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Water Transfer Triggering Mechanism for Multi-Reservoir Operation in Inter-Basin Water Transfer-Supply Project

WATER RESOURCES MANAGEMENT 2014年第5期28卷 1293-1308页

作者： Zeng, Xiang Hu, Tiesong Guo, Xuning Li, Xinjie Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China China Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China Yellow River Inst Hydraul Res Res Ctr Xiaolangdi Zhengzhou 450003 Peoples R China

This paper proposes a new water transfer triggering mechanism for multi-reservoir system to divert water from abundant to scarce regions with a constant diversion flow in an inter-basin water transfer-supply project. Taking into account of the uncertain nature of inflow, the storage of reservoir is taken as a signal for decision-making to indicate water abundance or water scarcity. In this study, a set of rule curves based on storage of donor reservoir and storage of recipient reservoir are used together to determine when to start water transfer. To initiate water diversion to each recipient reservoir effectively, several water transfer rule curves of the donor reservoir are set for each recipient reservoir respectively in the multi-reservoir system with one donor reservoir and several recipient reservoirs, which is the main difference in comparison with other water transfer triggering mechanisms. In addition, a systematic framework is developed to integrate the water transfer rule curves with hedging rule curves to simultaneously solve the water transfer and water supply problems, since they interact with each other during the operation process. In order to verify the utility of the new water transfer triggering mechanism, an inter-basin water transfer-supply project in China is used as a case study and an improved particle swarm optimization algorithm (IPSO) with a simulation model is adopted for optimizing the decision variables. The results show that the proposed water transfer triggering mechanism can improve the operation performances of the inter-basin system.

关键词： Inter-basin water transfer Multi-reservoir operation rule Water transfer rule Water transfer triggering mechanism Hedging rule curves improved particle swarm optimization algorithm

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Thyristor State Evaluation Method Based on Kernel Principal Component Analysis

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IEEE ACCESS 2022年 10卷 29992-30004页

作者： Lei, Zhaoyu Guo, Jianyi Zheng, Feng Li, Jiayang Wang, Lei Hao, Liangshou Fan, Youping CSG EHV Power Transmiss Co Tianshengqiao Bur Xingyi 210094 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the thyristor test data, operation records, maintenance history, appearance inspection information, states of other components and operating environment. A basic index system for evaluating the aging state of thyristor with 42 parameters is established. Next, a mathematical model was developed by Fisher Discriminant Analysis (FDA). The kernel function of the kernel principal components is then optimized by an improved particle swarm optimization (IPSO) algorithm. The improved KPCA is applied to extract key parameters from the base index system to obtain the reduced dimensional evaluation indicators. The obtained principal component factors are used to determine the weights of the fuzzy composite factors, which are applied for fuzzy evaluation of the thyristor. Finally, 20 thyristors are selected for experimental and theoretical calculations. The results show that the cumulative contribution of the first three principal component variables after dimensionality reduction reaches 93.76%, which is consistent with the state of the thyristor. Compared to the four existing evaluation methods, the results of the method proposed in this paper are more reasonable, which removes the influence of redundant indicators, reduces the amount of data, and provides a reference for the related research on thyristor state evaluation.

关键词： Thyristors Kernel Principal component analysis Feature extraction Indexes particle swarm optimization Mathematical models DC transmission system fuzzy comprehensive evaluation improved particle swarm optimization algorithm kernel principal component analysis

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Online measurement method for roundness of large aluminum alloy ring based on laser ranging principle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第14期237卷 2275-2285页

作者： Lu, Zheng Zhang, Kaiyue Zhang, Xinyu Wu, Xijun Zhang, Yungang Yanshan Univ Sch Elect Engn 438 Hebei Ave Qinhuangdao 066004 Hebei Peoples R China

Aluminum alloy rings are widely used in aerospace, energy, petrochemical, and equipment applications. The loss of roundness often occurs because of the weak rigidity and unstable plasticity of aluminum alloy rings. Therefore, an online measurement technique for the roundness of aluminum alloy rings is critical for ensuring the processing quality of aluminum alloy rings. In this paper, an online roundness measurement method based on the laser ranging principle and improved particle swarm optimization (IPSO) is proposed with the aid of the rotation of the ring during the rolling process. First, the distance from the outer surface of the ring to the laser rangefinder was measured using a triangular laser rangefinder with the help of the rotation of the ring in the rolling process. Second, the distance data were denoized using an improved Kalman filter algorithm to reduce the influence of random noise on the measurement results. Finally, the roundness and geometric center were evaluated using the IPSO algorithm combined with the minimum zone circle (MZC) method and compared with the results obtained using the standard particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and least square (LS) algorithm. The experimental results show that the roundness measured by the IPSO algorithm is less than 0.06 mm, demonstrating better performance in the roundness evaluation. The measurement method for roundness proposed in this paper is suitable for the online evaluation of large aluminum alloy rings, avoiding the influence of ring size on roundness measurement.

