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Dielectric Feature Parameters Database Using for Aging State Evaluation of Traction Transformer Insulation at Hotspot Region

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 2619-2628页

作者： Jiang, Zaijun Liu, Jiefeng Fan, Xianhao Zhang, Heng Gong, Heyan Zhou, Jiacheng Zhang, Yiyi Wu, Thomas Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Accurate aging state evaluation of traction transformer insulation at the hotspot region is crucial for ensuring the stable operation of the railway traffic system. Given this issue, this research begins by collecting the polarization currents of oil-paper insulation. Then, the modified dielectric response model is proposed to simulate the collected polarization currents. To extract the dielectric feature parameters related to the insulating paper's aging state, the cooperative co-evolutionary algorithm (CCEA) modified by niche mechanisms (NMs) is proposed. During the iteration process, the inversion computation technique is devised to reduce the number of feature parameters to be optimized. Futhermore, the dielectric feature parameters database is established by the surface fitting technique, Finally, the database is employed to evaluate the aging state of hotspot insulating paper under both laboratory conditions and field transformers and correct the influence of testing temperature. The relative errors between the calculated and measured DP in verification experiments are under 4%, and the corresponding aging state labels are exactly the same. In this context, the study is anticipated to provide a precise assessment of the aging degree of traction transformer insulation at the hotspot region.

关键词： Aging Dielectrics Temperature measurement Testing Power transformer insulation Oil insulation Dielectric measurement Dielectric response theory insulation evaluation intelligence optimization algorithm inversion computation technique traction transformer

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The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年第14期31卷 21986-22011页

作者： Xu, Yan Lin, Tong Du, Pei Wang, Jianzhou Ocean Univ China Qingdao 266100 Peoples R China Qingdao Financial Res Inst Qingdao 266100 Peoples R China Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Jiangnan Univ Food Safety Res Base Jiangsu Prov Wuxi 214122 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China

Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with a fractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.

关键词： Carbon emission forecasting Multivariate grey models Fractional order operator intelligence optimization algorithm

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Differential mutation and novel social learning particle swarm optimization algorithm

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INFORMATION SCIENCES 2019年 480卷 109-129页

作者： Zhang, Xinming Wang, Xia Kang, Qiang Cheng, Jinfeng Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Henan Peoples R China

Social Learning Particle Swarm optimization (SLPSO) is an improved Particle Swarm optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, such as poor balance between exploration and exploitation and low search efficiency, so that it cannot yet do well in solving many complex optimization problems. Thus, this paper proposes an improved SLPSO algorithm, that is, Differential mutation and novel Social learning PSO (DSPSO). Firstly, in order to balance exploration and exploitation better, a dynamic inertia weight is introduced to replace the random inertia weight of SLPSO, and a single-example learning approach and an example-mean learning one are proposed to replace the imitation component and the social influence component of SLPSO respectively. Secondly, the dimension-based velocity updating equation of SLPSO is divided into two particle-based updating equations with the two approaches, and the two are executed alternately to form a novel social learning PSO (NSLPSO), which enhance the exploitation of SLPSO. Finally, a dynamic differential mutation strategy is used in NSLPSO to update the three best particles to enhance the exploration to obtain DSPSO. Experimental results on the complex functions from CEC2013 reveal that DSPSO outperforms SLPSO and quite a few state-of-the-art and classic PSO variants. (C) 2018 Elsevier Inc. All rights reserved.

关键词： intelligence optimization algorithm Evolutionary algorithm Particle swarm optimization Differential mutation Social learning

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VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

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Computers, Materials & Continua 2023年第11期77卷 1617-1644页

作者： Junqiang Jiang Zhifang Sun Xiong Jiang Shengjie Jin Yinli Jiang Bo Fan School of Information Science and Engineering Hunan Institute of Science and TechnologyYueyang414006China Department of Computer and Information Science Linköping UniversityLinköping58183Sweden Department of Safety and Quality Hunan Construction Investment GroupChangsha410004China

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf *** with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these ***,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early *** solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this *** first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering *** 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.

