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6th International Conference on Renewable Energy and Power Engineering (REPE)

作者： Zhu, Liang Zhang, Peizheng Deng, Yue Yang, Na Hou, Tong Zhang, Youcai Li, Xiaoyu Yu, Qingguang State Grid Beijing Chaoyang Elect Power Supply Co Beijing Peoples R China Tsinghua Univ Dept EE Beijing Peoples R China

ISBN: (纸本)9798350306194;9798350306187

In this paper, the basic structure of the optical storage and charging integrated charging station and the distribution control of energy in the system are discussed, and the capacity allocation model of the optical storage and charging system is established by considering the economic return of the charging station and the impact on the grid as the optimization objective, and the optimization solution is combined with the particle swarm optimization algorithm, which can support the planning and construction of the optical storage and charging integrated charging station.

关键词： Integrated Optical Method charging station capacity allocation particle swarm optimization algorithm

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Power optimization of gas pipelines via an improved particle swarm optimization algorithm

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Petroleum Science 2012年第1期9卷 89-92页

作者： Zheng Zhiwei Wu Changchun Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of PetroleumBeijing 102249China

In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming.

关键词： Gas pipeline operation optimization particle swarm optimization algorithm

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How will tradable green certificates affect electricity trading markets under renewable portfolio standards?A China perspective

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Clean Energy 2022年第4期6卷 585-598页

作者： Kaiqiang Leng Zhongzhong Li Zihao Tong School of Economics and Management North China Electric Power UniversityBeijing102206China

Renewable portfolio standards(RPS)are important guarantees to promote renewable energy(RE)*** tradable green certificate(TGC)trading mechanism is a supporting mechanism of RPS,but the rate of TGC trading is low and there is a double-metering problem of RE *** the introduction of new policies in China,we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation(BEG)projects are considered to participate in TGC *** explore the interaction between the TGC market and the electricity market,this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium *** established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are *** main conclusions are as follows.(i)The established market structure and model effectively solve the double-metering problem of RE consumption,making the TGC turnover rate reach 82.97%,greatly improving the market efficiency.(ii)Increased demand for TGC will increase demand for RE *** participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers(BEPs),reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based *** will help promote the rapid development of China’s RE,especially the BEG industry.(iii)Among the influencing factors,the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of *** decline in TGC price and subsidy price will reduce the profit of ***,we put forward policy recommendations for China’s RPS and TGC trading *** study can provide a reference for the construction of China’s

关键词： tradable green certificate market electricity market renewable portfolio standards biomass-based electricity generation particle swarm optimization algorithm

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The impact of landscape heterogeneity on the dynamic urban growth simulation based on cellular automata model: the case of Wuhan metropolitan area, Central China

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GEOCARTO INTERNATIONAL 2022年第27期37卷 15847-15876页

作者： Wang, Quan Wang, Haijun Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China Wuhan Univ Key Lab Geog Informat Syst MOE Wuhan Peoples R China

As the main urban system of human life, metropolitan area has complicated heterogeneity. However, the impact of landscape heterogeneity (LH) on urban growth needs to be further analyzed. This study proposed a LH approach and constructed a cellular automata (CA) simulation model of urban growth considering the LH. Taking Wuhan metropolitan area as study area, the impact of LH on urban growth was explored. The results showed that the simulation accuracy indices considering LH showed marked improvement using CA models, indicating that historical LH has a significant impact on the possibility of urban growth. Furthermore, the LH not only affects the urban growth, but also causes the urban growth to produce the redistribution of LH. Overall, the findings show that the transition rules of CA model integrating LH can better grasp the dynamic process on urban growth, and the results have important practical significance for urban growth planning and management.

关键词： Landscape heterogeneity urban growth particle swarm optimization algorithm simulation cellular automata

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Dynamic clustering and routing using multi-objective particle swarm optimization with Levy distribution for wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第13期34卷 e4902-e4902页

作者： Jagadeesh, S. Muthulakshmi, I VV Coll Engn Dept Comp Sci & Engn Tuticorin Tamil Nadu India

Energy-efficient clustering and routing are two well-known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP-hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi-objective particle swarm optimization with Levy distribution (MOPSO-L) algorithm. Since the parameters in WSN are related to one another, multi-objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO-L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO-L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives.