关键词： Ring rolling laser ranging improved particle swarm optimization algorithm improved Kalman filter roundness evaluation

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A voyage with minimal fuel consumption for cruise ships

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JOURNAL OF CLEANER PRODUCTION 2019年 215卷 144-153页

作者： Zheng, Jianqin Zhang, Haoran Yin, Long Liang, Yongtu Wang, Bohong Li, Zhengbing Song, Xuan Zhang, Yu China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China Univ Tokyo Ctr Spatial Informat Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778568 Japan Univ Tokyo Dept Environm Syst Grad Sch Frontier Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778563 Japan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Koto Ku 2-3-26 Aomi Tokyo 1350064 Japan Hong Kong Univ Sci & Technol Big Data Inst Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China

With the rise of cruise services, energy consumption and emission of the maritime area are increasing. Due to the negative effect of greenhouse gases, many policies have been issued in the world to save energy and reduce emission. Adhering to the principle of energy conservation and emission reduction, an artificial neural network model with strong nonlinear fitting ability is introduced to explore the dynamic sailing data, and predict the fuel consumption for cruise ships based on automatic identification system data. Considering the constraints of station arrival time and the uncertainty of sailing speed and load during sailing, which can obtain the change rule from the historical voyage data, the objective function is to minimize the fuel consumption of a voyage. The established artificial neural network model is embedded into these four improved particle swarm optimization algorithms (GPSO, LPSO, MCPSO and SIPSO) with global search capability to optimize the sailing speed between stations, achieving the economic and environmental protection of a voyage. This method is applied to a real case study of Norwegian waters. By comparing the optimization results of these four algorithms, the total fuel consumption is potential to reduce from 97.4 t to 86.6 t of a voyage with the help of multi-swarm cooperative particle swarm optimization algorithm when its inertia weight is 0.7. It demonstrates that the method can be used as a tool to plan the sailing speed of cruise ships in advance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Fuel consumption Artificial neural network Uncertainty improved particle swarm optimization algorithm

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Energy Efficiency optimization of NOMA IoT Communication for 5G

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MOBILE NETWORKS & APPLICATIONS 2022年第3期27卷 862-873页

作者： Xu, Jijin Liang, Zhen Fujian Agr & Forestry Univ Coll Mech & Elect Engn Fuzhou 350002 Fujian Peoples R China

With the rapid increase in the number of mobile users and the continuous emergence of new wireless multimedia services, 5G mobile communication technology that can achieve spectral efficiency and data rate improvements has become a current research hotspot. This paper analyzes and summarizes the existing Internet of Things user grouping and power allocation algorithms under multi-carrier, and introduces the possible CSI (Channel State Information) error classification and modeling in the channel estimation process. For the dual-user cooperative 5G SWIPT-NOMA (Simultaneous Wireless Information and Power Transfer-Non-Orthogonal Multiple Access) transmission system with FD (Full-Duplex) antennas that exist in the presence of eavesdroppers, the physical layer security energy efficiency is used as the system performance measurement index. In view of the non-convex optimization problem and the high degree of coupling between variables, the joint solution of beamforming and power split ratio usually adopts the concave-convex procedure algorithm. This study proposes an improved particle swarm optimization algorithm, which is simpler than traditional solution methods. Aiming at the shortcomings of higher complexity of optimization algorithm, an improved particle swarm optimization algorithm with higher algorithm efficiency is proposed. The simulation results show that the optimal adaptive time slot optimization algorithm can obtain the optimal solution of the optimization problem, but the algorithm complexity is high. The improved particle swarm optimization algorithm is close to the optimal solution, the error is within the acceptable range, and the algorithm time is complicated.