关键词： intelligence optimization algorithm grey wolf optimizer(GWO) manhattan distance symmetric coordinates

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Numerical algorithm for optimal control of switched systems and its application in cancer chemotherapy

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APPLIED SOFT COMPUTING 2022年第0期115卷 108090-108090页

作者： Wu, Xiang Lin, Jinxing Zhang, Kanjian Cheng, Ming Guizhou Normal Univ Sch Math Sci Guiyang 550001 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Minist Educ CSE Key Lab Measurement & Control Nanjing 210096 Peoples R China

This paper considers an optimal control problem of switched dynamical systems with control input and system state constraints. Unlike in traditional switched dynamical systems, the switching times cannot be specified directly and they are governed by a state-dependent switching condition. Thus, the existing methods cannot be directly used to solve this problem. To overcome this difficulty, the switching conditions are transformed into a continuous-time inequality constraint by introducing an integer constraint. Further, the original optimal control problem is approximated by using a sequence of constrained non-convex nonlinear parameter optimization problems by using a relaxation method, a control vector parameterization technique, and a time-scaling transformation. Following that, a penalty function-based intelligent optimization algorithm is proposed for obtaining a global optimal solution based on a more effective penalty function method and a more effective intelligent optimization algorithm. The convergence results show that the proposed method is globally convergent. Numerical simulation results show that the proposed method is lower time-consuming, has faster convergence speed, can obtain a better objective function value than the existing typical algorithms, and can achieve a stable and robust performance when considering the small perturbations in constraint conditions or the small perturbations of the model parameters. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Switched dynamical system Optimal control Penalty function method intelligence optimization algorithm Cancer chemotherapy

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An Adaptive Grid Model Based on Mobility Constraints for UAV Path Planning 2

An Adaptive Grid Model Based on Mobility Constraints for UAV...

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2nd International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Yao, Kun Li, Jingwen Sun, Bing Zhang, Jieqiong Beihang Univ BUAA Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781467398725

The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpolation after path planning increases the amount and complexity of algorithm. Considering to solve UAV path planning smoothing problem in the process of scenario modelling, this paper proposed an adaptive grid model, which provide the sparsest grid division under the UAV mobility constraints. The simulation results implicate that the path using proposed model satisfies mobility constraints and decreases the computational complexity effectively in both 2-D and 3-D path planning.

关键词： UAV planning system adaptive grid mobility constraints intelligence optimization algorithm

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Integrated Energy System Modeling and Collaborative and Optimal Operation of Urban Multi-Energy Coupling 3

Integrated Energy System Modeling and Collaborative and Opti...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Wei, Dongni Jin, Panlong Zhang, Zelong Yang, Zhao Liu, Tong Economic and Technical Research Institute of State Grid Ningxia Electric Power Co. Ltd Ningxia China

ISBN: (纸本)9798350318180

The development of integrated energy systems (IESs) faces two key challenges: ensuring the availability of renewable energy and enhancing the economic benefits of renewable energy utilization. The integrated energy system will be able to overcome current limitations on energy distribution and address some issues related to urban power development. t is imperative to thoroughly analyze the interdependent coupling process of urban, comprehensive energy systems to achieve economic optimization of multi-energy utilization in urban areas and facilitate the adoption of renewable energy. In this study, the output side utilizes solar energy, wind energy, and grid integration methods. In contrast, the load side utilizes the coupling components of various energy flows such as heat, electricity, and cooling. IES's optimal operation dispatching model is established around the output side, load side, and optimal dispatching. The swarm intelligence optimization algorithm is used to evaluate and adjust the stability and economic benefits of the model. © 2023 IEEE.

关键词： integrated energy system intelligence optimization algorithm renewable energy

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Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models

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JOURNAL OF HYDROLOGY 2023年第PartA期620卷

作者： Xing, Liwen Feng, Yu Cui, Ningbo Guo, Li Du, Taisheng Wu, Zongjun Zhang, Yixuan Wen, Shenglin Gong, Daozhi Zhao, Lu Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Shenzhen 518000 Peoples R China China Agr Univ Ctr Agr Water Res China Beijing 100091 Peoples R China Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr State Engn Lab Efficient Water Use Crops & Disaste Beijing 100081 Peoples R China