关键词： clustering Levy distribution particle swarm optimization algorithm routing

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Infrared small target detection based on the dynamic particle swarm optimization

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INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103837-103837页

作者： Shahraki, Hadi Aalaei, Shokoufeh Moradi, Saed Univ Sistan & Baluchestan Fac Ind & Min Dept Comp Engn Khash Iran Mashhad Univ Med Sci Sch Med Dept Med Informat Mashhad Razavi Khorasan Iran Univ Isfahan Fac Engn Dept Elect Engn Esfahan Iran

In this paper, a new method is introduced for detecting small targets in infrared images. The proposed method is based on the particle swarm optimization algorithm (PSO). Considering the nature of the small target detection problem, a dynamic optimization algorithm is developed to detect targets. The proposed algorithm is called the dynamic particle swarm detector (DPSD). Unlike common small target detection algorithms, the computational complexity of the proposed algorithm is not linear to number of pixels in the input image. Therefore, it is capable of operating on a large-scale image in a relatively small timeslot. Many experiments are carried out to evaluate the effectiveness of the proposed method, where the DPSD is compared to seven well-known methods in terms of quantitative detection metrics. The results show that the proposed detector outperforms the baseline algorithms.

关键词： Artificial intelligence Infrared search and track Dynamic optimization algorithm particle swarm optimization algorithm Real-time small target detection

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A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

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ENERGY 2012年第1期38卷 406-413页

作者： Siddhartha Sharma, Naveen Varun Natl Inst Technol Dept Mech Engn Hamirpur 177005 India

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31 degrees 25'-31 degrees 52'N and (longitude) 76 degrees 18' to 76 degrees 44' E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Thermal performance Solar air heater particle swarm optimization algorithm

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Study on the optimization of Support Parameters of Metro Station Constructed by Arch Cover Method

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2022年第8期40卷 4147-4157页

作者： Guo, Xinping Jiang, Hongren Jiang, Annan Dalian Maritime Univ Sch Transportat Engn Dalian 116026 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

The arch is the main stress structure of metro station in the construction of arch cover method. The preliminary geological survey has some limitations, and the arch structure design based on the survey results is usually too conservative, which increases the investment cost. Therefore, it is necessary to optimize the design parameters of arch structure. In this paper, based on particle swarm optimization (PSO) algorithm, the engineering cost is taken as the optimization objective, and the monitoring control values of displacement are taken as the constraint condition. The scheme optimization is carried out for the thickness of outer primary lining and inner primary lining and removal length of temporary support. The final optimization values of parameters obtained by PSO algorithm are that the removal length of temporary support is 18 m, the thickness of the outer primary lining is 22 cm, and the thickness of the inner primary lining is 26 cm. Compared with the original design scheme, the engineering cost of the optimized scheme is reduced by 8.79%. The optimized parameters can not only meet the safety requirements of the project, but also effectively reduce the project cost, which has guiding significance to the actual project construction.

关键词： Arch cover method Temporary support Primary lining particle swarm optimization algorithm

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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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Two-dimensional variational mode decomposition for seismic record denoising

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2022年第3期19卷 433-444页

作者： Zhang, Xingli Chen, Yan Jia, Ruisheng Lu, Xinming Shandong Univ Sci & Technol Coll Comp Sci & Engn Qianwan Port Rd Qingdao 266590 Peoples R China

Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which K and a are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate K and a values and apply these values to the proposed method. First, for a 2D seismic signal, the 2D-VMD method can decompose it into K modes with specific direction and vibration characteristics. Next, the PE value of each mode is calculated. Random noise components are eliminated according to the PE value. Finally, the signal components are reconstructed to acquire the denoised seismic signal. Experimental and simulation results indicate that the proposed method has remarkable denoising effect on synthetic and real seismic signals. We hope that this new method can inspire and help evaluate new ideas in this field.

关键词： seismic signal noise reduction two-dimensional variational mode decomposition particle swarm optimization algorithm permutation entropy

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