关键词： 5G NOMA Energy efficiency improved particle swarm optimization algorithm Internet of things

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Wind power prediction method based on hybrid kernel function support vector machine

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WIND ENGINEERING 2018年第3期42卷 252-264页

作者： Tian, Zhongda Li, Shujiang Wang, Yanhong Wang, Xiangdong Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

关键词： Wind power support vector machine hybrid kernel function improved particle swarm optimization algorithm prediction

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optimization of Distributed Generation Integrated into Micro Grids Considering the Correlation of DGs

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第6期8卷 105-116页

作者： Zeng Pin-zhuo Wang Ke-you Li Guo-jie Jiang Xiu-chen Shanghai Jiao Tong Univ Dept Elect Engn Minist Educ Key Lab Control Power Transmiss & Transformat Shanghai 200240 Peoples R China

Optimal allocation of distributed generations (DGs) integrated into micro grids can significantly improve the stability and benefit the economy of micro grid operation. However, optimal micro grid planning is a kind of multi-dimensional and non-linear optimization problem. In this study, a multi-objective model is established by adopting the objective function which minimizes network loss, electricity price and operation cost;an improved particle swarm optimization (IPSO) algorithm with better optimizing performance is proposed by improving the initializing method and parameter control as well as average minimum and mutation factor are introduced. The proposed IPSO algorithm is then applied to a 29-node micro grid network structure. The comparison between different optimization schemes demonstrates the significance of optimal placement of DGs in micro grids. And it is also clear that the IPSO algorithm proposed in this study can effectively solve such problems.

关键词： Micro grid distributed generation multi-objective optimization improved particle swarm optimization algorithm mutation operator

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Optimal Dispatching of Microgrid for Island Operation Based on improved particle swarm algorithm

World Scientific Research Journal

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World Scientific Research Journal 2020年第6期6卷 260-269页

作者： Caitang Sun Chunxiu Zhang Sirui Zhou

With the development of the national economy, people's demand for electricity continues to increase, but in some remote areas such as islands and mountain areas, it is usually far away from the conventional large power grid, and it is difficult to get a good power supply. These areas generally contain abundant renewable energy, such as wind and solar energy. Therefore, in these areas, priority can be given to the development of micro-grids for clean energy power generation, making full use of renewable energy sources such as wind energy and solar energy to achieve self-sufficiency. However, wind energy and solar energy depend on natural conditions and have obvious randomness and volatility. Therefore, it is necessary to determine the control strategy of this island-operated microgrid and ensure its economic efficiency. This paper takes wind / solar /diesel engine/ storage island operation microgrid as an example to study the optimal control strategy of this microgrid in island mode, and uses an improved particle swarm optimization algorithm to optimize. The results show that the operation results can meet the needs of users. Both economical.

关键词： Manuscripts Microgrid improved particle swarm optimization algorithm Optimal dispatch

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P2P Traffic Detection Based on particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第12期9卷 155-164页

作者： Song Rong Li Xiating Jiangxi Vocat & Tech Coll Commun Nanchang 330013 Jiangxi Peoples R China

Considering the shortcomings of the conventional BP neural network, such as slow learning speed, weak anti-interference ability and easy to fall into local minimum, the detection accuracy of P2P traffic detection model is low and the speed is slow, the particle swarm optimization algorithm is used to optimize it here. As the conventional algorithm's optimization ability is the initial parameters, the algorithm is easy to be early, and the convergence speed is slow. Therefore, grouping, organizing, fission and mutation operation on the conventional algorithm have been carried on in order to improve the defect of conventional algorithm. Finally, the P2P traffic detection model is built by using MATLAB software, and traffic detection experiments are carried out on Bittorrent, EMule, PPlive and PPStream 4 P2P network applications. The test data show that the average recognition rate of the recognition model is 96.14%, which is 13.3% higher than that of the conventional PSO-BP model, and9.4% higher than that of the QPSO-BP recognition model for the four P2P network applications.

关键词： P2P technique flow detection artificial neural network improved particle swarm optimization algorithm

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Fuzzy Power System Reliability Model Based on Value-at-Risk

Fuzzy Power System Reliability Model Based on Value-at-Risk

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14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

作者： Wang, Bo Li, You Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9783642153891

Conventional power system optimization problems deal with the power demand and spinning reserve through real values. In this research, we employ fuzzy variables to better characterize these values in uncertain environment. In building the fuzzy power system reliable model, fuzzy Value-at-Risk (VaR) can evaluate the greatest value under given confidence level and is a new technique to measure the constraints and system reliability. The proposed model is a complex nonlinear optimization problem which cannot be solved by simplex algorithm. In this paper, particle swarm optimization (PSO) is used to find optimal solution. The original PSO algorithm is improved to straighten out local convergence problem. Finally, the proposed model and algorithm are exemplified by one numerical example.

关键词： Fuzzy variable Value-at-Risk System reliability improved particle swarm optimization algorithm

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