries

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Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第16期29卷 24269-24285页

作者： Cheng, Tiantian Zhang, Jiahua Zhang, Sha Bai, Yun Wang, Jingwen Li, Shuaishuai Javid, Tehseen Meng, Xianglei Sharma, Til Prasad Pangali Qingdao Univ Coll Comp Sci & Technol Remote Sensing Informat & Digital Earth Ctr Qingdao 266071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing Peoples R China

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R-2 of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R-2 of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher;in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.

关键词： Soil salinization Remote sensing multi-parameter optimization Yellow River Delta Spatiotemporal dynamic

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multi-parameter optimization Analysis of Hydrodynamic Performance for Rim-Driven Thruster

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ENERGIES 2023年第2期16卷 891-891页

作者： Nie, Yuanzhe Ouyang, Wu Zhang, Zhuo Li, Gaoqiang Zheng, Ruicong Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China Reliabil Engn Inst Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China East Lake Lab Wuhan 420202 Peoples R China Guangzhou Haigong Marine Equipment Co Ltd Guangzhou 510000 Peoples R China

The efficiency of rim-driven thrusters (RDT) has always been the focus of attention in the context of energy conservation and environmental protection. A multi-parameter collaborative optimization framework is proposed to improve the efficiency of RDT based on the response surface method (RSM). The common structural parameters of RDT, including pitch ratio, disk ratio and rake angle, are selected as design variables to carry out the Box-Behnken experimental design combined with the simulation data obtained through CFD calculations. The response surface second-order model is employed to evaluate the extent to which different parameters can affect the target variable and obtain the optimal hydraulic efficiency. The results show that the established model has high precision, good reproducibility and strong anti-interference ability. The influence of the pitch ratio, rake angle and disk ratio on open water efficiency decreases in sequence. Compared with the prototype RDT, the maximum efficiency of the optimized RDT is increased by 13.8%, and the surface pressure distribution and flow field characteristics are also significantly modified.

关键词： rim-driven thrusters (RDT) response surface method (RSM) hydrodynamic performance computational fluid dynamics (CFD) multi-parameter optimization

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multi-parameter optimization maximizes the performance of genetically engineered Geobacillus for degradation of high-concentration nitroalkanes in wastewater

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BIORESOURCE TECHNOLOGY 2022年第0期347卷 126690-126690页

作者： Sun, Shenmei Song, Xiaoru Bian, Ya Wan, Xuehua Zhang, Jingjing Wang, Wei Nankai Univ TEDA Inst Biol Sci & Biotechnol Key Lab Mol Microbiol & Technol Minist EducTEDA Tianjin 300457 Peoples R China Tianjin Key Lab Microbial Funct Genom Tianjin 300457 Peoples R China

Nitroalkanes are important toxic pollutants for which there is no effective removal method at present. Although genetic engineering bacteria have been developed as a promising bioremediation strategy for years, their actual performance is far lower than expected. In this study, important factors affecting the application of engineered Geobacillus for nitroalkanes degradation were comprehensively optimized. The deep-reconstructed engineered strains significantly raised the expression and activity level of catalytic enzymes, but failed to fully enhance the degradation efficiency. However, further debugging of a variety of key parameters effectively improved the performance of the engineering strains. The increased cell membrane permeability, trace supplementation of vital nutritional factors, synergy of multifunctional enzyme engineered bacteria, switch of oxygen-supply mode, and moderate initial biomass all effectively boosted the degradation efficiency. Finally, a low-cost and highly effective bioreactor test for high-concentration nitroalkanes degradation proved the multi-parameter optimization mode helps to maximize the performance of genetically engineered bacteria.

关键词： Geobacillus Genetic engineering Nitroalkanes multi-parameter optimization Bioreactor

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