Accurate estimates of reference evapotranspiration (ET0) are of great significance to water resources planning and management, but the actual solar radiation (Rs), as the primary parameter for ET0 estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (Rs) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligence algorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing Rs models to improve ET0 estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical Rs models, comprising three sunshine-based models (Angstrom, & BULL;Ogelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET0 estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate Rs estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimization algorithm, this study found that GSA performed better for the & BULL;Ogelman model, Fan model, and Chen model when integrating into the PM equation for ET0 estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PMGSA-Fan obtained the most accurate estimates of ET0 at four climatic zones, with a regional spatial gradient in estimates accuracy of Rs from north to south of China. In terms of sunshine-based models, the PMMEA-Angstrom performed better in TMZ, TCZ, and MPZ, whereas the PMGSA-O & BULL;gelman performed better in SMZ, respectively;in terms of temperature-based models, the PMMEA-Hunt performed

关键词： Associate Editor intelligence optimization algorithm Combined -based model Sunshine -based model Temperature -based model Climatic zones

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Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization

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APPLIED SOFT COMPUTING 2021年 101卷 107061-107061页

作者： Zhang, Xinming Lin, Qiuying Mao, Wentao Liu, Shangwang Dou, Zhi Liu, Guoqi Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

Grey Wolf Optimizer (GWO) and Particle Swarm optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique search mechanisms and their advantages after the improvements on them, this paper proposes a novel hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP). Firstly, GWO is simplified and a novel differential perturbation strategy is embedded in the search process of the simplified GWO to form a Simplified GWO with Differential Perturbation (SDPGWO) so that it can improve the global search ability while retaining the strong exploitation ability of GWO. Secondly, a stochastic mean example learning strategy is applied to PSO to create a Mean Example Learning PSO (MELPSO) to enhance the global search ability of PSO and prevent the algorithm from falling into local optima. Finally, a poor-for-change strategy is proposed to organically integrate SDPGWO and MELPSO to obtain an efficient hybrid algorithm of GWO and PSO. HGWOP can give full play to the advantages of these two improved algorithms, overcome the shortcomings of GWO and PSO and maximize the whole performance. A large number of experiments on the complex functions from CEC2013 and CEC2015 test sets reveal that HGWOP has better optimization performance and stronger universality compared with quite a few state-of-the-art algorithms. Experimental results on K-means clustering optimization show that HGWOP has obvious advantages over the comparison algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： intelligence optimization algorithm Grey Wolf Optimizer Particle Swarm optimization algorithm Hybrid algorithm K-means clustering

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Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf algorithm in four climatic regions of China

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AGRICULTURAL WATER MANAGEMENT 2024年 291卷

作者： Dong, Juan Xing, Liwen Cui, Ningbo Guo, Li Liang, Chuan Zhao, Lu Wang, Zhihui Gong, Daozhi Northwest A&F Univ Inst Soil & Water Conservat Xianyang 712100 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Chinese Acad Agr Sci Inst Agr Environm & Sustainable Dev Beijing 100081 Peoples R China

Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman-Monteith equation, empirical methods have been widely recommended worldwide. However, its application is still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimization algorithm for calibrating parameters is very necessary. Regarding the above requirement, the present study developed a novel improved Grey Wolf algorithm (MDSL-GWA) to optimize the most recommended ones among three types of ETo methods. After the optimization performance comparison among Least Square Method (LSM), Genetic algorithm (GA), Grey Wolf algorithm (GWA), and MDSL-GWA in four climatic regions of China, this study found that the Priestley-Taylor (PT) method was the best radiation-based (Rn-based) method and achieved better performance in temperate continental region (TCR), mountain plateau region (MPR), and temperate monsoon region (TMR) than other types. While the temperature-based (T-based) Hargreaves-Samani (HS) method performed best in subtropical monsoon region (SMR), further attaching better performance among the same type in TMR and TCR, while the Oudin method was the best T-based method in MPR. Moreover, the Romanenko method was better humidity-based (RH-based) in TCR and MPR, whereas the Brockamp-Wenner method exhibited higher in SMR and TMR. Furthermore, despite intelligence optimization algorithms significantly enhancing original ETo methods, the MDSL-GWA achieved best performance and outperformed other algorithms by 4.5-29.6% in determination coefficient (R2), 4.7-27.3% in nash-sutcliffe efficient (NSE), 3.7-44.4% in relative root mean square error (RRMSE), and 3.1-56.2% in mean absolute error (MAE), respectively. After optimization, the MDSL-GWA-PT was the most recommended ETo method in TMR, TCR, and MPR, and the median values of R2, NSE, RRMSE, and MAE ra

关键词： Empirical parameter intelligence optimization algorithm Radiation -based method Humidity -based method Temperature -based method

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